Recommendation Systems with CF and DL Techniques.ipynb

{
  "cells": [
    {
      "metadata": {
        "_uuid": "6c0f989904faf30bd6df4f6982a3fb3475278b77"
      },
      "cell_type": "markdown",
      "source": "# What are Recommendation Systems?"
    },
    {
      "metadata": {
        "_uuid": "ce89039a986c7744a4053976634d737eacc51d58"
      },
      "cell_type": "markdown",
      "source": "Every day we view and purchase products on Amazon, browse Netflix, stream music on Spotify. Have you ever wondered how accurately they know your tastes and preferences and make Recommendations based on your interests ?\n\nThe answer is they have personalized Recommendation Engines which uses advanced algorithms to recommend products to you... When you want to watch a new movie, you usually ask your friends for suggesting movies, they know your interests and accordingly they suggest you a good movie.\n\nSo, **Recommendation Systems work the same way, they are nothing but data filtering tools that uses algorithms to recommend most relevant items to a particular user.**\n\nNow, let us understand Why do we need Recommendation Systems and in the later part we will look at How Recommender Systems actually work?\n\n![](https://i.imgur.com/DlXYzzg.jpg)"
    },
    {
      "metadata": {
        "_uuid": "3bfb230af118afa9b264b5515e9e1ab31ce58f2e"
      },
      "cell_type": "markdown",
      "source": "# Why do we need Recommendation Systems?"
    },
    {
      "metadata": {
        "_uuid": "c4d599b1a68b9d974798d832f8e01b00e5bae20a"
      },
      "cell_type": "markdown",
      "source": "Internet is a vast ocean of Information, it consists of millions of items, a really large catalogue of products. There are some Users that know what they are looking for whereas others have no idea what to look for in such a large library of resources, and thus, Recommendation Systems play a vital role.\n\nThere are some products which may be really good but have not gained **Popularity** since they have not been advertised, so recommendation systems help such items gain popularity by bringing such items to one's notice.\n\nIt even helps in **Ad Targeting**- Say you're looking to buy a new laptop on Internet, your recent searches are based on laptop suggestions, and soon you'll start seeing ads on websites offering discounts on laptops. So, Ad Targeting is an advertisement technique meant to deliver ads automatically by using specialized software and algorithms that place ads depending on the user’s recent searches. Ad targeting was pegged to have secured 2.7 times as much revenue as non-targeted ads, as shown by a study conducted in 2009 by the Network Advertising Initiative.\n\nThus, it also helps in **Increasing Revenues** of the product's Organization."
    },
    {
      "metadata": {
        "_uuid": "605768a5174a2cdc24fd9c42539cdf2156c8fd3f"
      },
      "cell_type": "markdown",
      "source": "# Contents\n1. [**Exploring the Dataset**](#there_you_go_1)\n> *  [1.1 Importing Libraries ](#there_you_go_1.1)\n  * [1.2 Extract dataset ](#there_you_go_1.2)\n  >> * [1.2.1 Ratings](#there_you_go_1.2.1)\n    * [1.2.2 Movies](#there_you_go_1.2.2)\n  * [1.3 Combining the Movies and Ratings Dataframe ](#there_you_go_1.3)\n2. [**Visualizing the Dataset**](#there_you_go_2)\n> * [2.1 Genres ](#there_you_go_2.1)\n  * [2.2 Heavily Rated Movies ](#there_you_go_2.2)\n  * [2.3 Highly rated Movies](#there_you_go_2.3)\n  * [2.4 Mean ratings vs Total number of ratings](#there_you_go_2.4)\n3. [**Collaborative Filtering**](#there_you_go_3)\n> * [3.1 Create User-Item Matrix ](#there_you_go_3.1)\n * [3.2 Memory Based CF ](#there_you_go_3.2)\n >> * [3.2.1 User Based CF](#there_you_go_3.2.1)\n     * [3.2.2 Item based CF](#there_you_go_3.2.2)\n * [3.3 Model Based CF ](#there_you_go_3.3)\n >> * [3.3.1 K-Nearest Neighbour](#there_you_go_3.3.1)\n    * [3.3.2 Singular Value Decomposition](#there_you_go_3.3.2)\n    * [3.3.3 Non-Negative Matrix Factorization](#there_you_go_3.3.3)\n4. [**Matrix Factorization using Deep Learning**](#there_you_go_4)\n> * [4.1 Splitting Data into Train and Validation Set ](#there_you_go_4.1)\n * [4.2 Building the Model using Embedding Layers ](#there_you_go_4.2)\n * [4.3 Architecture 1 ](#there_you_go_4.3)\n * [4.4 Visulaizing the Model Architecture](#there_you_go_4.4)\n * [4.5 Compiling the Model ](#there_you_go_4.5)\n * [4.6 Fitting the Model](#there_you_go_4.6)\n * [4.7 Plotting Validation Curves](#there_you_go_4.7)\n * [4.8 Evaluating RMSE](#there_you_go_4.8)\n * [4.9 Architecture 2](#there_you_go_4.9)\n * [4.10 Visulaizing the Model Architecture](#there_you_go_4.10)\n * [4.11 Compiling the Model ](#there_you_go_4.11)\n * [4.12 Fitting the Model](#there_you_go_4.12)\n * [4.13 Plotting Validation Curves](#there_you_go_4.13)\n * [4.14 Evaluating RMSE](#there_you_go_4.14)\n5. [**References**](#there_you_go_5)"
    },
    {
      "metadata": {
        "_uuid": "b1c20eff9bfa834c52cc14c7ff3c502198e18d2e"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1\"></a>\n# 1) Exploring the Dataset\nThe dataset we are going to use is the MovieLens Dataset, which cotains 100k ratings of approximately 9000 movies by 700 users.\nLet's have a look at the dataset."
    },
    {
      "metadata": {
        "_uuid": "0a483be448fcd08898f87bf8136984055df69d8a"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1.1\"></a>\n## 1.1) Importing the Libraries "
    },
    {
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Ignore warnings :\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# Handle table-like data and matrices :\nimport numpy as np\nimport pandas as pd\nimport math \nimport itertools\n\n# Modelling Helpers :\nfrom sklearn.preprocessing import Imputer , Normalizer , scale\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.feature_selection import RFECV\nfrom sklearn.model_selection import GridSearchCV , KFold , cross_val_score\n\n\n\n# Evaluation metrics :\n\n# Regression\nfrom sklearn.metrics import mean_squared_log_error,mean_squared_error, r2_score,mean_absolute_error \n\n# Classification\nfrom sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n\n\n# Deep Learning Libraries\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, BatchNormalization\nfrom keras.optimizers import Adam,SGD,Adagrad,Adadelta,RMSprop\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import ReduceLROnPlateau, LearningRateScheduler\nfrom keras.utils import to_categorical\n\n\n# Visualisation\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport matplotlib.pylab as pylab\nimport seaborn as sns\nimport missingno as msno\n\n\n# Configure visualisations\n%matplotlib inline\nmpl.style.use( 'ggplot' )\nplt.style.use('fivethirtyeight')\nsns.set(context=\"notebook\", palette=\"dark\", style = 'whitegrid' , color_codes=True)",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "trusted": true
      },
      "cell_type": "code",
      "source": "# Center all plots\nfrom IPython.core.display import HTML\nHTML(\"\"\"\n<style>\n.output_png {\n    display: table-cell;\n    text-align: center;\n    vertical-align: middle;\n}\n</style>\n\"\"\");\n\n\n# Make Visualizations better\nparams = { \n    'axes.labelsize': \"large\",\n    'xtick.labelsize': 'x-large',\n    'legend.fontsize': 20,\n    'figure.dpi': 150,\n    'figure.figsize': [25, 7]\n}\nplt.rcParams.update(params)",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7757d7eb49a78b0b35f168d720f024ce173272b7"
      },
      "cell_type": "code",
      "source": "import os\nprint(os.listdir('../input/'))",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": "['links.csv', 'ratings.csv', 'movies.csv', 'tags.csv', 'README.txt']\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "584d9c76c6aa62617e9c4e3317538f72b8edf348"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1.2\"></a>\n## 1.2) Extract Dataset"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "82b99b1c7d3e3b31dc931858723cf5f94d4609b3"
      },
      "cell_type": "code",
      "source": "ratings = pd.read_csv('../input/ratings.csv')\nmovies = pd.read_csv('../input/movies.csv')\ndf_r = ratings.copy()\ndf_m = movies.copy()",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "8cc4959db5ac479ea2fa78d250e1be2fab81fc4c"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1.2.1\"></a>\n### 1.2.1) Ratings"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4c97be375d2ecf347ea13910686cc6942c378844",
        "scrolled": true
      },
      "cell_type": "code",
      "source": "ratings.head()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 12,
          "data": {
            "text/plain": "   userId  movieId  rating  timestamp\n0       1        1     4.0  964982703\n1       1        3     4.0  964981247\n2       1        6     4.0  964982224\n3       1       47     5.0  964983815\n4       1       50     5.0  964982931",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>964982703</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>964981247</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>6</td>\n      <td>4.0</td>\n      <td>964982224</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>47</td>\n      <td>5.0</td>\n      <td>964983815</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>50</td>\n      <td>5.0</td>\n      <td>964982931</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "e3ec44591a522a197809987ac97070c23da2c78b"
      },
      "cell_type": "markdown",
      "source": "As you can see the \"ratings\" dataframe has 4 columns.\n* **userId** - Every user is represented by an unique Id.\n* **movieId** - Every movie is represented by an uniue Id.\n* **rating** - Represents the rating given by the user to the corresponding movie.\n* **timestamp** - The time at which the rating was recorded."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cbf5afdd3663c9f0a206da2244063f41f4e87384"
      },
      "cell_type": "code",
      "source": "ratings.shape",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "(100836, 4)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6193a5bdb14f1bd80f84ba9327619c4f7c653802"
      },
      "cell_type": "code",
      "source": "ratings.describe()",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "              userId        movieId         rating     timestamp\ncount  100836.000000  100836.000000  100836.000000  1.008360e+05\nmean      326.127564   19435.295718       3.501557  1.205946e+09\nstd       182.618491   35530.987199       1.042529  2.162610e+08\nmin         1.000000       1.000000       0.500000  8.281246e+08\n25%       177.000000    1199.000000       3.000000  1.019124e+09\n50%       325.000000    2991.000000       3.500000  1.186087e+09\n75%       477.000000    8122.000000       4.000000  1.435994e+09\nmax       610.000000  193609.000000       5.000000  1.537799e+09",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>100836.000000</td>\n      <td>100836.000000</td>\n      <td>100836.000000</td>\n      <td>1.008360e+05</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>326.127564</td>\n      <td>19435.295718</td>\n      <td>3.501557</td>\n      <td>1.205946e+09</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>182.618491</td>\n      <td>35530.987199</td>\n      <td>1.042529</td>\n      <td>2.162610e+08</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.500000</td>\n      <td>8.281246e+08</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>177.000000</td>\n      <td>1199.000000</td>\n      <td>3.000000</td>\n      <td>1.019124e+09</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>325.000000</td>\n      <td>2991.000000</td>\n      <td>3.500000</td>\n      <td>1.186087e+09</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>477.000000</td>\n      <td>8122.000000</td>\n      <td>4.000000</td>\n      <td>1.435994e+09</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>610.000000</td>\n      <td>193609.000000</td>\n      <td>5.000000</td>\n      <td>1.537799e+09</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "d40afd33063271059ac295ac246146c41b2f6b07"
      },
      "cell_type": "markdown",
      "source": "Some insights that can be drawn are:\n\n1. The minimum rating given to the movie is 0,5 whereas the maximum rating given to the movies is 5.0\n2. The average rating that is the mean ratings given by the users to all the movies is 3.5\n3. The users have userId's in the range 1 - 610\n4. The movies have movieId's in the range 1 - 193609.( Note that 193609 is the highest movieId and not total number of movies. )\n\nHere, we will be dropping the timestamp attribute as we are not concerned with when the user rated a particular movie."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a5a04e2b5bc7625bba176707ef18194dd98afdeb"
      },
      "cell_type": "code",
      "source": "ratings.drop(['timestamp'], axis=1, inplace=True)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "25643d5a70b2dcf458d061f709aaef4a65f907a7"
      },
      "cell_type": "code",
      "source": "ratings.head()",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "   userId  movieId  rating\n0       1        1     4.0\n1       1        3     4.0\n2       1        6     4.0\n3       1       47     5.0\n4       1       50     5.0",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>6</td>\n      <td>4.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>47</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>50</td>\n      <td>5.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "0db882f8b0ba7ebb4be2e6099a866fe5515bce37"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1.2.2\"></a>\n### 1.2.2) Movies"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b85ac976102e84c1070c5439e07fde2a6d7e29d4"
      },
      "cell_type": "code",
      "source": "movies.head()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 17,
          "data": {
            "text/plain": "   movieId                     ...                                                            genres\n0        1                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n1        2                     ...                                        Adventure|Children|Fantasy\n2        3                     ...                                                    Comedy|Romance\n3        4                     ...                                              Comedy|Drama|Romance\n4        5                     ...                                                            Comedy\n\n[5 rows x 3 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>movieId</th>\n      <th>title</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>Jumanji (1995)</td>\n      <td>Adventure|Children|Fantasy</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Grumpier Old Men (1995)</td>\n      <td>Comedy|Romance</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Waiting to Exhale (1995)</td>\n      <td>Comedy|Drama|Romance</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Father of the Bride Part II (1995)</td>\n      <td>Comedy</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "83d52a2ec24be3b1ed019da5a88cd2ead2a15033"
      },
      "cell_type": "markdown",
      "source": "As you can see the \"movies\" dataframe has 3 columns:\n* **movieId** - Every movie is represented by an unique Id.\n* **title** - Movie which is represented by the corresponding movieId.\n* **genres** - Represents category of the movie."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f172700ce0f457bf8d477f88c09d110984436406"
      },
      "cell_type": "code",
      "source": "print('Shape: ', movies.shape, '\\n')\nmovies.info()",
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Shape:  (9742, 3) \n\n<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 9742 entries, 0 to 9741\nData columns (total 3 columns):\nmovieId    9742 non-null int64\ntitle      9742 non-null object\ngenres     9742 non-null object\ndtypes: int64(1), object(2)\nmemory usage: 228.4+ KB\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "61294765bc05f49116a0f1ff287b48c007246327"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_1.3\"></a>\n## 1.3) Combining the Movies and Ratings DataFrame\nLet's have a combined view on both the ratings and movies dataframe.\n\nAnd for that we need to merge on \"movieId\" attribute since it is common between both the dataframes."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8f3642d37de0d4e04487e65b089ed12119d69e9f"
      },
      "cell_type": "code",
      "source": "df_combined = pd.merge(ratings, movies, on = 'movieId')",
      "execution_count": 19,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5c2c75b9f091959f3f36420ac2140858d00706cb"
      },
      "cell_type": "code",
      "source": "df_combined.head()",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "   userId                     ...                                                            genres\n0       1                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n1       5                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n2       7                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n3      15                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n4      17                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n\n[5 rows x 5 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>7</td>\n      <td>1</td>\n      <td>4.5</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>15</td>\n      <td>1</td>\n      <td>2.5</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17</td>\n      <td>1</td>\n      <td>4.5</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "31b90ef3e7ea9cb480f55e2a07ff5e9823cd0458"
      },
      "cell_type": "code",
      "source": "df_combined.shape",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "(100836, 5)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "09928fa4f6e71d5a508fcedd6557a11ffbf7ebd2"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2\"></a>\n# 2) Visualizations on the Dataset"
    },
    {
      "metadata": {
        "_uuid": "2fe4775471fd87bfe678f65a4b915ffff6a8a8d2"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2.1\"></a>\n## 2.1) Genres\nLets have a look at various genres in the Dataset."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a897520396787e720f8ca7198a3f7baa1517ab77"
      },
      "cell_type": "code",
      "source": "# Create a function to find genres in the dataset\n\ngenres = {} # create a dictionary to store different genre values\n\ndef find_genres():\n    for genre in movies['genres']:\n        words = genre.split('|')\n        for word in words:\n            genres[word] = genres.get(word, 0) + 1\n            \nfind_genres()",
      "execution_count": 22,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "952c4be8b9e0bdace08883beaa399d8f230f0584"
      },
      "cell_type": "code",
      "source": "genres",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "{'Adventure': 1263,\n 'Animation': 611,\n 'Children': 664,\n 'Comedy': 3756,\n 'Fantasy': 779,\n 'Romance': 1596,\n 'Drama': 4361,\n 'Action': 1828,\n 'Crime': 1199,\n 'Thriller': 1894,\n 'Horror': 978,\n 'Mystery': 573,\n 'Sci-Fi': 980,\n 'War': 382,\n 'Musical': 334,\n 'Documentary': 440,\n 'IMAX': 158,\n 'Western': 167,\n 'Film-Noir': 87,\n '(no genres listed)': 34}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "304e7477eabdd4326ba063b621e66432bbeffcdb"
      },
      "cell_type": "code",
      "source": "# replace '(no genres listed)' by 'None'\ngenres['None'] = genres.pop('(no genres listed)')",
      "execution_count": 24,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "0b90c9816f32a5d944f0e00c19ddfa209495091a"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2.1.1\"></a>\n### 2.1.1) Genre WordCloud"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7d252757351e8d36fd3b394fa8bd84c7f3156ed1"
      },
      "cell_type": "code",
      "source": "from wordcloud import WordCloud\n\nwordcloud = WordCloud(width=400, height=200, background_color = 'black', min_font_size=7).generate_from_frequencies(genres)\n\nplt.imshow(wordcloud)\nplt.axis('off')\nplt.show()",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "85c25f351caa1130395a776dd30a6880e5d30d45"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2.2\"></a>\n## 2.2) Heavily Rated Movies"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c47ad156607971607898ae65749a381061f09b02"
      },
      "cell_type": "code",
      "source": "df_n_ratings = pd.DataFrame(df_combined.groupby('title')['rating'].mean())\ndf_n_ratings['total ratings'] = pd.DataFrame(df_combined.groupby('title')['rating'].count())\ndf_n_ratings.rename(columns = {'rating': 'mean ratings'}, inplace=True)\n\ndf_n_ratings.sort_values('total ratings', ascending=False).head(10)",
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 26,
          "data": {
            "text/plain": "                                           mean ratings  total ratings\ntitle                                                                 \nForrest Gump (1994)                            4.164134            329\nShawshank Redemption, The (1994)               4.429022            317\nPulp Fiction (1994)                            4.197068            307\nSilence of the Lambs, The (1991)               4.161290            279\nMatrix, The (1999)                             4.192446            278\nStar Wars: Episode IV - A New Hope (1977)      4.231076            251\nJurassic Park (1993)                           3.750000            238\nBraveheart (1995)                              4.031646            237\nTerminator 2: Judgment Day (1991)              3.970982            224\nSchindler's List (1993)                        4.225000            220",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean ratings</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Forrest Gump (1994)</th>\n      <td>4.164134</td>\n      <td>329</td>\n    </tr>\n    <tr>\n      <th>Shawshank Redemption, The (1994)</th>\n      <td>4.429022</td>\n      <td>317</td>\n    </tr>\n    <tr>\n      <th>Pulp Fiction (1994)</th>\n      <td>4.197068</td>\n      <td>307</td>\n    </tr>\n    <tr>\n      <th>Silence of the Lambs, The (1991)</th>\n      <td>4.161290</td>\n      <td>279</td>\n    </tr>\n    <tr>\n      <th>Matrix, The (1999)</th>\n      <td>4.192446</td>\n      <td>278</td>\n    </tr>\n    <tr>\n      <th>Star Wars: Episode IV - A New Hope (1977)</th>\n      <td>4.231076</td>\n      <td>251</td>\n    </tr>\n    <tr>\n      <th>Jurassic Park (1993)</th>\n      <td>3.750000</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>Braveheart (1995)</th>\n      <td>4.031646</td>\n      <td>237</td>\n    </tr>\n    <tr>\n      <th>Terminator 2: Judgment Day (1991)</th>\n      <td>3.970982</td>\n      <td>224</td>\n    </tr>\n    <tr>\n      <th>Schindler's List (1993)</th>\n      <td>4.225000</td>\n      <td>220</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "004505c4a7e7bc97ba9cf99bb84cda1697999706"
      },
      "cell_type": "markdown",
      "source": "Most of these movies stand among the Top 50 movies in the IMDB ratings even today."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1ef247ab14cae32999e18bebc9e77b1573cf25f6"
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(8,4))\nsns.distplot(df_n_ratings['total ratings'], bins=20)\nplt.xlabel('Total Number of Ratings')\nplt.ylabel('Probability')\nplt.show()",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x600 with 1 Axes>",
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "8e4300398ad483f6eae5c794d39d857590edf137"
      },
      "cell_type": "markdown",
      "source": "We can see that - \n* Majority of the movies have less than 50 ratings.\n* The number of movies having more than 100 ratings is very low.\n\nRefer one cell above, we can see that there are only 3 movies with 300+ ratings."
    },
    {
      "metadata": {
        "_uuid": "78d81e82705c100c4a940671a0c9b46b1709c473"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2.3\"></a>\n## 2.3) Highly Rated Movies"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e85d5bb8cc0889b9359ce84833ca970ca3d6607e"
      },
      "cell_type": "code",
      "source": "df_n_ratings.sort_values('mean ratings', ascending=False).head(10)",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "                                           mean ratings  total ratings\ntitle                                                                 \nGena the Crocodile (1969)                           5.0              1\nTrue Stories (1986)                                 5.0              1\nCosmic Scrat-tastrophe (2015)                       5.0              1\nLove and Pigeons (1985)                             5.0              1\nRed Sorghum (Hong gao liang) (1987)                 5.0              1\nThin Line Between Love and Hate, A (1996)           5.0              1\nLesson Faust (1994)                                 5.0              2\nEva (2011)                                          5.0              1\nWho Killed Chea Vichea? (2010)                      5.0              1\nSiam Sunset (1999)                                  5.0              1",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean ratings</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Gena the Crocodile (1969)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>True Stories (1986)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Cosmic Scrat-tastrophe (2015)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Love and Pigeons (1985)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Red Sorghum (Hong gao liang) (1987)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Thin Line Between Love and Hate, A (1996)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Lesson Faust (1994)</th>\n      <td>5.0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>Eva (2011)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Who Killed Chea Vichea? (2010)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>Siam Sunset (1999)</th>\n      <td>5.0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "710829904ef7b77eea10789c5babea7e5843e027"
      },
      "cell_type": "code",
      "source": "print('Total no of users that gave rating of 5.0 : ', len(df_n_ratings.loc[df_n_ratings['mean ratings'] == 5]), '\\n')\nprint('Total no of Individual users that gave rating of 5.0 : ', len(df_n_ratings.loc[(df_n_ratings['mean ratings'] == 5) \n                                                                           & (df_n_ratings['total ratings'] == 1)]))",
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Total no of users that gave rating of 5.0 :  296 \n\nTotal no of Individual users that gave rating of 5.0 :  289\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "0b148aadede48005bb067748d7a227ff5d844f39"
      },
      "cell_type": "markdown",
      "source": "1. As you can see there are over 296 users that have rated 5 stars, among which there are 289 individual raters ( only user to rate the movie 5 star ).\n\n2. So, this cannot be the lone factor that should be considered while recommending movies. As this factor only shows the preferences of a particular user.\n\n3. It would make a good recommendation system if we can use both the factors ( -> Highly Rated Movies and Heavily Rated Movies <- ) together."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "85594914dfca2b6d1130cacb55e57d12a9f011c9"
      },
      "cell_type": "code",
      "source": "plt.figure(figsize=(8,4))\nsns.distplot(df_n_ratings['mean ratings'], bins=30)\nplt.xlabel('Mean Ratings')\nplt.ylabel('Probability')\nplt.show()",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x600 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "3658e4d3d80fd064f48589f529cd4594c9b87721"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_2.4\"></a>\n## 2.4) Mean Ratings vs Total Number of Ratings"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "af127ded1183c61079fa2395d181996e48904015"
      },
      "cell_type": "code",
      "source": "sns.jointplot(x = 'mean ratings', y = 'total ratings', data = df_n_ratings )",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "<seaborn.axisgrid.JointGrid at 0x7f1dc4555160>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 900x900 with 3 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "f4bb3964e26346357c1a1da3b5bea316491f5379"
      },
      "cell_type": "markdown",
      "source": "* Here, as you can see every Data Point represents a distinct Movie, with y-coordinate representing the total no of users which has rated that movie and x-coordinate representing the mean of all the ratings of the corresponding users.\n* Also you can see that there is a huge Density in the region corresponding to 0-50 no of users and between mean rating 3-4 ."
    },
    {
      "metadata": {
        "_uuid": "8e9adee013ed2c8e089ebc17bcb30d8a2fd45efc"
      },
      "cell_type": "markdown",
      "source": "Many more Visualizations can be drawn and many different conclusions can be inferred, But here, I'm going to focus on Collaborative Filtering.\n\nSo, let's proceed to what is Collaborative Filtering and how it is used in Recommendation Systems?"
    },
    {
      "metadata": {
        "_uuid": "90c097447ca91831c3f2589158e574dbc4f22176"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3\"></a>\n# 3) Collaborative Filtering (CF)"
    },
    {
      "metadata": {
        "_uuid": "d526c06b1c8c6acb67896f5715ef663a2865ed11"
      },
      "cell_type": "markdown",
      "source": "**Collaborative filtering** is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources.Basically, it is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users. \n\nThere are 2 approaches to CF -->\n\n1) **Memory-Based CF** - It is an approach which finds similarity between users or between items to recommend similar items. Examples include Neighbourhood-based CF and Item-based/User-based top-N recommendations.\n\n2) **Model-Based CF** - In this approach we use different data mining, machine learning algorithms to predict users' rating of unrated items.  Examples include Singular Value Decomposition (SVD) , Principal Component Analysis (PCA) etc."
    },
    {
      "metadata": {
        "_uuid": "d5d73487ddb54a1a5103a1214419e22ae42a483c"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.1\"></a>\n## 3.1) Create User-Item Matrix"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c9cdb0771a1eaf51732caeea3b9dc0944a3310cc"
      },
      "cell_type": "code",
      "source": "util_mat = df_combined.pivot_table(index = 'userId', columns = 'title', values = 'rating')\nutil_mat.head(20)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "title   '71 (2014)                    ...                      À nous la liberté (Freedom for Us) (1931)\nuserId                                ...                                                               \n1              NaN                    ...                                                            NaN\n2              NaN                    ...                                                            NaN\n3              NaN                    ...                                                            NaN\n4              NaN                    ...                                                            NaN\n5              NaN                    ...                                                            NaN\n6              NaN                    ...                                                            NaN\n7              NaN                    ...                                                            NaN\n8              NaN                    ...                                                            NaN\n9              NaN                    ...                                                            NaN\n10             NaN                    ...                                                            NaN\n11             NaN                    ...                                                            NaN\n12             NaN                    ...                                                            NaN\n13             NaN                    ...                                                            NaN\n14             NaN                    ...                                                            NaN\n15             NaN                    ...                                                            NaN\n16             NaN                    ...                                                            NaN\n17             NaN                    ...                                                            NaN\n18             NaN                    ...                                                            NaN\n19             NaN                    ...                                                            NaN\n20             NaN                    ...                                                            NaN\n\n[20 rows x 9719 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>title</th>\n      <th>'71 (2014)</th>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <th>'Round Midnight (1986)</th>\n      <th>'Salem's Lot (2004)</th>\n      <th>'Til There Was You (1997)</th>\n      <th>'Tis the Season for Love (2015)</th>\n      <th>'burbs, The (1989)</th>\n      <th>'night Mother (1986)</th>\n      <th>(500) Days of Summer (2009)</th>\n      <th>*batteries not included (1987)</th>\n      <th>...All the Marbles (1981)</th>\n      <th>...And Justice for All (1979)</th>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <th>1-900 (06) (1994)</th>\n      <th>10 (1979)</th>\n      <th>10 Cent Pistol (2015)</th>\n      <th>10 Cloverfield Lane (2016)</th>\n      <th>10 Items or Less (2006)</th>\n      <th>10 Things I Hate About You (1999)</th>\n      <th>10 Years (2011)</th>\n      <th>10,000 BC (2008)</th>\n      <th>100 Girls (2000)</th>\n      <th>100 Streets (2016)</th>\n      <th>101 Dalmatians (1996)</th>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <th>102 Dalmatians (2000)</th>\n      <th>10th &amp; Wolf (2006)</th>\n      <th>10th Kingdom, The (2000)</th>\n      <th>10th Victim, The (La decima vittima) (1965)</th>\n      <th>11'09\"01 - September 11 (2002)</th>\n      <th>11:14 (2003)</th>\n      <th>11th Hour, The (2007)</th>\n      <th>12 Angry Men (1957)</th>\n      <th>12 Angry Men (1997)</th>\n      <th>12 Chairs (1971)</th>\n      <th>12 Chairs (1976)</th>\n      <th>12 Rounds (2009)</th>\n      <th>12 Years a Slave (2013)</th>\n      <th>...</th>\n      <th>Zathura (2005)</th>\n      <th>Zatoichi and the Chest of Gold (Zatôichi senryô-kubi) (Zatôichi 6) (1964)</th>\n      <th>Zazie dans le métro (1960)</th>\n      <th>Zebraman (2004)</th>\n      <th>Zed &amp; Two Noughts, A (1985)</th>\n      <th>Zeitgeist: Addendum (2008)</th>\n      <th>Zeitgeist: Moving Forward (2011)</th>\n      <th>Zeitgeist: The Movie (2007)</th>\n      <th>Zelary (2003)</th>\n      <th>Zelig (1983)</th>\n      <th>Zero Dark Thirty (2012)</th>\n      <th>Zero Effect (1998)</th>\n      <th>Zero Theorem, The (2013)</th>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <th>Zeus and Roxanne (1997)</th>\n      <th>Zipper (2015)</th>\n      <th>Zodiac (2007)</th>\n      <th>Zombeavers (2014)</th>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <th>Zombie Strippers! (2008)</th>\n      <th>Zombieland (2009)</th>\n      <th>Zone 39 (1997)</th>\n      <th>Zone, The (La Zona) (2007)</th>\n      <th>Zookeeper (2011)</th>\n      <th>Zoolander (2001)</th>\n      <th>Zoolander 2 (2016)</th>\n      <th>Zoom (2006)</th>\n      <th>Zoom (2015)</th>\n      <th>Zootopia (2016)</th>\n      <th>Zulu (1964)</th>\n      <th>Zulu (2013)</th>\n      <th>[REC] (2007)</th>\n      <th>[REC]² (2009)</th>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <th>eXistenZ (1999)</th>\n      <th>xXx (2002)</th>\n      <th>xXx: State of the Union (2005)</th>\n      <th>¡Three Amigos! (1986)</th>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.5</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "ee8a4b5af65524dd3885f4f030df18a7147f30ee"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.2\"></a>\n## 3.2) Memory Based Collaborative Filtering"
    },
    {
      "metadata": {
        "_uuid": "104b464ee6d44688f2c120193855179204592a75"
      },
      "cell_type": "markdown",
      "source": "There are 2 approaches to Memory-Based CF -->\n\n1) **User-User Collaborative Filtering** - In this we we calculate similarity of all the users to the active user ( the user whom the prediction is for ).Then sort and filter the Top-N users to make predictions for the active user. This is usually very effective but takes a lot of time and resources. For example if Dennis and Davis like the same movies and a new movie comes out that Davis likes,then we can recommend that movie to Dennis because Davis and Dennis seem to like the same movies.\n\n2) **Item-Item Collaborative Filtering** - This is similar to User-User CF, just that we now compute similarity between items to recommend similar items. Eg. When you buy any product on Amazon, you will find this line \"Users who bought this item also bought...\", so Amazon uses item-item CF widely, Mind that I'm not saying they use only item-item CF, they have hybrid techniques to better suit users of even unique interests.\n\nItem-Item CF are a lot faster than User-User CF. and secondly user profiles changes quickly and the entire system model has to be recomputed, whereas item's average ratings doesn't change that quickly, and this leads to more stable rating distributions in the model, so the model doesn't have to be rebuilt as often."
    },
    {
      "metadata": {
        "_uuid": "71a4c5d724ea1a62b7220c0b904b0ef24680979f"
      },
      "cell_type": "markdown",
      "source": "**Q) How do we calculate similarity?**\n\n**Ans.** There are many measures to calculate the similarity matrix, some of them are -->\n\n1) **Jaccard Similarity** - It is a statistic used for comparing the similarity and diversity of sample sets. It is defined as the size of the intersection divided by the size of the union of the sample sets.\n\n2) **Cosine Similarity** - It measures the angle between the ratings vector. If the angle is 0°, then they are vectors having same orientation and if the angle is 180°, then they are highly dissimilar vectors.\n\n3) **Pearson Similarity** - It is actually Centered-Cosine similarity. We subtract the mean ratings from the user ratings, so that the mean is centered at 0, and then calculate the cosine similarity."
    },
    {
      "metadata": {
        "_uuid": "82fef40ac1a4136eeeea55f5881335087ad571a3"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.2.1\"></a>\n### 3.2.1) User based Collaborative Filtering"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "38ae615711be06f6a1aa16770ddd6ead7919bb56"
      },
      "cell_type": "code",
      "source": "user_util_matrix = util_mat.copy()\n\n# We will fill the row wise NaN's with the corresponding user's mean ratings, so that we can carry out Pearson correlation.\n# Here we assume avg ratings for the movie that is not rated.\nuser_util_matrix = user_util_matrix.apply(lambda row: row.fillna(row.mean()), axis=1)\nuser_util_matrix.head(5)",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "title   '71 (2014)                    ...                      À nous la liberté (Freedom for Us) (1931)\nuserId                                ...                                                               \n1         4.366379                    ...                                                       4.366379\n2         3.948276                    ...                                                       3.948276\n3         2.435897                    ...                                                       2.435897\n4         3.555556                    ...                                                       3.555556\n5         3.636364                    ...                                                       3.636364\n\n[5 rows x 9719 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>title</th>\n      <th>'71 (2014)</th>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <th>'Round Midnight (1986)</th>\n      <th>'Salem's Lot (2004)</th>\n      <th>'Til There Was You (1997)</th>\n      <th>'Tis the Season for Love (2015)</th>\n      <th>'burbs, The (1989)</th>\n      <th>'night Mother (1986)</th>\n      <th>(500) Days of Summer (2009)</th>\n      <th>*batteries not included (1987)</th>\n      <th>...All the Marbles (1981)</th>\n      <th>...And Justice for All (1979)</th>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <th>1-900 (06) (1994)</th>\n      <th>10 (1979)</th>\n      <th>10 Cent Pistol (2015)</th>\n      <th>10 Cloverfield Lane (2016)</th>\n      <th>10 Items or Less (2006)</th>\n      <th>10 Things I Hate About You (1999)</th>\n      <th>10 Years (2011)</th>\n      <th>10,000 BC (2008)</th>\n      <th>100 Girls (2000)</th>\n      <th>100 Streets (2016)</th>\n      <th>101 Dalmatians (1996)</th>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <th>102 Dalmatians (2000)</th>\n      <th>10th &amp; Wolf (2006)</th>\n      <th>10th Kingdom, The (2000)</th>\n      <th>10th Victim, The (La decima vittima) (1965)</th>\n      <th>11'09\"01 - September 11 (2002)</th>\n      <th>11:14 (2003)</th>\n      <th>11th Hour, The (2007)</th>\n      <th>12 Angry Men (1957)</th>\n      <th>12 Angry Men (1997)</th>\n      <th>12 Chairs (1971)</th>\n      <th>12 Chairs (1976)</th>\n      <th>12 Rounds (2009)</th>\n      <th>12 Years a Slave (2013)</th>\n      <th>...</th>\n      <th>Zathura (2005)</th>\n      <th>Zatoichi and the Chest of Gold (Zatôichi senryô-kubi) (Zatôichi 6) (1964)</th>\n      <th>Zazie dans le métro (1960)</th>\n      <th>Zebraman (2004)</th>\n      <th>Zed &amp; Two Noughts, A (1985)</th>\n      <th>Zeitgeist: Addendum (2008)</th>\n      <th>Zeitgeist: Moving Forward (2011)</th>\n      <th>Zeitgeist: The Movie (2007)</th>\n      <th>Zelary (2003)</th>\n      <th>Zelig (1983)</th>\n      <th>Zero Dark Thirty (2012)</th>\n      <th>Zero Effect (1998)</th>\n      <th>Zero Theorem, The (2013)</th>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <th>Zeus and Roxanne (1997)</th>\n      <th>Zipper (2015)</th>\n      <th>Zodiac (2007)</th>\n      <th>Zombeavers (2014)</th>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <th>Zombie Strippers! (2008)</th>\n      <th>Zombieland (2009)</th>\n      <th>Zone 39 (1997)</th>\n      <th>Zone, The (La Zona) (2007)</th>\n      <th>Zookeeper (2011)</th>\n      <th>Zoolander (2001)</th>\n      <th>Zoolander 2 (2016)</th>\n      <th>Zoom (2006)</th>\n      <th>Zoom (2015)</th>\n      <th>Zootopia (2016)</th>\n      <th>Zulu (1964)</th>\n      <th>Zulu (2013)</th>\n      <th>[REC] (2007)</th>\n      <th>[REC]² (2009)</th>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <th>eXistenZ (1999)</th>\n      <th>xXx (2002)</th>\n      <th>xXx: State of the Union (2005)</th>\n      <th>¡Three Amigos! (1986)</th>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>...</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.366379</td>\n      <td>4.000000</td>\n      <td>4.366379</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>...</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.000000</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n      <td>3.948276</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>...</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n      <td>2.435897</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>5.000000</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>...</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n      <td>3.555556</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>...</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n      <td>3.636364</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "96f2450c5cb20a39dcfc6449c9ea342c2f1e22e6"
      },
      "cell_type": "code",
      "source": "user_util_matrix.T.corr()",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 34,
          "data": {
            "text/plain": "userId       1             2      ...              609       610\nuserId                            ...                           \n1       1.000000  1.264516e-03    ...    -2.571255e-02  0.010932\n2       0.001265  1.000000e+00    ...    -6.009100e-02  0.024999\n3       0.000553 -8.476071e-25    ...     7.905178e-25  0.019550\n4       0.048419 -1.716402e-02    ...    -1.788358e-02 -0.000995\n5       0.021847  2.179571e-02    ...     9.382892e-02 -0.000278\n6      -0.045497 -2.105129e-02    ...     5.301727e-02  0.009603\n7      -0.006200 -1.111357e-02    ...     8.754391e-03  0.068430\n8       0.047013 -4.808546e-02    ...     7.718048e-02  0.017144\n9       0.019510 -5.534891e-23    ...     5.226205e-23  0.051898\n10     -0.008754  3.011629e-03    ...    -4.009050e-02 -0.026004\n11     -0.056206 -2.745410e-03    ...     4.766628e-02  0.026876\n12      0.013266  0.000000e+00    ...     0.000000e+00 -0.009941\n13      0.059383  3.047559e-03    ...     2.391257e-23  0.077008\n14      0.021815  1.251784e-02    ...    -1.007497e-02  0.001992\n15      0.037209 -3.328039e-02    ...     7.066206e-02 -0.014099\n16      0.009664 -1.477139e-02    ...    -3.346090e-02  0.008550\n17      0.037997 -1.262775e-03    ...     1.243348e-01  0.042927\n18      0.043789 -4.782450e-02    ...     4.882887e-02  0.100803\n19      0.085249 -1.060765e-04    ...    -8.841222e-03  0.081843\n20      0.066990  6.099867e-04    ...     5.972991e-24  0.069265\n21      0.011732 -1.948576e-03    ...     3.429030e-02  0.000528\n22     -0.004171 -2.482779e-02    ...     1.012680e-01  0.020412\n23     -0.023124 -1.169271e-03    ...     1.110310e-02  0.002287\n24      0.008417 -3.741771e-02    ...     8.386414e-02  0.031640\n25      0.001181  1.620844e-02    ...     4.295918e-02  0.033907\n26     -0.013518  5.351820e-23    ...     1.609377e-01  0.046661\n27      0.045303 -3.754397e-23    ...     4.257213e-02 -0.010629\n28      0.001611 -4.240417e-03    ...     5.065528e-02  0.063953\n29     -0.006969 -1.971385e-02    ...     5.578511e-03  0.027594\n30      0.003274 -3.399468e-02    ...     5.300944e-02 -0.031724\n...          ...           ...    ...              ...       ...\n581     0.007481 -7.127559e-02    ...     6.800770e-02 -0.014157\n582     0.009522 -2.549881e-02    ...     3.888094e-23  0.054730\n583    -0.005893 -1.193537e-03    ...     6.361259e-02 -0.005362\n584    -0.011143 -6.607594e-23    ...    -1.322189e-02  0.018099\n585     0.010023  1.072662e-02    ...     3.974755e-02  0.012454\n586     0.011086 -1.253459e-02    ...     2.542629e-02  0.013240\n587    -0.043080 -4.196319e-23    ...     3.874710e-02  0.021618\n588    -0.019135 -4.808986e-02    ...     1.774925e-01  0.036834\n589    -0.059246 -3.566801e-02    ...     2.082214e-01  0.008520\n590     0.095191  2.702576e-03    ...     3.934268e-02  0.142449\n591     0.038276  2.482889e-03    ...     2.318105e-02  0.020651\n592    -0.059507 -1.031647e-02    ...     4.307486e-02  0.028104\n593     0.082403 -2.612039e-02    ...     6.486151e-02  0.033001\n594    -0.006195  3.381087e-04    ...    -6.963011e-02 -0.002394\n595     0.036529 -4.207526e-23    ...     3.974119e-23  0.009556\n596     0.035349  3.126313e-02    ...     3.605659e-03  0.130250\n597     0.102631  6.004722e-03    ...     3.021940e-02  0.053947\n598     0.004179  1.917654e-03    ...     2.892179e-23 -0.008280\n599     0.052614 -2.129451e-04    ...     2.847442e-02  0.205007\n600     0.069528 -9.797327e-04    ...     7.313385e-02  0.080934\n601     0.018127 -5.055136e-02    ...     4.346543e-02  0.021603\n602    -0.017172 -3.158078e-02    ...     6.239957e-02  0.030061\n603    -0.015221 -1.688095e-03    ...     1.533445e-02  0.051255\n604    -0.037059 -3.345291e-23    ...     9.403771e-02  0.015621\n605    -0.029121 -3.088913e-23    ...    -5.472183e-02  0.069837\n606     0.012016  6.225827e-03    ...     3.842210e-02  0.075464\n607     0.055261 -2.050374e-02    ...     1.904919e-02  0.021860\n608     0.075224 -6.000828e-03    ...     5.071449e-02  0.054454\n609    -0.025713 -6.009100e-02    ...     1.000000e+00 -0.012471\n610     0.010932  2.499921e-02    ...    -1.247143e-02  1.000000\n\n[610 rows x 610 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>userId</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n      <th>13</th>\n      <th>14</th>\n      <th>15</th>\n      <th>16</th>\n      <th>17</th>\n      <th>18</th>\n      <th>19</th>\n      <th>20</th>\n      <th>21</th>\n      <th>22</th>\n      <th>23</th>\n      <th>24</th>\n      <th>25</th>\n      <th>26</th>\n      <th>27</th>\n      <th>28</th>\n      <th>29</th>\n      <th>30</th>\n      <th>31</th>\n      <th>32</th>\n      <th>33</th>\n      <th>34</th>\n      <th>35</th>\n      <th>36</th>\n      <th>37</th>\n      <th>38</th>\n      <th>39</th>\n      <th>40</th>\n      <th>...</th>\n      <th>571</th>\n      <th>572</th>\n      <th>573</th>\n      <th>574</th>\n      <th>575</th>\n      <th>576</th>\n      <th>577</th>\n      <th>578</th>\n      <th>579</th>\n      <th>580</th>\n      <th>581</th>\n      <th>582</th>\n      <th>583</th>\n      <th>584</th>\n      <th>585</th>\n      <th>586</th>\n      <th>587</th>\n      <th>588</th>\n      <th>589</th>\n      <th>590</th>\n      <th>591</th>\n      <th>592</th>\n      <th>593</th>\n      <th>594</th>\n      <th>595</th>\n      <th>596</th>\n      <th>597</th>\n      <th>598</th>\n      <th>599</th>\n      <th>600</th>\n      <th>601</th>\n      <th>602</th>\n      <th>603</th>\n      <th>604</th>\n      <th>605</th>\n      <th>606</th>\n      <th>607</th>\n      <th>608</th>\n      <th>609</th>\n      <th>610</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1.000000</td>\n      <td>1.264516e-03</td>\n      <td>5.525772e-04</td>\n      <td>0.048419</td>\n      <td>2.184657e-02</td>\n      <td>-0.045497</td>\n      <td>-6.199672e-03</td>\n      <td>4.701283e-02</td>\n      <td>1.950985e-02</td>\n      <td>-8.754088e-03</td>\n      <td>-0.056206</td>\n      <td>0.013266</td>\n      <td>5.938345e-02</td>\n      <td>2.181535e-02</td>\n      <td>0.037209</td>\n      <td>0.009664</td>\n      <td>0.037997</td>\n      <td>0.043789</td>\n      <td>0.085249</td>\n      <td>6.699047e-02</td>\n      <td>0.011732</td>\n      <td>-4.170826e-03</td>\n      <td>-2.312394e-02</td>\n      <td>0.008417</td>\n      <td>1.181400e-03</td>\n      <td>-1.351819e-02</td>\n      <td>4.530300e-02</td>\n      <td>0.001611</td>\n      <td>-6.969388e-03</td>\n      <td>3.274438e-03</td>\n      <td>6.956394e-03</td>\n      <td>0.042510</td>\n      <td>0.012918</td>\n      <td>0.011187</td>\n      <td>2.150784e-02</td>\n      <td>2.652436e-02</td>\n      <td>-4.934852e-02</td>\n      <td>0.009979</td>\n      <td>-0.072666</td>\n      <td>-4.175337e-02</td>\n      <td>...</td>\n      <td>-3.037625e-02</td>\n      <td>0.046056</td>\n      <td>1.408778e-02</td>\n      <td>4.071870e-02</td>\n      <td>1.232650e-03</td>\n      <td>-8.180768e-05</td>\n      <td>0.089396</td>\n      <td>1.834885e-23</td>\n      <td>-1.050026e-01</td>\n      <td>0.019428</td>\n      <td>0.007481</td>\n      <td>9.521898e-03</td>\n      <td>-5.892852e-03</td>\n      <td>-1.114252e-02</td>\n      <td>1.002267e-02</td>\n      <td>0.011086</td>\n      <td>-4.307956e-02</td>\n      <td>-0.019135</td>\n      <td>-5.924583e-02</td>\n      <td>0.095191</td>\n      <td>3.827620e-02</td>\n      <td>-5.950684e-02</td>\n      <td>0.082403</td>\n      <td>-0.006195</td>\n      <td>3.652909e-02</td>\n      <td>0.035349</td>\n      <td>0.102631</td>\n      <td>4.179383e-03</td>\n      <td>0.052614</td>\n      <td>0.069528</td>\n      <td>0.018127</td>\n      <td>-0.017172</td>\n      <td>-0.015221</td>\n      <td>-3.705875e-02</td>\n      <td>-2.912138e-02</td>\n      <td>0.012016</td>\n      <td>0.055261</td>\n      <td>0.075224</td>\n      <td>-2.571255e-02</td>\n      <td>0.010932</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.001265</td>\n      <td>1.000000e+00</td>\n      <td>-8.476071e-25</td>\n      <td>-0.017164</td>\n      <td>2.179571e-02</td>\n      <td>-0.021051</td>\n      <td>-1.111357e-02</td>\n      <td>-4.808546e-02</td>\n      <td>-5.534891e-23</td>\n      <td>3.011629e-03</td>\n      <td>-0.002745</td>\n      <td>0.000000</td>\n      <td>3.047559e-03</td>\n      <td>1.251784e-02</td>\n      <td>-0.033280</td>\n      <td>-0.014771</td>\n      <td>-0.001263</td>\n      <td>-0.047825</td>\n      <td>-0.000106</td>\n      <td>6.099867e-04</td>\n      <td>-0.001949</td>\n      <td>-2.482779e-02</td>\n      <td>-1.169271e-03</td>\n      <td>-0.037418</td>\n      <td>1.620844e-02</td>\n      <td>5.351820e-23</td>\n      <td>-3.754397e-23</td>\n      <td>-0.004240</td>\n      <td>-1.971385e-02</td>\n      <td>-3.399468e-02</td>\n      <td>-1.282363e-22</td>\n      <td>-0.032900</td>\n      <td>0.008467</td>\n      <td>-0.008806</td>\n      <td>-5.546318e-24</td>\n      <td>-3.370691e-02</td>\n      <td>7.806392e-03</td>\n      <td>-0.042923</td>\n      <td>0.000000</td>\n      <td>-2.610328e-02</td>\n      <td>...</td>\n      <td>-9.716309e-24</td>\n      <td>-0.006322</td>\n      <td>2.238275e-02</td>\n      <td>-5.331292e-02</td>\n      <td>5.742380e-23</td>\n      <td>-8.105733e-23</td>\n      <td>-0.029436</td>\n      <td>6.444172e-23</td>\n      <td>4.037417e-23</td>\n      <td>-0.009902</td>\n      <td>-0.071276</td>\n      <td>-2.549881e-02</td>\n      <td>-1.193537e-03</td>\n      <td>-6.607594e-23</td>\n      <td>1.072662e-02</td>\n      <td>-0.012535</td>\n      <td>-4.196319e-23</td>\n      <td>-0.048090</td>\n      <td>-3.566801e-02</td>\n      <td>0.002703</td>\n      <td>2.482889e-03</td>\n      <td>-1.031647e-02</td>\n      <td>-0.026120</td>\n      <td>0.000338</td>\n      <td>-4.207526e-23</td>\n      <td>0.031263</td>\n      <td>0.006005</td>\n      <td>1.917654e-03</td>\n      <td>-0.000213</td>\n      <td>-0.000980</td>\n      <td>-0.050551</td>\n      <td>-0.031581</td>\n      <td>-0.001688</td>\n      <td>-3.345291e-23</td>\n      <td>-3.088913e-23</td>\n      <td>0.006226</td>\n      <td>-0.020504</td>\n      <td>-0.006001</td>\n      <td>-6.009100e-02</td>\n      <td>0.024999</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.000553</td>\n      <td>-8.476071e-25</td>\n      <td>1.000000e+00</td>\n      <td>-0.011260</td>\n      <td>-3.153892e-02</td>\n      <td>0.004800</td>\n      <td>3.308497e-25</td>\n      <td>-3.247105e-02</td>\n      <td>5.123183e-25</td>\n      <td>2.203980e-25</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.345529e-25</td>\n      <td>8.453024e-03</td>\n      <td>-0.046046</td>\n      <td>-0.005142</td>\n      <td>-0.004690</td>\n      <td>-0.012322</td>\n      <td>-0.009560</td>\n      <td>-2.114474e-02</td>\n      <td>-0.022848</td>\n      <td>1.023857e-02</td>\n      <td>-3.015000e-02</td>\n      <td>-0.009327</td>\n      <td>-1.535679e-02</td>\n      <td>-4.927043e-25</td>\n      <td>-5.515577e-03</td>\n      <td>0.016249</td>\n      <td>-1.753720e-02</td>\n      <td>-2.298376e-24</td>\n      <td>-1.704944e-03</td>\n      <td>-0.045848</td>\n      <td>-0.030532</td>\n      <td>0.029048</td>\n      <td>5.161233e-26</td>\n      <td>-6.239109e-25</td>\n      <td>-3.753826e-25</td>\n      <td>-0.028985</td>\n      <td>0.028645</td>\n      <td>-3.331081e-03</td>\n      <td>...</td>\n      <td>-2.542548e-02</td>\n      <td>-0.005930</td>\n      <td>-2.362342e-25</td>\n      <td>-3.146267e-25</td>\n      <td>-5.318024e-25</td>\n      <td>-4.999971e-02</td>\n      <td>-0.037601</td>\n      <td>-5.944731e-25</td>\n      <td>-1.013576e-03</td>\n      <td>0.015241</td>\n      <td>-0.037817</td>\n      <td>3.819230e-25</td>\n      <td>9.702808e-25</td>\n      <td>6.096952e-25</td>\n      <td>-2.118945e-25</td>\n      <td>0.031381</td>\n      <td>-1.332141e-02</td>\n      <td>-0.014251</td>\n      <td>-2.408582e-02</td>\n      <td>-0.028410</td>\n      <td>3.576149e-25</td>\n      <td>-3.792476e-25</td>\n      <td>0.010920</td>\n      <td>-0.007823</td>\n      <td>3.916421e-25</td>\n      <td>0.018377</td>\n      <td>-0.020662</td>\n      <td>2.830972e-25</td>\n      <td>0.000223</td>\n      <td>-0.004669</td>\n      <td>-0.004904</td>\n      <td>-0.016117</td>\n      <td>0.017749</td>\n      <td>3.085290e-25</td>\n      <td>-1.430628e-03</td>\n      <td>-0.037289</td>\n      <td>-0.007789</td>\n      <td>-0.013001</td>\n      <td>7.905178e-25</td>\n      <td>0.019550</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.048419</td>\n      <td>-1.716402e-02</td>\n      <td>-1.125978e-02</td>\n      <td>1.000000</td>\n      <td>-2.962008e-02</td>\n      <td>0.013956</td>\n      <td>5.809139e-02</td>\n      <td>2.064948e-03</td>\n      <td>-5.873603e-03</td>\n      <td>5.159032e-02</td>\n      <td>0.062453</td>\n      <td>-0.016056</td>\n      <td>-5.903304e-02</td>\n      <td>-5.838781e-02</td>\n      <td>0.033399</td>\n      <td>-0.027573</td>\n      <td>-0.039002</td>\n      <td>-0.004090</td>\n      <td>0.009273</td>\n      <td>3.542258e-02</td>\n      <td>-0.005827</td>\n      <td>-6.828554e-03</td>\n      <td>-1.483629e-02</td>\n      <td>-0.008388</td>\n      <td>-8.842116e-03</td>\n      <td>-1.729245e-02</td>\n      <td>1.700677e-02</td>\n      <td>0.014851</td>\n      <td>3.342637e-02</td>\n      <td>9.596864e-04</td>\n      <td>-6.446306e-03</td>\n      <td>0.056834</td>\n      <td>0.011651</td>\n      <td>0.050031</td>\n      <td>4.037649e-03</td>\n      <td>-1.010067e-03</td>\n      <td>-1.558075e-02</td>\n      <td>0.022612</td>\n      <td>0.023640</td>\n      <td>-2.802362e-03</td>\n      <td>...</td>\n      <td>5.153728e-03</td>\n      <td>-0.014396</td>\n      <td>-1.012132e-03</td>\n      <td>3.299309e-02</td>\n      <td>-2.610603e-02</td>\n      <td>-2.594862e-02</td>\n      <td>0.060230</td>\n      <td>-2.015518e-23</td>\n      <td>-2.099000e-02</td>\n      <td>-0.078191</td>\n      <td>0.049113</td>\n      <td>-2.630610e-02</td>\n      <td>-3.076547e-02</td>\n      <td>-8.766061e-03</td>\n      <td>4.294412e-03</td>\n      <td>-0.016293</td>\n      <td>-4.037277e-02</td>\n      <td>0.007293</td>\n      <td>-8.866109e-03</td>\n      <td>0.087103</td>\n      <td>-1.826945e-02</td>\n      <td>-1.676408e-02</td>\n      <td>0.057924</td>\n      <td>-0.007381</td>\n      <td>9.839663e-03</td>\n      <td>-0.001616</td>\n      <td>0.044671</td>\n      <td>-1.585653e-02</td>\n      <td>0.014862</td>\n      <td>0.023748</td>\n      <td>-0.037687</td>\n      <td>0.063122</td>\n      <td>0.027640</td>\n      <td>-1.378212e-02</td>\n      <td>4.003747e-02</td>\n      <td>0.020590</td>\n      <td>0.014628</td>\n      <td>-0.037569</td>\n      <td>-1.788358e-02</td>\n      <td>-0.000995</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.021847</td>\n      <td>2.179571e-02</td>\n      <td>-3.153892e-02</td>\n      <td>-0.029620</td>\n      <td>1.000000e+00</td>\n      <td>0.009111</td>\n      <td>1.011715e-02</td>\n      <td>-1.228356e-02</td>\n      <td>-1.950809e-24</td>\n      <td>-3.316512e-02</td>\n      <td>-0.013396</td>\n      <td>0.066440</td>\n      <td>1.171895e-02</td>\n      <td>-3.801558e-02</td>\n      <td>-0.015300</td>\n      <td>-0.033442</td>\n      <td>0.052430</td>\n      <td>0.021218</td>\n      <td>-0.014108</td>\n      <td>4.408585e-02</td>\n      <td>-0.004803</td>\n      <td>-2.253953e-02</td>\n      <td>-1.034427e-02</td>\n      <td>0.003476</td>\n      <td>2.146473e-02</td>\n      <td>1.042304e-01</td>\n      <td>8.737227e-03</td>\n      <td>0.024003</td>\n      <td>-2.545623e-02</td>\n      <td>3.455710e-02</td>\n      <td>2.994713e-02</td>\n      <td>0.065724</td>\n      <td>0.057004</td>\n      <td>0.040536</td>\n      <td>3.012556e-01</td>\n      <td>-1.759305e-02</td>\n      <td>1.350826e-01</td>\n      <td>0.160828</td>\n      <td>-0.024023</td>\n      <td>1.539057e-02</td>\n      <td>...</td>\n      <td>-1.884562e-02</td>\n      <td>0.034142</td>\n      <td>3.135125e-02</td>\n      <td>3.843883e-02</td>\n      <td>5.390044e-02</td>\n      <td>-2.857975e-24</td>\n      <td>0.102826</td>\n      <td>2.279426e-24</td>\n      <td>3.943181e-03</td>\n      <td>0.005858</td>\n      <td>0.023493</td>\n      <td>-1.455653e-24</td>\n      <td>-2.190478e-02</td>\n      <td>1.170911e-01</td>\n      <td>3.089337e-02</td>\n      <td>-0.045611</td>\n      <td>9.256750e-02</td>\n      <td>0.121282</td>\n      <td>1.027808e-01</td>\n      <td>0.050302</td>\n      <td>-1.363111e-24</td>\n      <td>-5.476072e-02</td>\n      <td>0.041755</td>\n      <td>0.007496</td>\n      <td>-9.246321e-03</td>\n      <td>0.020641</td>\n      <td>0.030517</td>\n      <td>-1.082644e-24</td>\n      <td>0.012748</td>\n      <td>0.021435</td>\n      <td>0.015964</td>\n      <td>0.012427</td>\n      <td>0.027076</td>\n      <td>1.246135e-02</td>\n      <td>-3.627206e-02</td>\n      <td>0.026319</td>\n      <td>0.031896</td>\n      <td>-0.001751</td>\n      <td>9.382892e-02</td>\n      <td>-0.000278</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-0.045497</td>\n      <td>-2.105129e-02</td>\n      <td>4.799946e-03</td>\n      <td>0.013956</td>\n      <td>9.111281e-03</td>\n      <td>1.000000</td>\n      <td>4.708608e-03</td>\n      <td>-7.588789e-02</td>\n      <td>-2.586776e-05</td>\n      <td>2.366073e-02</td>\n      <td>0.097799</td>\n      <td>-0.008447</td>\n      <td>4.426741e-03</td>\n      <td>1.005712e-01</td>\n      <td>-0.001260</td>\n      <td>0.002296</td>\n      <td>0.013779</td>\n      <td>-0.007323</td>\n      <td>0.062994</td>\n      <td>2.245210e-02</td>\n      <td>0.021996</td>\n      <td>2.822900e-02</td>\n      <td>-1.216774e-02</td>\n      <td>0.015580</td>\n      <td>1.073049e-02</td>\n      <td>6.021898e-04</td>\n      <td>4.057156e-02</td>\n      <td>-0.010645</td>\n      <td>4.087116e-02</td>\n      <td>2.939784e-02</td>\n      <td>3.063452e-02</td>\n      <td>-0.058358</td>\n      <td>0.009342</td>\n      <td>-0.010863</td>\n      <td>-4.332423e-02</td>\n      <td>9.676205e-03</td>\n      <td>-2.562061e-02</td>\n      <td>0.045616</td>\n      <td>-0.028550</td>\n      <td>7.860231e-02</td>\n      <td>...</td>\n      <td>-5.937711e-03</td>\n      <td>-0.005767</td>\n      <td>2.440162e-02</td>\n      <td>1.420918e-01</td>\n      <td>-2.547872e-02</td>\n      <td>1.638844e-02</td>\n      <td>0.044402</td>\n      <td>6.506565e-04</td>\n      <td>8.517920e-02</td>\n      <td>-0.001424</td>\n      <td>0.018318</td>\n      <td>-1.813068e-04</td>\n      <td>3.250451e-03</td>\n      <td>2.187986e-01</td>\n      <td>-2.260705e-03</td>\n      <td>0.003033</td>\n      <td>6.237638e-03</td>\n      <td>0.020798</td>\n      <td>1.074670e-01</td>\n      <td>0.012956</td>\n      <td>2.396337e-02</td>\n      <td>1.448341e-01</td>\n      <td>-0.017848</td>\n      <td>0.052900</td>\n      <td>-5.643977e-02</td>\n      <td>0.013312</td>\n      <td>-0.029519</td>\n      <td>-1.478338e-02</td>\n      <td>0.011388</td>\n      <td>0.016769</td>\n      <td>-0.015571</td>\n      <td>-0.002229</td>\n      <td>-0.014805</td>\n      <td>-3.356397e-02</td>\n      <td>1.681540e-02</td>\n      <td>-0.009137</td>\n      <td>0.045501</td>\n      <td>0.021727</td>\n      <td>5.301727e-02</td>\n      <td>0.009603</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-0.006200</td>\n      <td>-1.111357e-02</td>\n      <td>3.308497e-25</td>\n      <td>0.058091</td>\n      <td>1.011715e-02</td>\n      <td>0.004709</td>\n      <td>1.000000e+00</td>\n      <td>3.109777e-02</td>\n      <td>6.434956e-02</td>\n      <td>-2.384936e-02</td>\n      <td>0.034898</td>\n      <td>0.003379</td>\n      <td>-2.016739e-02</td>\n      <td>2.198328e-02</td>\n      <td>0.028049</td>\n      <td>-0.033002</td>\n      <td>0.098165</td>\n      <td>0.046704</td>\n      <td>0.033054</td>\n      <td>6.200437e-02</td>\n      <td>0.024334</td>\n      <td>1.430274e-02</td>\n      <td>-1.071610e-02</td>\n      <td>0.004207</td>\n      <td>2.095348e-02</td>\n      <td>2.388479e-02</td>\n      <td>2.546725e-02</td>\n      <td>0.012089</td>\n      <td>-2.459195e-02</td>\n      <td>2.736640e-03</td>\n      <td>3.520073e-02</td>\n      <td>0.032071</td>\n      <td>0.041220</td>\n      <td>0.060916</td>\n      <td>2.965240e-02</td>\n      <td>-1.803481e-02</td>\n      <td>5.666505e-02</td>\n      <td>0.016211</td>\n      <td>0.048490</td>\n      <td>1.848241e-02</td>\n      <td>...</td>\n      <td>2.610948e-02</td>\n      <td>-0.010793</td>\n      <td>9.669717e-02</td>\n      <td>2.647875e-02</td>\n      <td>-2.248156e-23</td>\n      <td>3.174083e-23</td>\n      <td>0.080538</td>\n      <td>-2.484556e-02</td>\n      <td>2.265528e-02</td>\n      <td>0.011523</td>\n      <td>0.031527</td>\n      <td>-1.725705e-02</td>\n      <td>-2.470784e-03</td>\n      <td>2.742675e-02</td>\n      <td>-2.765667e-03</td>\n      <td>0.013816</td>\n      <td>1.320107e-02</td>\n      <td>0.040429</td>\n      <td>3.480403e-02</td>\n      <td>0.129029</td>\n      <td>2.155651e-02</td>\n      <td>5.612566e-02</td>\n      <td>0.065634</td>\n      <td>0.027874</td>\n      <td>1.775516e-02</td>\n      <td>0.107557</td>\n      <td>0.020689</td>\n      <td>-5.784777e-02</td>\n      <td>0.056930</td>\n      <td>0.073639</td>\n      <td>-0.037080</td>\n      <td>0.050746</td>\n      <td>0.036846</td>\n      <td>2.024890e-02</td>\n      <td>1.086215e-01</td>\n      <td>0.028326</td>\n      <td>0.030981</td>\n      <td>0.028414</td>\n      <td>8.754391e-03</td>\n      <td>0.068430</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.047013</td>\n      <td>-4.808546e-02</td>\n      <td>-3.247105e-02</td>\n      <td>0.002065</td>\n      <td>-1.228356e-02</td>\n      <td>-0.075888</td>\n      <td>3.109777e-02</td>\n      <td>1.000000e+00</td>\n      <td>3.855042e-23</td>\n      <td>-1.339030e-02</td>\n      <td>0.043053</td>\n      <td>-0.039177</td>\n      <td>2.662598e-02</td>\n      <td>4.489223e-02</td>\n      <td>0.036418</td>\n      <td>0.016597</td>\n      <td>0.051869</td>\n      <td>0.001361</td>\n      <td>0.013955</td>\n      <td>2.291039e-02</td>\n      <td>-0.004714</td>\n      <td>3.710931e-02</td>\n      <td>7.663906e-04</td>\n      <td>0.045502</td>\n      <td>-5.607238e-03</td>\n      <td>4.881935e-02</td>\n      <td>3.264006e-02</td>\n      <td>0.011474</td>\n      <td>-2.334049e-02</td>\n      <td>1.073976e-02</td>\n      <td>5.199938e-03</td>\n      <td>0.088559</td>\n      <td>-0.004337</td>\n      <td>-0.048724</td>\n      <td>1.609713e-01</td>\n      <td>3.881359e-02</td>\n      <td>2.604117e-02</td>\n      <td>0.161386</td>\n      <td>0.035069</td>\n      <td>1.256108e-01</td>\n      <td>...</td>\n      <td>4.157701e-02</td>\n      <td>0.041130</td>\n      <td>4.169463e-02</td>\n      <td>3.866203e-02</td>\n      <td>1.656521e-02</td>\n      <td>5.645333e-23</td>\n      <td>0.105387</td>\n      <td>-4.489192e-23</td>\n      <td>-4.226623e-02</td>\n      <td>0.041220</td>\n      <td>0.087046</td>\n      <td>-3.477035e-04</td>\n      <td>-4.627283e-02</td>\n      <td>3.440889e-02</td>\n      <td>9.494453e-03</td>\n      <td>-0.038495</td>\n      <td>2.431043e-04</td>\n      <td>0.034995</td>\n      <td>7.401687e-02</td>\n      <td>0.049414</td>\n      <td>-7.475262e-03</td>\n      <td>2.556147e-02</td>\n      <td>0.052153</td>\n      <td>-0.053317</td>\n      <td>4.759797e-02</td>\n      <td>0.042789</td>\n      <td>0.024004</td>\n      <td>-2.835102e-02</td>\n      <td>0.025501</td>\n      <td>-0.000465</td>\n      <td>0.075826</td>\n      <td>0.125282</td>\n      <td>0.025362</td>\n      <td>1.894854e-01</td>\n      <td>-2.122042e-02</td>\n      <td>0.022277</td>\n      <td>0.048822</td>\n      <td>0.071759</td>\n      <td>7.718048e-02</td>\n      <td>0.017144</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.019510</td>\n      <td>-5.534891e-23</td>\n      <td>5.123183e-25</td>\n      <td>-0.005874</td>\n      <td>-1.950809e-24</td>\n      <td>-0.000026</td>\n      <td>6.434956e-02</td>\n      <td>3.855042e-23</td>\n      <td>1.000000e+00</td>\n      <td>2.120696e-02</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>5.117104e-02</td>\n      <td>3.746926e-23</td>\n      <td>0.037946</td>\n      <td>-0.033648</td>\n      <td>0.065293</td>\n      <td>0.027365</td>\n      <td>0.054993</td>\n      <td>7.411499e-02</td>\n      <td>0.002490</td>\n      <td>9.546852e-03</td>\n      <td>1.062935e-02</td>\n      <td>0.020737</td>\n      <td>6.248413e-02</td>\n      <td>-3.252392e-23</td>\n      <td>5.125162e-02</td>\n      <td>-0.001541</td>\n      <td>7.176594e-03</td>\n      <td>5.086326e-02</td>\n      <td>4.624750e-03</td>\n      <td>0.029242</td>\n      <td>0.002826</td>\n      <td>-0.040818</td>\n      <td>3.371759e-24</td>\n      <td>-1.440941e-02</td>\n      <td>-2.483206e-23</td>\n      <td>0.016707</td>\n      <td>-0.002914</td>\n      <td>9.482811e-03</td>\n      <td>...</td>\n      <td>2.472152e-02</td>\n      <td>-0.002757</td>\n      <td>2.191889e-02</td>\n      <td>-2.059520e-23</td>\n      <td>-3.491079e-23</td>\n      <td>4.927288e-23</td>\n      <td>0.018769</td>\n      <td>-3.918124e-23</td>\n      <td>-2.455708e-23</td>\n      <td>0.029280</td>\n      <td>0.017097</td>\n      <td>-2.159028e-02</td>\n      <td>6.386598e-23</td>\n      <td>4.017738e-23</td>\n      <td>1.414656e-02</td>\n      <td>0.040795</td>\n      <td>-3.367791e-02</td>\n      <td>0.019167</td>\n      <td>-6.910616e-23</td>\n      <td>0.055637</td>\n      <td>7.430191e-03</td>\n      <td>-2.482392e-23</td>\n      <td>0.039219</td>\n      <td>0.000316</td>\n      <td>2.557875e-23</td>\n      <td>-0.008878</td>\n      <td>0.020583</td>\n      <td>1.859309e-23</td>\n      <td>0.029654</td>\n      <td>0.076736</td>\n      <td>0.024267</td>\n      <td>0.001060</td>\n      <td>0.008300</td>\n      <td>6.073345e-03</td>\n      <td>7.340770e-02</td>\n      <td>0.031633</td>\n      <td>-0.012161</td>\n      <td>0.032783</td>\n      <td>5.226205e-23</td>\n      <td>0.051898</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>-0.008754</td>\n      <td>3.011629e-03</td>\n      <td>2.203980e-25</td>\n      <td>0.051590</td>\n      <td>-3.316512e-02</td>\n      <td>0.023661</td>\n      <td>-2.384936e-02</td>\n      <td>-1.339030e-02</td>\n      <td>2.120696e-02</td>\n      <td>1.000000e+00</td>\n      <td>0.005253</td>\n      <td>0.038732</td>\n      <td>-3.918631e-02</td>\n      <td>9.723511e-03</td>\n      <td>-0.002117</td>\n      <td>0.076168</td>\n      <td>-0.035984</td>\n      <td>-0.023972</td>\n      <td>-0.044967</td>\n      <td>1.825082e-02</td>\n      <td>0.056340</td>\n      <td>-2.522298e-03</td>\n      <td>6.355693e-03</td>\n      <td>-0.010024</td>\n      <td>2.781578e-02</td>\n      <td>-4.617847e-02</td>\n      <td>6.324012e-03</td>\n      <td>-0.015406</td>\n      <td>2.254493e-02</td>\n      <td>1.029208e-02</td>\n      <td>1.594969e-02</td>\n      <td>-0.016615</td>\n      <td>-0.033218</td>\n      <td>-0.006938</td>\n      <td>1.457020e-24</td>\n      <td>-1.116863e-02</td>\n      <td>-3.517301e-02</td>\n      <td>0.000167</td>\n      <td>-0.079497</td>\n      <td>-1.813554e-02</td>\n      <td>...</td>\n      <td>2.556816e-24</td>\n      <td>0.000502</td>\n      <td>9.070595e-03</td>\n      <td>4.858752e-02</td>\n      <td>-6.559137e-02</td>\n      <td>2.129170e-23</td>\n      <td>0.009001</td>\n      <td>4.318307e-02</td>\n      <td>2.865106e-03</td>\n      <td>-0.050126</td>\n      <td>0.051287</td>\n      <td>2.337188e-02</td>\n      <td>-5.323612e-02</td>\n      <td>1.730796e-02</td>\n      <td>-6.621725e-02</td>\n      <td>0.026032</td>\n      <td>-3.118961e-02</td>\n      <td>-0.036893</td>\n      <td>-2.379638e-02</td>\n      <td>0.011595</td>\n      <td>-1.576617e-02</td>\n      <td>1.940330e-03</td>\n      <td>0.015499</td>\n      <td>-0.000962</td>\n      <td>1.106440e-23</td>\n      <td>-0.028938</td>\n      <td>-0.017965</td>\n      <td>-3.034223e-02</td>\n      <td>-0.035046</td>\n      <td>-0.015802</td>\n      <td>-0.099928</td>\n      <td>-0.025856</td>\n      <td>-0.034765</td>\n      <td>-3.707362e-02</td>\n      <td>2.778959e-02</td>\n      <td>-0.039946</td>\n      <td>-0.017656</td>\n      <td>-0.052000</td>\n      <td>-4.009050e-02</td>\n      <td>-0.026004</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>-0.056206</td>\n      <td>-2.745410e-03</td>\n      <td>0.000000e+00</td>\n      <td>0.062453</td>\n      <td>-1.339634e-02</td>\n      <td>0.097799</td>\n      <td>3.489821e-02</td>\n      <td>4.305265e-02</td>\n      <td>0.000000e+00</td>\n      <td>5.253392e-03</td>\n      <td>1.000000</td>\n      <td>0.024767</td>\n      <td>1.081655e-02</td>\n      <td>4.966138e-02</td>\n      <td>0.075530</td>\n      <td>-0.028423</td>\n      <td>0.081731</td>\n      <td>0.077022</td>\n      <td>-0.002489</td>\n      <td>0.000000e+00</td>\n      <td>0.080780</td>\n      <td>2.809058e-02</td>\n      <td>8.578823e-03</td>\n      <td>0.098131</td>\n      <td>1.681007e-02</td>\n      <td>1.277367e-01</td>\n      <td>5.149549e-02</td>\n      <td>0.037407</td>\n      <td>9.762630e-02</td>\n      <td>7.134244e-03</td>\n      <td>-6.632511e-02</td>\n      <td>0.006913</td>\n      <td>0.060205</td>\n      <td>-0.018399</td>\n      <td>3.555852e-02</td>\n      <td>3.748229e-03</td>\n      <td>4.172407e-02</td>\n      <td>0.089231</td>\n      <td>0.034169</td>\n      <td>7.595324e-02</td>\n      <td>...</td>\n      <td>0.000000e+00</td>\n      <td>-0.017462</td>\n      <td>5.705235e-02</td>\n      <td>1.102245e-01</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.142537</td>\n      <td>0.000000e+00</td>\n      <td>6.491973e-02</td>\n      <td>0.054250</td>\n      <td>0.009376</td>\n      <td>-8.860304e-04</td>\n      <td>1.690964e-02</td>\n      <td>4.609266e-02</td>\n      <td>0.000000e+00</td>\n      <td>-0.017834</td>\n      <td>9.062744e-03</td>\n      <td>0.165058</td>\n      <td>-5.096228e-03</td>\n      <td>0.059528</td>\n      <td>-1.419366e-03</td>\n      <td>1.969149e-01</td>\n      <td>0.034674</td>\n      <td>-0.006758</td>\n      <td>-1.624638e-03</td>\n      <td>0.051866</td>\n      <td>0.079575</td>\n      <td>-7.224509e-02</td>\n      <td>0.050224</td>\n      <td>-0.004012</td>\n      <td>-0.026649</td>\n      <td>0.086690</td>\n      <td>-0.026121</td>\n      <td>-1.842626e-03</td>\n      <td>1.539191e-02</td>\n      <td>0.027605</td>\n      <td>0.079421</td>\n      <td>0.048957</td>\n      <td>4.766628e-02</td>\n      <td>0.026876</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>0.013266</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>-0.016056</td>\n      <td>6.643999e-02</td>\n      <td>-0.008447</td>\n      <td>3.379416e-03</td>\n      <td>-3.917655e-02</td>\n      <td>0.000000e+00</td>\n      <td>3.873166e-02</td>\n      <td>0.024767</td>\n      <td>1.000000</td>\n      <td>9.908460e-03</td>\n      <td>5.433431e-03</td>\n      <td>-0.004878</td>\n      <td>0.000000</td>\n      <td>-0.006721</td>\n      <td>0.020636</td>\n      <td>-0.034932</td>\n      <td>2.183363e-02</td>\n      <td>-0.002803</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>-0.024049</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>2.596787e-03</td>\n      <td>-0.006711</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>2.023695e-01</td>\n      <td>0.028399</td>\n      <td>-0.010371</td>\n      <td>0.016223</td>\n      <td>2.760175e-02</td>\n      <td>-2.638591e-03</td>\n      <td>0.000000e+00</td>\n      <td>-0.031000</td>\n      <td>0.000000</td>\n      <td>-5.065962e-02</td>\n      <td>...</td>\n      <td>0.000000e+00</td>\n      <td>0.053907</td>\n      <td>-2.433909e-02</td>\n      <td>0.000000e+00</td>\n      <td>-7.030582e-03</td>\n      <td>0.000000e+00</td>\n      <td>0.058231</td>\n      <td>2.678796e-02</td>\n      <td>1.016350e-03</td>\n      <td>0.011862</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>-4.149089e-02</td>\n      <td>3.078674e-02</td>\n      <td>2.560066e-02</td>\n      <td>-0.017917</td>\n      <td>3.484802e-02</td>\n      <td>0.000000</td>\n      <td>9.050942e-04</td>\n      <td>0.001730</td>\n      <td>-3.314248e-04</td>\n      <td>1.671754e-02</td>\n      <td>0.050658</td>\n      <td>-0.005465</td>\n      <td>0.000000e+00</td>\n      <td>0.008394</td>\n      <td>-0.008951</td>\n      <td>0.000000e+00</td>\n      <td>0.000202</td>\n      <td>0.008060</td>\n      <td>-0.014097</td>\n      <td>-0.025518</td>\n      <td>-0.020093</td>\n      <td>-2.571490e-02</td>\n      <td>2.340212e-02</td>\n      <td>0.003277</td>\n      <td>0.021000</td>\n      <td>-0.007228</td>\n      <td>0.000000e+00</td>\n      <td>-0.009941</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>0.059383</td>\n      <td>3.047559e-03</td>\n      <td>2.345529e-25</td>\n      <td>-0.059033</td>\n      <td>1.171895e-02</td>\n      <td>0.004427</td>\n      <td>-2.016739e-02</td>\n      <td>2.662598e-02</td>\n      <td>5.117104e-02</td>\n      <td>-3.918631e-02</td>\n      <td>0.010817</td>\n      <td>0.009908</td>\n      <td>1.000000e+00</td>\n      <td>6.401056e-02</td>\n      <td>0.049583</td>\n      <td>-0.064580</td>\n      <td>0.042208</td>\n      <td>0.061773</td>\n      <td>0.051387</td>\n      <td>4.855365e-03</td>\n      <td>0.007976</td>\n      <td>3.339850e-02</td>\n      <td>-5.550042e-03</td>\n      <td>0.061479</td>\n      <td>7.703283e-02</td>\n      <td>6.847840e-02</td>\n      <td>5.244040e-02</td>\n      <td>0.023376</td>\n      <td>4.979923e-02</td>\n      <td>4.180411e-02</td>\n      <td>2.850784e-03</td>\n      <td>0.037148</td>\n      <td>0.030698</td>\n      <td>-0.001307</td>\n      <td>1.544964e-24</td>\n      <td>1.930180e-02</td>\n      <td>-1.135614e-23</td>\n      <td>0.015773</td>\n      <td>0.011404</td>\n      <td>-1.493917e-23</td>\n      <td>...</td>\n      <td>2.707902e-24</td>\n      <td>0.046208</td>\n      <td>-3.771522e-02</td>\n      <td>6.019622e-02</td>\n      <td>3.642672e-02</td>\n      <td>2.254518e-23</td>\n      <td>0.016306</td>\n      <td>-1.792872e-23</td>\n      <td>1.624154e-02</td>\n      <td>0.075924</td>\n      <td>-0.073772</td>\n      <td>4.917437e-02</td>\n      <td>-2.709533e-03</td>\n      <td>3.564636e-02</td>\n      <td>3.695681e-02</td>\n      <td>0.062904</td>\n      <td>1.020964e-02</td>\n      <td>-0.007943</td>\n      <td>5.950003e-02</td>\n      <td>0.072151</td>\n      <td>1.335447e-01</td>\n      <td>7.263767e-03</td>\n      <td>0.045345</td>\n      <td>0.017517</td>\n      <td>1.171442e-23</td>\n      <td>0.024595</td>\n      <td>0.025136</td>\n      <td>8.512745e-24</td>\n      <td>0.052465</td>\n      <td>0.019654</td>\n      <td>-0.017364</td>\n      <td>0.050369</td>\n      <td>0.068137</td>\n      <td>9.315813e-24</td>\n      <td>-7.799120e-04</td>\n      <td>0.007966</td>\n      <td>-0.014839</td>\n      <td>0.065678</td>\n      <td>2.391257e-23</td>\n      <td>0.077008</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>0.021815</td>\n      <td>1.251784e-02</td>\n      <td>8.453024e-03</td>\n      <td>-0.058388</td>\n      <td>-3.801558e-02</td>\n      <td>0.100571</td>\n      <td>2.198328e-02</td>\n      <td>4.489223e-02</td>\n      <td>3.746926e-23</td>\n      <td>9.723511e-03</td>\n      <td>0.049661</td>\n      <td>0.005433</td>\n      <td>6.401056e-02</td>\n      <td>1.000000e+00</td>\n      <td>-0.004618</td>\n      <td>-0.022612</td>\n      <td>-0.020053</td>\n      <td>0.133321</td>\n      <td>-0.004242</td>\n      <td>1.838857e-03</td>\n      <td>0.016732</td>\n      <td>4.289779e-03</td>\n      <td>-6.544533e-04</td>\n      <td>0.013135</td>\n      <td>1.840944e-02</td>\n      <td>-1.004313e-02</td>\n      <td>1.221204e-03</td>\n      <td>0.010579</td>\n      <td>5.630809e-03</td>\n      <td>1.429550e-02</td>\n      <td>-5.797089e-02</td>\n      <td>-0.022847</td>\n      <td>0.065045</td>\n      <td>-0.056706</td>\n      <td>-2.394205e-02</td>\n      <td>-6.926332e-03</td>\n      <td>7.805658e-02</td>\n      <td>-0.006578</td>\n      <td>-0.027110</td>\n      <td>6.638159e-02</td>\n      <td>...</td>\n      <td>6.600658e-24</td>\n      <td>-0.005355</td>\n      <td>5.723985e-02</td>\n      <td>1.574926e-01</td>\n      <td>-1.444633e-02</td>\n      <td>5.488635e-23</td>\n      <td>0.047493</td>\n      <td>-4.367945e-23</td>\n      <td>2.414127e-02</td>\n      <td>0.058913</td>\n      <td>-0.020008</td>\n      <td>-4.628220e-04</td>\n      <td>-2.392499e-02</td>\n      <td>2.705460e-01</td>\n      <td>-8.280003e-03</td>\n      <td>0.013421</td>\n      <td>4.328977e-02</td>\n      <td>0.160999</td>\n      <td>9.556360e-02</td>\n      <td>0.070366</td>\n      <td>7.370511e-03</td>\n      <td>3.454851e-02</td>\n      <td>0.031828</td>\n      <td>0.002731</td>\n      <td>5.021591e-02</td>\n      <td>0.025321</td>\n      <td>0.012978</td>\n      <td>-3.773755e-02</td>\n      <td>-0.004617</td>\n      <td>0.027157</td>\n      <td>-0.035552</td>\n      <td>0.010431</td>\n      <td>0.015157</td>\n      <td>2.249223e-01</td>\n      <td>-1.929689e-02</td>\n      <td>0.041250</td>\n      <td>0.030564</td>\n      <td>0.039392</td>\n      <td>-1.007497e-02</td>\n      <td>0.001992</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>0.037209</td>\n      <td>-3.328039e-02</td>\n      <td>-4.604603e-02</td>\n      <td>0.033399</td>\n      <td>-1.529957e-02</td>\n      <td>-0.001260</td>\n      <td>2.804912e-02</td>\n      <td>3.641813e-02</td>\n      <td>3.794639e-02</td>\n      <td>-2.117430e-03</td>\n      <td>0.075530</td>\n      <td>-0.004878</td>\n      <td>4.958310e-02</td>\n      <td>-4.618493e-03</td>\n      <td>1.000000</td>\n      <td>-0.106568</td>\n      <td>0.172769</td>\n      <td>0.042943</td>\n      <td>0.027212</td>\n      <td>3.938889e-03</td>\n      <td>0.060501</td>\n      <td>3.377155e-02</td>\n      <td>-1.319429e-02</td>\n      <td>0.044688</td>\n      <td>7.212561e-02</td>\n      <td>2.467657e-03</td>\n      <td>7.032981e-02</td>\n      <td>0.028175</td>\n      <td>7.258790e-03</td>\n      <td>-1.177062e-01</td>\n      <td>-2.779507e-02</td>\n      <td>0.083450</td>\n      <td>0.052840</td>\n      <td>0.012236</td>\n      <td>-2.338410e-24</td>\n      <td>-7.643861e-03</td>\n      <td>5.547650e-04</td>\n      <td>0.051576</td>\n      <td>0.102494</td>\n      <td>1.665834e-02</td>\n      <td>...</td>\n      <td>1.680298e-02</td>\n      <td>0.040406</td>\n      <td>-1.266275e-03</td>\n      <td>1.186790e-02</td>\n      <td>1.570612e-02</td>\n      <td>3.812107e-02</td>\n      <td>0.077591</td>\n      <td>2.720153e-23</td>\n      <td>5.792147e-02</td>\n      <td>0.016400</td>\n      <td>0.035676</td>\n      <td>-5.135754e-02</td>\n      <td>1.938811e-02</td>\n      <td>-4.816075e-03</td>\n      <td>2.291525e-02</td>\n      <td>-0.014343</td>\n      <td>1.201254e-02</td>\n      <td>0.040189</td>\n      <td>6.427439e-02</td>\n      <td>0.076337</td>\n      <td>9.550520e-02</td>\n      <td>4.535211e-02</td>\n      <td>0.093685</td>\n      <td>0.011749</td>\n      <td>-2.175555e-04</td>\n      <td>-0.023045</td>\n      <td>0.049969</td>\n      <td>9.570308e-02</td>\n      <td>0.060774</td>\n      <td>0.020008</td>\n      <td>0.015158</td>\n      <td>0.082104</td>\n      <td>-0.001537</td>\n      <td>3.949312e-02</td>\n      <td>3.120836e-02</td>\n      <td>0.021381</td>\n      <td>-0.005414</td>\n      <td>0.086489</td>\n      <td>7.066206e-02</td>\n      <td>-0.014099</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>0.009664</td>\n      <td>-1.477139e-02</td>\n      <td>-5.141746e-03</td>\n      <td>-0.027573</td>\n      <td>-3.344150e-02</td>\n      <td>0.002296</td>\n      <td>-3.300159e-02</td>\n      <td>1.659711e-02</td>\n      <td>-3.364763e-02</td>\n      <td>7.616752e-02</td>\n      <td>-0.028423</td>\n      <td>0.000000</td>\n      <td>-6.458006e-02</td>\n      <td>-2.261212e-02</td>\n      <td>-0.106568</td>\n      <td>1.000000</td>\n      <td>-0.180045</td>\n      <td>0.007288</td>\n      <td>-0.018142</td>\n      <td>7.911325e-03</td>\n      <td>-0.000458</td>\n      <td>2.946975e-02</td>\n      <td>1.186334e-01</td>\n      <td>-0.013651</td>\n      <td>-3.645488e-03</td>\n      <td>-8.039822e-02</td>\n      <td>-3.269576e-02</td>\n      <td>-0.056351</td>\n      <td>-2.247010e-02</td>\n      <td>-1.405656e-02</td>\n      <td>-6.582190e-03</td>\n      <td>-0.040725</td>\n      <td>-0.020624</td>\n      <td>0.026309</td>\n      <td>1.095822e-02</td>\n      <td>-2.208487e-02</td>\n      <td>-5.644371e-02</td>\n      <td>0.036597</td>\n      <td>-0.025768</td>\n      <td>7.047407e-03</td>\n      <td>...</td>\n      <td>-2.189224e-02</td>\n      <td>0.002999</td>\n      <td>-2.134153e-02</td>\n      <td>-4.123141e-04</td>\n      <td>-3.902600e-02</td>\n      <td>7.833440e-02</td>\n      <td>-0.045290</td>\n      <td>-6.424206e-23</td>\n      <td>6.300156e-04</td>\n      <td>-0.015644</td>\n      <td>-0.028979</td>\n      <td>-2.179020e-02</td>\n      <td>-2.875483e-03</td>\n      <td>6.674191e-03</td>\n      <td>-8.107503e-02</td>\n      <td>0.016326</td>\n      <td>-6.345261e-03</td>\n      <td>0.021010</td>\n      <td>-1.023157e-02</td>\n      <td>-0.020190</td>\n      <td>-1.494062e-02</td>\n      <td>-7.744425e-03</td>\n      <td>-0.124402</td>\n      <td>-0.027261</td>\n      <td>6.508731e-02</td>\n      <td>0.026997</td>\n      <td>-0.008551</td>\n      <td>-3.104987e-02</td>\n      <td>-0.008643</td>\n      <td>0.035247</td>\n      <td>-0.019244</td>\n      <td>-0.038124</td>\n      <td>0.015177</td>\n      <td>-4.791749e-02</td>\n      <td>-1.845184e-02</td>\n      <td>-0.006709</td>\n      <td>0.013760</td>\n      <td>-0.041041</td>\n      <td>-3.346090e-02</td>\n      <td>0.008550</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>0.037997</td>\n      <td>-1.262775e-03</td>\n      <td>-4.689983e-03</td>\n      <td>-0.039002</td>\n      <td>5.243000e-02</td>\n      <td>0.013779</td>\n      <td>9.816514e-02</td>\n      <td>5.186852e-02</td>\n      <td>6.529290e-02</td>\n      <td>-3.598441e-02</td>\n      <td>0.081731</td>\n      <td>-0.006721</td>\n      <td>4.220829e-02</td>\n      <td>-2.005272e-02</td>\n      <td>0.172769</td>\n      <td>-0.180045</td>\n      <td>1.000000</td>\n      <td>0.092045</td>\n      <td>0.009553</td>\n      <td>2.179922e-02</td>\n      <td>0.041013</td>\n      <td>1.599975e-03</td>\n      <td>-9.365620e-03</td>\n      <td>0.121765</td>\n      <td>6.871981e-02</td>\n      <td>5.975848e-02</td>\n      <td>2.984041e-02</td>\n      <td>0.067820</td>\n      <td>3.545841e-02</td>\n      <td>1.902001e-02</td>\n      <td>2.438914e-03</td>\n      <td>0.079231</td>\n      <td>0.081380</td>\n      <td>-0.028332</td>\n      <td>-6.515456e-03</td>\n      <td>2.170845e-02</td>\n      <td>-1.330925e-02</td>\n      <td>0.049901</td>\n      <td>0.054898</td>\n      <td>3.526560e-02</td>\n      <td>...</td>\n      <td>3.740269e-03</td>\n      <td>0.064748</td>\n      <td>7.750660e-02</td>\n      <td>6.538285e-03</td>\n      <td>4.654842e-02</td>\n      <td>4.490818e-23</td>\n      <td>0.147353</td>\n      <td>-3.569463e-23</td>\n      <td>3.943276e-02</td>\n      <td>0.067499</td>\n      <td>0.023234</td>\n      <td>2.852985e-02</td>\n      <td>3.369159e-02</td>\n      <td>1.421192e-02</td>\n      <td>5.996536e-02</td>\n      <td>0.004224</td>\n      <td>3.381510e-02</td>\n      <td>0.096613</td>\n      <td>8.787710e-02</td>\n      <td>0.089391</td>\n      <td>9.468887e-02</td>\n      <td>5.545019e-02</td>\n      <td>0.169074</td>\n      <td>0.029361</td>\n      <td>1.455851e-02</td>\n      <td>0.042188</td>\n      <td>0.083246</td>\n      <td>2.309930e-02</td>\n      <td>0.047476</td>\n      <td>0.076654</td>\n      <td>0.084115</td>\n      <td>0.092969</td>\n      <td>0.034110</td>\n      <td>4.543963e-02</td>\n      <td>1.447999e-02</td>\n      <td>0.049376</td>\n      <td>0.009034</td>\n      <td>0.082083</td>\n      <td>1.243348e-01</td>\n      <td>0.042927</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>0.043789</td>\n      <td>-4.782450e-02</td>\n      <td>-1.232161e-02</td>\n      <td>-0.004090</td>\n      <td>2.121822e-02</td>\n      <td>-0.007323</td>\n      <td>4.670432e-02</td>\n      <td>1.361051e-03</td>\n      <td>2.736544e-02</td>\n      <td>-2.397213e-02</td>\n      <td>0.077022</td>\n      <td>0.020636</td>\n      <td>6.177257e-02</td>\n      <td>1.333215e-01</td>\n      <td>0.042943</td>\n      <td>0.007288</td>\n      <td>0.092045</td>\n      <td>1.000000</td>\n      <td>0.068502</td>\n      <td>2.185777e-02</td>\n      <td>-0.025686</td>\n      <td>1.854674e-02</td>\n      <td>3.634008e-02</td>\n      <td>0.086483</td>\n      <td>6.773871e-02</td>\n      <td>7.438727e-02</td>\n      <td>3.005975e-03</td>\n      <td>0.086762</td>\n      <td>4.123038e-02</td>\n      <td>2.623566e-02</td>\n      <td>-4.614805e-02</td>\n      <td>0.068130</td>\n      <td>0.058563</td>\n      <td>0.043674</td>\n      <td>5.809155e-02</td>\n      <td>2.724353e-02</td>\n      <td>1.811437e-02</td>\n      <td>0.096644</td>\n      <td>0.041155</td>\n      <td>7.809718e-02</td>\n      <td>...</td>\n      <td>-7.937052e-03</td>\n      <td>0.061349</td>\n      <td>5.954977e-02</td>\n      <td>9.471986e-03</td>\n      <td>2.512653e-02</td>\n      <td>1.237764e-02</td>\n      <td>0.070391</td>\n      <td>1.465725e-02</td>\n      <td>1.544420e-02</td>\n      <td>0.103530</td>\n      <td>0.001483</td>\n      <td>1.092245e-02</td>\n      <td>3.144837e-02</td>\n      <td>5.315986e-02</td>\n      <td>2.326581e-02</td>\n      <td>-0.027941</td>\n      <td>2.420658e-02</td>\n      <td>0.140603</td>\n      <td>6.151294e-02</td>\n      <td>0.218372</td>\n      <td>2.385479e-02</td>\n      <td>6.089845e-03</td>\n      <td>0.114465</td>\n      <td>-0.009535</td>\n      <td>1.868873e-02</td>\n      <td>0.066303</td>\n      <td>0.076056</td>\n      <td>-3.768974e-02</td>\n      <td>0.126159</td>\n      <td>0.083236</td>\n      <td>0.082769</td>\n      <td>0.094587</td>\n      <td>0.070622</td>\n      <td>2.177133e-02</td>\n      <td>1.131642e-02</td>\n      <td>0.141273</td>\n      <td>0.036393</td>\n      <td>0.100433</td>\n      <td>4.882887e-02</td>\n      <td>0.100803</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>0.085249</td>\n      <td>-1.060765e-04</td>\n      <td>-9.560404e-03</td>\n      <td>0.009273</td>\n      <td>-1.410827e-02</td>\n      <td>0.062994</td>\n      <td>3.305397e-02</td>\n      <td>1.395528e-02</td>\n      <td>5.499271e-02</td>\n      <td>-4.496736e-02</td>\n      <td>-0.002489</td>\n      <td>-0.034932</td>\n      <td>5.138716e-02</td>\n      <td>-4.241967e-03</td>\n      <td>0.027212</td>\n      <td>-0.018142</td>\n      <td>0.009553</td>\n      <td>0.068502</td>\n      <td>1.000000</td>\n      <td>8.463124e-02</td>\n      <td>-0.032919</td>\n      <td>1.600990e-02</td>\n      <td>1.408106e-02</td>\n      <td>0.012262</td>\n      <td>3.379364e-02</td>\n      <td>5.893118e-02</td>\n      <td>5.795097e-02</td>\n      <td>0.030735</td>\n      <td>2.247469e-02</td>\n      <td>-3.686034e-02</td>\n      <td>5.837885e-02</td>\n      <td>0.076348</td>\n      <td>0.016183</td>\n      <td>0.005922</td>\n      <td>7.336364e-03</td>\n      <td>4.526183e-02</td>\n      <td>-4.692400e-02</td>\n      <td>0.050092</td>\n      <td>0.007071</td>\n      <td>6.985953e-02</td>\n      <td>...</td>\n      <td>4.088949e-02</td>\n      <td>0.001578</td>\n      <td>7.436012e-02</td>\n      <td>1.803281e-03</td>\n      <td>1.329848e-02</td>\n      <td>7.193994e-02</td>\n      <td>0.021901</td>\n      <td>1.345468e-03</td>\n      <td>2.160407e-02</td>\n      <td>0.028927</td>\n      <td>-0.011221</td>\n      <td>9.961552e-03</td>\n      <td>-1.974745e-03</td>\n      <td>3.655364e-03</td>\n      <td>1.254163e-02</td>\n      <td>0.020199</td>\n      <td>-2.400699e-03</td>\n      <td>-0.002565</td>\n      <td>2.625051e-03</td>\n      <td>0.164386</td>\n      <td>4.988356e-02</td>\n      <td>4.585336e-02</td>\n      <td>0.016781</td>\n      <td>0.007658</td>\n      <td>1.576667e-02</td>\n      <td>0.091810</td>\n      <td>0.115589</td>\n      <td>-1.693001e-02</td>\n      <td>0.157420</td>\n      <td>0.129392</td>\n      <td>0.017747</td>\n      <td>0.043580</td>\n      <td>0.026347</td>\n      <td>1.770198e-02</td>\n      <td>5.264732e-02</td>\n      <td>0.047589</td>\n      <td>-0.015030</td>\n      <td>0.069046</td>\n      <td>-8.841222e-03</td>\n      <td>0.081843</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>0.066990</td>\n      <td>6.099867e-04</td>\n      <td>-2.114474e-02</td>\n      <td>0.035423</td>\n      <td>4.408585e-02</td>\n      <td>0.022452</td>\n      <td>6.200437e-02</td>\n      <td>2.291039e-02</td>\n      <td>7.411499e-02</td>\n      <td>1.825082e-02</td>\n      <td>0.000000</td>\n      <td>0.021834</td>\n      <td>4.855365e-03</td>\n      <td>1.838857e-03</td>\n      <td>0.003939</td>\n      <td>0.007911</td>\n      <td>0.021799</td>\n      <td>0.021858</td>\n      <td>0.084631</td>\n      <td>1.000000e+00</td>\n      <td>-0.009114</td>\n      <td>-1.205731e-02</td>\n      <td>-4.594695e-03</td>\n      <td>-0.003634</td>\n      <td>2.107205e-02</td>\n      <td>-1.927456e-03</td>\n      <td>4.381796e-02</td>\n      <td>0.015035</td>\n      <td>6.071639e-04</td>\n      <td>1.296937e-02</td>\n      <td>2.047032e-02</td>\n      <td>0.007467</td>\n      <td>0.017621</td>\n      <td>-0.048652</td>\n      <td>-2.006085e-02</td>\n      <td>-1.939652e-02</td>\n      <td>-2.461102e-02</td>\n      <td>0.000133</td>\n      <td>-0.101139</td>\n      <td>-1.139911e-02</td>\n      <td>...</td>\n      <td>1.137581e-03</td>\n      <td>-0.011617</td>\n      <td>5.316699e-02</td>\n      <td>1.945351e-02</td>\n      <td>1.407921e-03</td>\n      <td>-1.284521e-03</td>\n      <td>-0.010684</td>\n      <td>-4.485248e-24</td>\n      <td>-7.615445e-03</td>\n      <td>-0.017026</td>\n      <td>0.018563</td>\n      <td>-1.648851e-02</td>\n      <td>6.743431e-03</td>\n      <td>1.498314e-02</td>\n      <td>3.703127e-03</td>\n      <td>0.027317</td>\n      <td>-1.140566e-02</td>\n      <td>-0.002464</td>\n      <td>-3.286370e-03</td>\n      <td>0.145024</td>\n      <td>3.353346e-02</td>\n      <td>4.252218e-03</td>\n      <td>0.004710</td>\n      <td>0.005660</td>\n      <td>2.822777e-02</td>\n      <td>0.068068</td>\n      <td>0.031722</td>\n      <td>-2.007013e-02</td>\n      <td>0.060271</td>\n      <td>0.089546</td>\n      <td>-0.001406</td>\n      <td>0.009081</td>\n      <td>0.016003</td>\n      <td>-1.674970e-02</td>\n      <td>1.486656e-02</td>\n      <td>0.049280</td>\n      <td>0.014582</td>\n      <td>0.009094</td>\n      <td>5.972991e-24</td>\n      <td>0.069265</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>0.011732</td>\n      <td>-1.948576e-03</td>\n      <td>-2.284777e-02</td>\n      <td>-0.005827</td>\n      <td>-4.802786e-03</td>\n      <td>0.021996</td>\n      <td>2.433364e-02</td>\n      <td>-4.714110e-03</td>\n      <td>2.490376e-03</td>\n      <td>5.634027e-02</td>\n      <td>0.080780</td>\n      <td>-0.002803</td>\n      <td>7.975818e-03</td>\n      <td>1.673151e-02</td>\n      <td>0.060501</td>\n      <td>-0.000458</td>\n      <td>0.041013</td>\n      <td>-0.025686</td>\n      <td>-0.032919</td>\n      <td>-9.114328e-03</td>\n      <td>1.000000</td>\n      <td>1.537629e-02</td>\n      <td>2.382346e-02</td>\n      <td>0.033427</td>\n      <td>-2.293705e-02</td>\n      <td>-2.752017e-03</td>\n      <td>6.885061e-02</td>\n      <td>0.002950</td>\n      <td>3.271955e-03</td>\n      <td>8.381011e-02</td>\n      <td>6.617676e-03</td>\n      <td>-0.006729</td>\n      <td>0.015962</td>\n      <td>0.009461</td>\n      <td>-2.335768e-04</td>\n      <td>3.289986e-03</td>\n      <td>-4.693336e-04</td>\n      <td>-0.000768</td>\n      <td>-0.015123</td>\n      <td>4.111526e-02</td>\n      <td>...</td>\n      <td>2.140424e-02</td>\n      <td>-0.010152</td>\n      <td>5.000939e-02</td>\n      <td>3.414674e-03</td>\n      <td>5.917819e-03</td>\n      <td>1.902508e-02</td>\n      <td>0.048377</td>\n      <td>-7.896958e-03</td>\n      <td>4.644321e-02</td>\n      <td>0.001930</td>\n      <td>-0.017210</td>\n      <td>6.873983e-02</td>\n      <td>-8.451595e-03</td>\n      <td>2.977430e-02</td>\n      <td>-1.581771e-02</td>\n      <td>0.035997</td>\n      <td>1.142276e-02</td>\n      <td>-0.000653</td>\n      <td>1.535626e-02</td>\n      <td>-0.023523</td>\n      <td>2.258410e-02</td>\n      <td>6.369234e-02</td>\n      <td>-0.059050</td>\n      <td>0.018500</td>\n      <td>-2.553182e-03</td>\n      <td>-0.040467</td>\n      <td>-0.013875</td>\n      <td>-1.615035e-02</td>\n      <td>-0.025060</td>\n      <td>-0.051094</td>\n      <td>-0.030766</td>\n      <td>0.017155</td>\n      <td>-0.058559</td>\n      <td>2.418340e-02</td>\n      <td>8.465674e-03</td>\n      <td>-0.046887</td>\n      <td>-0.026727</td>\n      <td>0.011608</td>\n      <td>3.429030e-02</td>\n      <td>0.000528</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>-0.004171</td>\n      <td>-2.482779e-02</td>\n      <td>1.023857e-02</td>\n      <td>-0.006829</td>\n      <td>-2.253953e-02</td>\n      <td>0.028229</td>\n      <td>1.430274e-02</td>\n      <td>3.710931e-02</td>\n      <td>9.546852e-03</td>\n      <td>-2.522298e-03</td>\n      <td>0.028091</td>\n      <td>0.000000</td>\n      <td>3.339850e-02</td>\n      <td>4.289779e-03</td>\n      <td>0.033772</td>\n      <td>0.029470</td>\n      <td>0.001600</td>\n      <td>0.018547</td>\n      <td>0.016010</td>\n      <td>-1.205731e-02</td>\n      <td>0.015376</td>\n      <td>1.000000e+00</td>\n      <td>-3.082055e-02</td>\n      <td>0.002796</td>\n      <td>4.694795e-02</td>\n      <td>-1.234030e-02</td>\n      <td>3.038111e-02</td>\n      <td>0.034053</td>\n      <td>2.809999e-02</td>\n      <td>-2.800653e-02</td>\n      <td>1.035981e-23</td>\n      <td>0.021422</td>\n      <td>0.023416</td>\n      <td>0.075653</td>\n      <td>4.475644e-25</td>\n      <td>1.919722e-02</td>\n      <td>-1.016575e-02</td>\n      <td>0.004069</td>\n      <td>-0.003656</td>\n      <td>-2.889132e-03</td>\n      <td>...</td>\n      <td>2.602682e-02</td>\n      <td>0.037711</td>\n      <td>-2.179239e-02</td>\n      <td>3.471294e-02</td>\n      <td>-1.292455e-03</td>\n      <td>7.584174e-03</td>\n      <td>0.040604</td>\n      <td>-5.205341e-24</td>\n      <td>1.832458e-02</td>\n      <td>0.067365</td>\n      <td>0.105281</td>\n      <td>9.618135e-03</td>\n      <td>3.380271e-02</td>\n      <td>3.469163e-03</td>\n      <td>4.129999e-02</td>\n      <td>-0.020983</td>\n      <td>-8.657915e-03</td>\n      <td>0.014010</td>\n      <td>4.644809e-02</td>\n      <td>0.036433</td>\n      <td>1.304773e-02</td>\n      <td>3.117772e-02</td>\n      <td>0.024128</td>\n      <td>-0.015104</td>\n      <td>3.395412e-24</td>\n      <td>-0.030809</td>\n      <td>0.010301</td>\n      <td>-5.108802e-02</td>\n      <td>0.023635</td>\n      <td>0.029242</td>\n      <td>-0.017278</td>\n      <td>0.012586</td>\n      <td>0.018426</td>\n      <td>1.746862e-02</td>\n      <td>-2.674431e-02</td>\n      <td>0.018593</td>\n      <td>-0.005139</td>\n      <td>0.024018</td>\n      <td>1.012680e-01</td>\n      <td>0.020412</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>-0.023124</td>\n      <td>-1.169271e-03</td>\n      <td>-3.015000e-02</td>\n      <td>-0.014836</td>\n      <td>-1.034427e-02</td>\n      <td>-0.012168</td>\n      <td>-1.071610e-02</td>\n      <td>7.663906e-04</td>\n      <td>1.062935e-02</td>\n      <td>6.355693e-03</td>\n      <td>0.008579</td>\n      <td>0.000000</td>\n      <td>-5.550042e-03</td>\n      <td>-6.544533e-04</td>\n      <td>-0.013194</td>\n      <td>0.118633</td>\n      <td>-0.009366</td>\n      <td>0.036340</td>\n      <td>0.014081</td>\n      <td>-4.594695e-03</td>\n      <td>0.023823</td>\n      <td>-3.082055e-02</td>\n      <td>1.000000e+00</td>\n      <td>0.006795</td>\n      <td>1.187244e-22</td>\n      <td>-4.433640e-02</td>\n      <td>-2.631178e-02</td>\n      <td>0.008127</td>\n      <td>-1.133899e-02</td>\n      <td>7.484949e-02</td>\n      <td>7.597198e-03</td>\n      <td>-0.017576</td>\n      <td>0.007159</td>\n      <td>-0.037230</td>\n      <td>1.326738e-02</td>\n      <td>-2.028429e-02</td>\n      <td>-4.652692e-03</td>\n      <td>-0.012021</td>\n      <td>0.003628</td>\n      <td>-2.287112e-02</td>\n      <td>...</td>\n      <td>3.624389e-02</td>\n      <td>0.010473</td>\n      <td>3.574486e-03</td>\n      <td>2.602564e-02</td>\n      <td>-3.041181e-02</td>\n      <td>3.440199e-03</td>\n      <td>-0.016237</td>\n      <td>3.795635e-23</td>\n      <td>2.377259e-23</td>\n      <td>0.008807</td>\n      <td>0.020217</td>\n      <td>-6.210342e-03</td>\n      <td>-2.601736e-03</td>\n      <td>-7.864598e-03</td>\n      <td>-2.473728e-02</td>\n      <td>-0.000091</td>\n      <td>-9.466653e-03</td>\n      <td>0.019915</td>\n      <td>1.340265e-02</td>\n      <td>0.001230</td>\n      <td>-1.896195e-03</td>\n      <td>8.489739e-03</td>\n      <td>-0.008394</td>\n      <td>-0.012263</td>\n      <td>8.785065e-04</td>\n      <td>0.016004</td>\n      <td>-0.001110</td>\n      <td>-4.716189e-02</td>\n      <td>0.025762</td>\n      <td>0.017654</td>\n      <td>0.013366</td>\n      <td>-0.012559</td>\n      <td>0.007056</td>\n      <td>-1.490645e-02</td>\n      <td>1.681381e-03</td>\n      <td>0.016352</td>\n      <td>0.002651</td>\n      <td>-0.000566</td>\n      <td>1.110310e-02</td>\n      <td>0.002287</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>0.008417</td>\n      <td>-3.741771e-02</td>\n      <td>-9.326876e-03</td>\n      <td>-0.008388</td>\n      <td>3.476401e-03</td>\n      <td>0.015580</td>\n      <td>4.207474e-03</td>\n      <td>4.550247e-02</td>\n      <td>2.073667e-02</td>\n      <td>-1.002383e-02</td>\n      <td>0.098131</td>\n      <td>-0.024049</td>\n      <td>6.147896e-02</td>\n      <td>1.313545e-02</td>\n      <td>0.044688</td>\n      <td>-0.013651</td>\n      <td>0.121765</td>\n      <td>0.086483</td>\n      <td>0.012262</td>\n      <td>-3.634279e-03</td>\n      <td>0.033427</td>\n      <td>2.796195e-03</td>\n      <td>6.794702e-03</td>\n      <td>1.000000</td>\n      <td>4.171329e-02</td>\n      <td>3.068257e-02</td>\n      <td>3.986258e-02</td>\n      <td>0.058086</td>\n      <td>3.180785e-02</td>\n      <td>4.871862e-02</td>\n      <td>-5.622761e-02</td>\n      <td>0.088371</td>\n      <td>0.025477</td>\n      <td>0.000767</td>\n      <td>2.027796e-02</td>\n      <td>2.874574e-02</td>\n      <td>2.396845e-02</td>\n      <td>0.034378</td>\n      <td>0.028755</td>\n      <td>2.827543e-02</td>\n      <td>...</td>\n      <td>-5.118189e-03</td>\n      <td>-0.023524</td>\n      <td>1.851701e-02</td>\n      <td>5.837212e-02</td>\n      <td>2.380613e-02</td>\n      <td>-9.765547e-23</td>\n      <td>0.070140</td>\n      <td>7.762090e-23</td>\n      <td>1.750647e-03</td>\n      <td>0.035045</td>\n      <td>0.047639</td>\n      <td>6.275674e-02</td>\n      <td>1.029840e-02</td>\n      <td>8.740779e-03</td>\n      <td>-3.546431e-02</td>\n      <td>0.004298</td>\n      <td>9.077768e-03</td>\n      <td>0.111918</td>\n      <td>1.166250e-01</td>\n      <td>0.073468</td>\n      <td>3.835104e-02</td>\n      <td>1.887393e-02</td>\n      <td>0.044944</td>\n      <td>0.004705</td>\n      <td>1.513673e-02</td>\n      <td>0.032042</td>\n      <td>0.065708</td>\n      <td>-6.566402e-02</td>\n      <td>0.060016</td>\n      <td>0.018555</td>\n      <td>0.065139</td>\n      <td>0.046402</td>\n      <td>0.023492</td>\n      <td>2.282876e-02</td>\n      <td>-1.012858e-02</td>\n      <td>0.026197</td>\n      <td>0.024607</td>\n      <td>0.037568</td>\n      <td>8.386414e-02</td>\n      <td>0.031640</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>0.001181</td>\n      <td>1.620844e-02</td>\n      <td>-1.535679e-02</td>\n      <td>-0.008842</td>\n      <td>2.146473e-02</td>\n      <td>0.010730</td>\n      <td>2.095348e-02</td>\n      <td>-5.607238e-03</td>\n      <td>6.248413e-02</td>\n      <td>2.781578e-02</td>\n      <td>0.016810</td>\n      <td>0.000000</td>\n      <td>7.703283e-02</td>\n      <td>1.840944e-02</td>\n      <td>0.072126</td>\n      <td>-0.003645</td>\n      <td>0.068720</td>\n      <td>0.067739</td>\n      <td>0.033794</td>\n      <td>2.107205e-02</td>\n      <td>-0.022937</td>\n      <td>4.694795e-02</td>\n      <td>1.187244e-22</td>\n      <td>0.041713</td>\n      <td>1.000000e+00</td>\n      <td>1.185415e-22</td>\n      <td>1.581513e-02</td>\n      <td>-0.005031</td>\n      <td>3.457164e-02</td>\n      <td>5.785250e-02</td>\n      <td>1.160349e-03</td>\n      <td>0.033217</td>\n      <td>0.028666</td>\n      <td>-0.023191</td>\n      <td>1.105409e-01</td>\n      <td>1.486169e-22</td>\n      <td>-9.040906e-02</td>\n      <td>0.019726</td>\n      <td>0.019495</td>\n      <td>1.130698e-01</td>\n      <td>...</td>\n      <td>-2.151018e-23</td>\n      <td>0.019874</td>\n      <td>1.857880e-02</td>\n      <td>-1.029065e-01</td>\n      <td>1.192952e-02</td>\n      <td>-1.795504e-22</td>\n      <td>0.044684</td>\n      <td>1.427414e-22</td>\n      <td>1.362392e-02</td>\n      <td>0.051914</td>\n      <td>0.024022</td>\n      <td>-1.465344e-02</td>\n      <td>-2.327100e-22</td>\n      <td>9.052038e-02</td>\n      <td>-2.425364e-02</td>\n      <td>0.094941</td>\n      <td>2.110467e-04</td>\n      <td>0.077589</td>\n      <td>1.815763e-02</td>\n      <td>0.090743</td>\n      <td>6.275972e-02</td>\n      <td>2.607683e-02</td>\n      <td>0.086774</td>\n      <td>0.008546</td>\n      <td>-9.320653e-23</td>\n      <td>0.008346</td>\n      <td>0.014229</td>\n      <td>1.615745e-02</td>\n      <td>0.010653</td>\n      <td>0.022216</td>\n      <td>0.107385</td>\n      <td>0.081840</td>\n      <td>0.011411</td>\n      <td>-7.411563e-23</td>\n      <td>6.026395e-02</td>\n      <td>0.047744</td>\n      <td>0.022167</td>\n      <td>0.031203</td>\n      <td>4.295918e-02</td>\n      <td>0.033907</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>-0.013518</td>\n      <td>5.351820e-23</td>\n      <td>-4.927043e-25</td>\n      <td>-0.017292</td>\n      <td>1.042304e-01</td>\n      <td>0.000602</td>\n      <td>2.388479e-02</td>\n      <td>4.881935e-02</td>\n      <td>-3.252392e-23</td>\n      <td>-4.617847e-02</td>\n      <td>0.127737</td>\n      <td>0.000000</td>\n      <td>6.847840e-02</td>\n      <td>-1.004313e-02</td>\n      <td>0.002468</td>\n      <td>-0.080398</td>\n      <td>0.059758</td>\n      <td>0.074387</td>\n      <td>0.058931</td>\n      <td>-1.927456e-03</td>\n      <td>-0.002752</td>\n      <td>-1.234030e-02</td>\n      <td>-4.433640e-02</td>\n      <td>0.030683</td>\n      <td>1.185415e-22</td>\n      <td>1.000000e+00</td>\n      <td>-2.010364e-02</td>\n      <td>0.006071</td>\n      <td>-3.784966e-02</td>\n      <td>1.459086e-22</td>\n      <td>1.324824e-02</td>\n      <td>0.128609</td>\n      <td>0.084032</td>\n      <td>-0.014733</td>\n      <td>-4.070995e-02</td>\n      <td>3.945571e-23</td>\n      <td>1.249789e-01</td>\n      <td>0.242340</td>\n      <td>0.025995</td>\n      <td>1.176278e-01</td>\n      <td>...</td>\n      <td>-5.723033e-24</td>\n      <td>0.018368</td>\n      <td>7.155032e-02</td>\n      <td>5.921620e-02</td>\n      <td>6.999141e-02</td>\n      <td>-4.763123e-23</td>\n      <td>-0.027741</td>\n      <td>3.788335e-23</td>\n      <td>-5.748889e-04</td>\n      <td>0.034423</td>\n      <td>-0.003575</td>\n      <td>-1.469119e-03</td>\n      <td>-1.409504e-02</td>\n      <td>2.316396e-17</td>\n      <td>4.011601e-02</td>\n      <td>0.011083</td>\n      <td>3.758652e-02</td>\n      <td>0.257571</td>\n      <td>1.017234e-01</td>\n      <td>0.010833</td>\n      <td>-7.311230e-03</td>\n      <td>8.239422e-02</td>\n      <td>0.022529</td>\n      <td>0.013295</td>\n      <td>-2.474082e-23</td>\n      <td>0.013013</td>\n      <td>0.071674</td>\n      <td>-1.197889e-01</td>\n      <td>0.043497</td>\n      <td>0.043688</td>\n      <td>-0.025517</td>\n      <td>0.249830</td>\n      <td>0.019601</td>\n      <td>1.709167e-01</td>\n      <td>-4.704243e-02</td>\n      <td>0.007945</td>\n      <td>0.042324</td>\n      <td>0.037426</td>\n      <td>1.609377e-01</td>\n      <td>0.046661</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>0.045303</td>\n      <td>-3.754397e-23</td>\n      <td>-5.515577e-03</td>\n      <td>0.017007</td>\n      <td>8.737227e-03</td>\n      <td>0.040572</td>\n      <td>2.546725e-02</td>\n      <td>3.264006e-02</td>\n      <td>5.125162e-02</td>\n      <td>6.324012e-03</td>\n      <td>0.051495</td>\n      <td>0.002597</td>\n      <td>5.244040e-02</td>\n      <td>1.221204e-03</td>\n      <td>0.070330</td>\n      <td>-0.032696</td>\n      <td>0.029840</td>\n      <td>0.003006</td>\n      <td>0.057951</td>\n      <td>4.381796e-02</td>\n      <td>0.068851</td>\n      <td>3.038111e-02</td>\n      <td>-2.631178e-02</td>\n      <td>0.039863</td>\n      <td>1.581513e-02</td>\n      <td>-2.010364e-02</td>\n      <td>1.000000e+00</td>\n      <td>0.004800</td>\n      <td>1.602144e-02</td>\n      <td>1.319496e-01</td>\n      <td>2.796347e-02</td>\n      <td>0.042206</td>\n      <td>0.018715</td>\n      <td>-0.020465</td>\n      <td>4.915498e-03</td>\n      <td>2.710192e-02</td>\n      <td>1.480779e-02</td>\n      <td>-0.001784</td>\n      <td>0.023990</td>\n      <td>2.552074e-02</td>\n      <td>...</td>\n      <td>-9.602585e-03</td>\n      <td>0.001747</td>\n      <td>-3.400068e-02</td>\n      <td>7.333357e-04</td>\n      <td>-2.368351e-23</td>\n      <td>3.343155e-23</td>\n      <td>0.112213</td>\n      <td>-2.658214e-23</td>\n      <td>1.348650e-02</td>\n      <td>-0.042222</td>\n      <td>0.004950</td>\n      <td>-3.957113e-04</td>\n      <td>4.053141e-02</td>\n      <td>-1.745839e-02</td>\n      <td>1.542186e-02</td>\n      <td>-0.016576</td>\n      <td>2.795466e-02</td>\n      <td>-0.023470</td>\n      <td>1.003522e-02</td>\n      <td>0.088182</td>\n      <td>5.177299e-02</td>\n      <td>2.945069e-02</td>\n      <td>0.031202</td>\n      <td>0.003024</td>\n      <td>-4.042528e-03</td>\n      <td>0.029823</td>\n      <td>0.011608</td>\n      <td>-2.051078e-02</td>\n      <td>0.001726</td>\n      <td>0.013740</td>\n      <td>-0.027809</td>\n      <td>-0.011291</td>\n      <td>-0.047493</td>\n      <td>1.541072e-02</td>\n      <td>3.837496e-02</td>\n      <td>-0.003759</td>\n      <td>0.000019</td>\n      <td>0.005137</td>\n      <td>4.257213e-02</td>\n      <td>-0.010629</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>0.001611</td>\n      <td>-4.240417e-03</td>\n      <td>1.624940e-02</td>\n      <td>0.014851</td>\n      <td>2.400270e-02</td>\n      <td>-0.010645</td>\n      <td>1.208863e-02</td>\n      <td>1.147357e-02</td>\n      <td>-1.540714e-03</td>\n      <td>-1.540611e-02</td>\n      <td>0.037407</td>\n      <td>-0.006711</td>\n      <td>2.337627e-02</td>\n      <td>1.057879e-02</td>\n      <td>0.028175</td>\n      <td>-0.056351</td>\n      <td>0.067820</td>\n      <td>0.086762</td>\n      <td>0.030735</td>\n      <td>1.503463e-02</td>\n      <td>0.002950</td>\n      <td>3.405343e-02</td>\n      <td>8.127038e-03</td>\n      <td>0.058086</td>\n      <td>-5.030803e-03</td>\n      <td>6.070609e-03</td>\n      <td>4.799981e-03</td>\n      <td>1.000000</td>\n      <td>1.060016e-02</td>\n      <td>-3.168518e-02</td>\n      <td>-4.947186e-02</td>\n      <td>0.013076</td>\n      <td>0.030509</td>\n      <td>0.011340</td>\n      <td>1.030228e-02</td>\n      <td>9.895046e-03</td>\n      <td>2.484959e-02</td>\n      <td>0.013981</td>\n      <td>0.038934</td>\n      <td>8.474303e-03</td>\n      <td>...</td>\n      <td>-5.220923e-03</td>\n      <td>0.061613</td>\n      <td>4.966725e-02</td>\n      <td>6.531995e-03</td>\n      <td>3.070150e-02</td>\n      <td>5.860484e-03</td>\n      <td>0.050049</td>\n      <td>-7.562365e-24</td>\n      <td>1.229418e-02</td>\n      <td>0.106010</td>\n      <td>-0.039392</td>\n      <td>3.274947e-02</td>\n      <td>2.342461e-02</td>\n      <td>-2.000312e-02</td>\n      <td>1.432688e-02</td>\n      <td>-0.008352</td>\n      <td>8.897896e-03</td>\n      <td>0.010753</td>\n      <td>7.319586e-02</td>\n      <td>0.114570</td>\n      <td>4.364677e-02</td>\n      <td>1.368362e-02</td>\n      <td>0.063942</td>\n      <td>-0.006811</td>\n      <td>-7.551662e-03</td>\n      <td>0.044200</td>\n      <td>0.049987</td>\n      <td>-7.878301e-03</td>\n      <td>0.071202</td>\n      <td>-0.028752</td>\n      <td>0.033904</td>\n      <td>0.019269</td>\n      <td>0.008016</td>\n      <td>-8.857087e-04</td>\n      <td>1.272468e-02</td>\n      <td>0.023718</td>\n      <td>-0.025664</td>\n      <td>0.051794</td>\n      <td>5.065528e-02</td>\n      <td>0.063953</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>-0.006969</td>\n      <td>-1.971385e-02</td>\n      <td>-1.753720e-02</td>\n      <td>0.033426</td>\n      <td>-2.545623e-02</td>\n      <td>0.040871</td>\n      <td>-2.459195e-02</td>\n      <td>-2.334049e-02</td>\n      <td>7.176594e-03</td>\n      <td>2.254493e-02</td>\n      <td>0.097626</td>\n      <td>0.000000</td>\n      <td>4.979923e-02</td>\n      <td>5.630809e-03</td>\n      <td>0.007259</td>\n      <td>-0.022470</td>\n      <td>0.035458</td>\n      <td>0.041230</td>\n      <td>0.022475</td>\n      <td>6.071639e-04</td>\n      <td>0.003272</td>\n      <td>2.809999e-02</td>\n      <td>-1.133899e-02</td>\n      <td>0.031808</td>\n      <td>3.457164e-02</td>\n      <td>-3.784966e-02</td>\n      <td>1.602144e-02</td>\n      <td>0.010600</td>\n      <td>1.000000e+00</td>\n      <td>7.789293e-03</td>\n      <td>2.895799e-03</td>\n      <td>-0.009543</td>\n      <td>0.013646</td>\n      <td>0.006202</td>\n      <td>-2.168189e-02</td>\n      <td>6.794812e-02</td>\n      <td>-4.075858e-02</td>\n      <td>-0.009801</td>\n      <td>-0.073314</td>\n      <td>-6.972679e-03</td>\n      <td>...</td>\n      <td>-2.619015e-03</td>\n      <td>0.061898</td>\n      <td>-4.892005e-03</td>\n      <td>3.652322e-02</td>\n      <td>-2.687082e-02</td>\n      <td>-1.208663e-24</td>\n      <td>0.089166</td>\n      <td>-4.213270e-03</td>\n      <td>-1.229859e-02</td>\n      <td>-0.004157</td>\n      <td>0.043074</td>\n      <td>3.602846e-03</td>\n      <td>-5.451063e-03</td>\n      <td>1.524282e-02</td>\n      <td>-1.156185e-02</td>\n      <td>-0.014893</td>\n      <td>-2.092596e-02</td>\n      <td>-0.033068</td>\n      <td>-1.679977e-03</td>\n      <td>0.082057</td>\n      <td>2.465420e-02</td>\n      <td>1.889268e-04</td>\n      <td>0.039826</td>\n      <td>0.004760</td>\n      <td>-4.482113e-02</td>\n      <td>-0.001843</td>\n      <td>0.047703</td>\n      <td>-4.603576e-25</td>\n      <td>0.041527</td>\n      <td>0.007672</td>\n      <td>0.032472</td>\n      <td>-0.021968</td>\n      <td>0.044637</td>\n      <td>-1.265272e-02</td>\n      <td>7.748637e-03</td>\n      <td>0.013539</td>\n      <td>0.021657</td>\n      <td>-0.000658</td>\n      <td>5.578511e-03</td>\n      <td>0.027594</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>0.003274</td>\n      <td>-3.399468e-02</td>\n      <td>-2.298376e-24</td>\n      <td>0.000960</td>\n      <td>3.455710e-02</td>\n      <td>0.029398</td>\n      <td>2.736640e-03</td>\n      <td>1.073976e-02</td>\n      <td>5.086326e-02</td>\n      <td>1.029208e-02</td>\n      <td>0.007134</td>\n      <td>0.000000</td>\n      <td>4.180411e-02</td>\n      <td>1.429550e-02</td>\n      <td>-0.117706</td>\n      <td>-0.014057</td>\n      <td>0.019020</td>\n      <td>0.026236</td>\n      <td>-0.036860</td>\n      <td>1.296937e-02</td>\n      <td>0.083810</td>\n      <td>-2.800653e-02</td>\n      <td>7.484949e-02</td>\n      <td>0.048719</td>\n      <td>5.785250e-02</td>\n      <td>1.459086e-22</td>\n      <td>1.319496e-01</td>\n      <td>-0.031685</td>\n      <td>7.789293e-03</td>\n      <td>1.000000e+00</td>\n      <td>3.326900e-02</td>\n      <td>0.005158</td>\n      <td>0.018355</td>\n      <td>-0.019978</td>\n      <td>-1.510043e-23</td>\n      <td>1.494246e-02</td>\n      <td>-2.920423e-03</td>\n      <td>0.032115</td>\n      <td>-0.012343</td>\n      <td>1.161084e-02</td>\n      <td>...</td>\n      <td>2.824646e-03</td>\n      <td>0.021043</td>\n      <td>3.334892e-02</td>\n      <td>3.988944e-02</td>\n      <td>9.710856e-03</td>\n      <td>-2.209342e-22</td>\n      <td>0.055950</td>\n      <td>1.756847e-22</td>\n      <td>2.105720e-02</td>\n      <td>0.003520</td>\n      <td>-0.020951</td>\n      <td>9.214355e-02</td>\n      <td>-2.864644e-22</td>\n      <td>-2.720173e-02</td>\n      <td>-1.239601e-02</td>\n      <td>-0.010973</td>\n      <td>1.717960e-04</td>\n      <td>0.049124</td>\n      <td>1.334364e-02</td>\n      <td>-0.011167</td>\n      <td>5.108759e-02</td>\n      <td>-4.122883e-02</td>\n      <td>0.061155</td>\n      <td>-0.043517</td>\n      <td>-1.147570e-22</td>\n      <td>0.036259</td>\n      <td>-0.020798</td>\n      <td>1.315247e-02</td>\n      <td>-0.016607</td>\n      <td>0.049220</td>\n      <td>0.093554</td>\n      <td>-0.019691</td>\n      <td>-0.019847</td>\n      <td>-5.388175e-03</td>\n      <td>5.260133e-02</td>\n      <td>0.023341</td>\n      <td>-0.053949</td>\n      <td>0.002666</td>\n      <td>5.300944e-02</td>\n      <td>-0.031724</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>581</th>\n      <td>0.007481</td>\n      <td>-7.127559e-02</td>\n      <td>-3.781708e-02</td>\n      <td>0.049113</td>\n      <td>2.349257e-02</td>\n      <td>0.018318</td>\n      <td>3.152727e-02</td>\n      <td>8.704572e-02</td>\n      <td>1.709680e-02</td>\n      <td>5.128713e-02</td>\n      <td>0.009376</td>\n      <td>0.000000</td>\n      <td>-7.377157e-02</td>\n      <td>-2.000780e-02</td>\n      <td>0.035676</td>\n      <td>-0.028979</td>\n      <td>0.023234</td>\n      <td>0.001483</td>\n      <td>-0.011221</td>\n      <td>1.856273e-02</td>\n      <td>-0.017210</td>\n      <td>1.052809e-01</td>\n      <td>2.021734e-02</td>\n      <td>0.047639</td>\n      <td>2.402167e-02</td>\n      <td>-3.574903e-03</td>\n      <td>4.950306e-03</td>\n      <td>-0.039392</td>\n      <td>4.307421e-02</td>\n      <td>-2.095081e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.037136</td>\n      <td>-0.014131</td>\n      <td>0.004759</td>\n      <td>0.000000e+00</td>\n      <td>2.260193e-02</td>\n      <td>-8.834848e-03</td>\n      <td>0.079972</td>\n      <td>-0.024004</td>\n      <td>2.461855e-02</td>\n      <td>...</td>\n      <td>0.000000e+00</td>\n      <td>0.019563</td>\n      <td>5.463329e-03</td>\n      <td>5.028054e-02</td>\n      <td>-3.427343e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.061075</td>\n      <td>7.637057e-03</td>\n      <td>-1.019234e-02</td>\n      <td>-0.017586</td>\n      <td>1.000000</td>\n      <td>-2.637795e-02</td>\n      <td>2.876661e-03</td>\n      <td>3.740478e-03</td>\n      <td>3.048808e-03</td>\n      <td>-0.065787</td>\n      <td>3.139952e-03</td>\n      <td>0.065016</td>\n      <td>7.400636e-02</td>\n      <td>0.038908</td>\n      <td>-5.927182e-02</td>\n      <td>1.519683e-02</td>\n      <td>0.052924</td>\n      <td>-0.015143</td>\n      <td>0.000000e+00</td>\n      <td>-0.012036</td>\n      <td>-0.001947</td>\n      <td>4.164557e-02</td>\n      <td>-0.020311</td>\n      <td>0.011509</td>\n      <td>0.057718</td>\n      <td>0.058114</td>\n      <td>-0.007148</td>\n      <td>0.000000e+00</td>\n      <td>3.083148e-02</td>\n      <td>0.003842</td>\n      <td>0.035489</td>\n      <td>0.010186</td>\n      <td>6.800770e-02</td>\n      <td>-0.014157</td>\n    </tr>\n    <tr>\n      <th>582</th>\n      <td>0.009522</td>\n      <td>-2.549881e-02</td>\n      <td>3.819230e-25</td>\n      <td>-0.026306</td>\n      <td>-1.455653e-24</td>\n      <td>-0.000181</td>\n      <td>-1.725705e-02</td>\n      <td>-3.477035e-04</td>\n      <td>-2.159028e-02</td>\n      <td>2.337188e-02</td>\n      <td>-0.000886</td>\n      <td>0.000000</td>\n      <td>4.917437e-02</td>\n      <td>-4.628220e-04</td>\n      <td>-0.051358</td>\n      <td>-0.021790</td>\n      <td>0.028530</td>\n      <td>0.010922</td>\n      <td>0.009962</td>\n      <td>-1.648851e-02</td>\n      <td>0.068740</td>\n      <td>9.618135e-03</td>\n      <td>-6.210342e-03</td>\n      <td>0.062757</td>\n      <td>-1.465344e-02</td>\n      <td>-1.469119e-03</td>\n      <td>-3.957113e-04</td>\n      <td>0.032749</td>\n      <td>3.602846e-03</td>\n      <td>9.214355e-02</td>\n      <td>-6.116002e-05</td>\n      <td>-0.001751</td>\n      <td>0.017796</td>\n      <td>-0.007269</td>\n      <td>2.504764e-24</td>\n      <td>-3.037003e-23</td>\n      <td>-1.134597e-03</td>\n      <td>0.000127</td>\n      <td>0.017126</td>\n      <td>-2.430096e-23</td>\n      <td>...</td>\n      <td>-1.828978e-04</td>\n      <td>-0.000681</td>\n      <td>2.087368e-02</td>\n      <td>-1.291435e-03</td>\n      <td>2.284585e-02</td>\n      <td>3.667729e-23</td>\n      <td>-0.000648</td>\n      <td>-2.895033e-02</td>\n      <td>2.476968e-02</td>\n      <td>0.006577</td>\n      <td>-0.026378</td>\n      <td>1.000000e+00</td>\n      <td>1.192346e-02</td>\n      <td>5.842255e-05</td>\n      <td>-2.424592e-02</td>\n      <td>0.018317</td>\n      <td>4.041688e-04</td>\n      <td>-0.001193</td>\n      <td>-8.640104e-04</td>\n      <td>0.017012</td>\n      <td>3.977850e-02</td>\n      <td>-7.913588e-04</td>\n      <td>-0.035243</td>\n      <td>0.009915</td>\n      <td>1.903176e-23</td>\n      <td>0.028728</td>\n      <td>-0.000500</td>\n      <td>3.446837e-02</td>\n      <td>0.006881</td>\n      <td>0.001708</td>\n      <td>0.049548</td>\n      <td>-0.001530</td>\n      <td>0.007724</td>\n      <td>-1.104813e-03</td>\n      <td>1.005562e-02</td>\n      <td>0.004280</td>\n      <td>0.018192</td>\n      <td>0.004623</td>\n      <td>3.888094e-23</td>\n      <td>0.054730</td>\n    </tr>\n    <tr>\n      <th>583</th>\n      <td>-0.005893</td>\n      <td>-1.193537e-03</td>\n      <td>9.702808e-25</td>\n      <td>-0.030765</td>\n      <td>-2.190478e-02</td>\n      <td>0.003250</td>\n      <td>-2.470784e-03</td>\n      <td>-4.627283e-02</td>\n      <td>6.386598e-23</td>\n      <td>-5.323612e-02</td>\n      <td>0.016910</td>\n      <td>-0.041491</td>\n      <td>-2.709533e-03</td>\n      <td>-2.392499e-02</td>\n      <td>0.019388</td>\n      <td>-0.002875</td>\n      <td>0.033692</td>\n      <td>0.031448</td>\n      <td>-0.001975</td>\n      <td>6.743431e-03</td>\n      <td>-0.008452</td>\n      <td>3.380271e-02</td>\n      <td>-2.601736e-03</td>\n      <td>0.010298</td>\n      <td>-2.327100e-22</td>\n      <td>-1.409504e-02</td>\n      <td>4.053141e-02</td>\n      <td>0.023425</td>\n      <td>-5.451063e-03</td>\n      <td>-2.864644e-22</td>\n      <td>5.252496e-03</td>\n      <td>-0.027731</td>\n      <td>0.071722</td>\n      <td>-0.002063</td>\n      <td>-1.035984e-01</td>\n      <td>-7.748006e-23</td>\n      <td>-7.270123e-03</td>\n      <td>-0.002762</td>\n      <td>-0.003268</td>\n      <td>-9.460731e-03</td>\n      <td>...</td>\n      <td>1.121686e-23</td>\n      <td>0.000142</td>\n      <td>-3.031378e-02</td>\n      <td>-4.826638e-02</td>\n      <td>-1.707852e-02</td>\n      <td>9.354348e-23</td>\n      <td>-0.004578</td>\n      <td>-3.933446e-02</td>\n      <td>4.968455e-02</td>\n      <td>0.005249</td>\n      <td>0.002877</td>\n      <td>1.192346e-02</td>\n      <td>1.000000e+00</td>\n      <td>-8.074341e-02</td>\n      <td>1.477147e-02</td>\n      <td>0.000571</td>\n      <td>1.459924e-02</td>\n      <td>-0.007022</td>\n      <td>1.648939e-02</td>\n      <td>-0.007608</td>\n      <td>1.659697e-02</td>\n      <td>5.566185e-02</td>\n      <td>0.009168</td>\n      <td>0.015007</td>\n      <td>4.857129e-23</td>\n      <td>0.014103</td>\n      <td>-0.013455</td>\n      <td>6.885209e-02</td>\n      <td>0.011710</td>\n      <td>-0.003873</td>\n      <td>0.007437</td>\n      <td>0.034216</td>\n      <td>0.015965</td>\n      <td>-3.576897e-02</td>\n      <td>3.590781e-02</td>\n      <td>0.016162</td>\n      <td>-0.019254</td>\n      <td>0.000343</td>\n      <td>6.361259e-02</td>\n      <td>-0.005362</td>\n    </tr>\n    <tr>\n      <th>584</th>\n      <td>-0.011143</td>\n      <td>-6.607594e-23</td>\n      <td>6.096952e-25</td>\n      <td>-0.008766</td>\n      <td>1.170911e-01</td>\n      <td>0.218799</td>\n      <td>2.742675e-02</td>\n      <td>3.440889e-02</td>\n      <td>4.017738e-23</td>\n      <td>1.730796e-02</td>\n      <td>0.046093</td>\n      <td>0.030787</td>\n      <td>3.564636e-02</td>\n      <td>2.705460e-01</td>\n      <td>-0.004816</td>\n      <td>0.006674</td>\n      <td>0.014212</td>\n      <td>0.053160</td>\n      <td>0.003655</td>\n      <td>1.498314e-02</td>\n      <td>0.029774</td>\n      <td>3.469163e-03</td>\n      <td>-7.864598e-03</td>\n      <td>0.008741</td>\n      <td>9.052038e-02</td>\n      <td>2.316396e-17</td>\n      <td>-1.745839e-02</td>\n      <td>-0.020003</td>\n      <td>1.524282e-02</td>\n      <td>-2.720173e-02</td>\n      <td>6.913832e-02</td>\n      <td>0.019321</td>\n      <td>-0.043604</td>\n      <td>0.002901</td>\n      <td>2.159795e-01</td>\n      <td>-2.498594e-02</td>\n      <td>-1.150324e-01</td>\n      <td>0.150100</td>\n      <td>-0.020101</td>\n      <td>7.396744e-02</td>\n      <td>...</td>\n      <td>-2.131696e-02</td>\n      <td>-0.012315</td>\n      <td>3.646427e-02</td>\n      <td>2.456508e-02</td>\n      <td>-2.845206e-02</td>\n      <td>5.881956e-23</td>\n      <td>0.086917</td>\n      <td>-4.676912e-23</td>\n      <td>5.617261e-02</td>\n      <td>0.013440</td>\n      <td>0.003740</td>\n      <td>5.842255e-05</td>\n      <td>-8.074341e-02</td>\n      <td>1.000000e+00</td>\n      <td>-1.630748e-02</td>\n      <td>-0.004564</td>\n      <td>5.598577e-02</td>\n      <td>0.081295</td>\n      <td>4.711650e-02</td>\n      <td>0.078688</td>\n      <td>7.649857e-03</td>\n      <td>5.954190e-02</td>\n      <td>0.046090</td>\n      <td>0.049549</td>\n      <td>3.053116e-23</td>\n      <td>0.003143</td>\n      <td>0.005817</td>\n      <td>4.763654e-03</td>\n      <td>0.006246</td>\n      <td>-0.032619</td>\n      <td>-0.016687</td>\n      <td>0.097262</td>\n      <td>-0.006898</td>\n      <td>4.625324e-02</td>\n      <td>1.562600e-02</td>\n      <td>-0.011948</td>\n      <td>0.081991</td>\n      <td>-0.010763</td>\n      <td>-1.322189e-02</td>\n      <td>0.018099</td>\n    </tr>\n    <tr>\n      <th>585</th>\n      <td>0.010023</td>\n      <td>1.072662e-02</td>\n      <td>-2.118945e-25</td>\n      <td>0.004294</td>\n      <td>3.089337e-02</td>\n      <td>-0.002261</td>\n      <td>-2.765667e-03</td>\n      <td>9.494453e-03</td>\n      <td>1.414656e-02</td>\n      <td>-6.621725e-02</td>\n      <td>0.000000</td>\n      <td>0.025601</td>\n      <td>3.695681e-02</td>\n      <td>-8.280003e-03</td>\n      <td>0.022915</td>\n      <td>-0.081075</td>\n      <td>0.059965</td>\n      <td>0.023266</td>\n      <td>0.012542</td>\n      <td>3.703127e-03</td>\n      <td>-0.015818</td>\n      <td>4.129999e-02</td>\n      <td>-2.473728e-02</td>\n      <td>-0.035464</td>\n      <td>-2.425364e-02</td>\n      <td>4.011601e-02</td>\n      <td>1.542186e-02</td>\n      <td>0.014327</td>\n      <td>-1.156185e-02</td>\n      <td>-1.239601e-02</td>\n      <td>3.085462e-02</td>\n      <td>0.021762</td>\n      <td>0.029616</td>\n      <td>0.006840</td>\n      <td>-1.393866e-24</td>\n      <td>3.261690e-04</td>\n      <td>3.098151e-02</td>\n      <td>-0.015866</td>\n      <td>0.013856</td>\n      <td>1.705301e-02</td>\n      <td>...</td>\n      <td>-2.448830e-24</td>\n      <td>0.003196</td>\n      <td>3.114176e-02</td>\n      <td>-3.232547e-02</td>\n      <td>7.554673e-02</td>\n      <td>-2.043632e-23</td>\n      <td>0.030149</td>\n      <td>1.625746e-23</td>\n      <td>1.018164e-23</td>\n      <td>0.028623</td>\n      <td>0.003049</td>\n      <td>-2.424592e-02</td>\n      <td>1.477147e-02</td>\n      <td>-1.630748e-02</td>\n      <td>1.000000e+00</td>\n      <td>-0.052143</td>\n      <td>1.600227e-02</td>\n      <td>0.053308</td>\n      <td>2.941821e-02</td>\n      <td>0.012200</td>\n      <td>-1.922146e-02</td>\n      <td>6.336468e-03</td>\n      <td>0.039413</td>\n      <td>0.000670</td>\n      <td>-1.061504e-23</td>\n      <td>-0.047920</td>\n      <td>0.011719</td>\n      <td>-1.177095e-02</td>\n      <td>0.037070</td>\n      <td>0.015547</td>\n      <td>-0.001615</td>\n      <td>0.008166</td>\n      <td>0.002702</td>\n      <td>3.526485e-02</td>\n      <td>-1.145570e-02</td>\n      <td>0.014322</td>\n      <td>-0.006172</td>\n      <td>0.001525</td>\n      <td>3.974755e-02</td>\n      <td>0.012454</td>\n    </tr>\n    <tr>\n      <th>586</th>\n      <td>0.011086</td>\n      <td>-1.253459e-02</td>\n      <td>3.138097e-02</td>\n      <td>-0.016293</td>\n      <td>-4.561076e-02</td>\n      <td>0.003033</td>\n      <td>1.381579e-02</td>\n      <td>-3.849498e-02</td>\n      <td>4.079519e-02</td>\n      <td>2.603188e-02</td>\n      <td>-0.017834</td>\n      <td>-0.017917</td>\n      <td>6.290356e-02</td>\n      <td>1.342076e-02</td>\n      <td>-0.014343</td>\n      <td>0.016326</td>\n      <td>0.004224</td>\n      <td>-0.027941</td>\n      <td>0.020199</td>\n      <td>2.731709e-02</td>\n      <td>0.035997</td>\n      <td>-2.098255e-02</td>\n      <td>-9.121397e-05</td>\n      <td>0.004298</td>\n      <td>9.494057e-02</td>\n      <td>1.108304e-02</td>\n      <td>-1.657557e-02</td>\n      <td>-0.008352</td>\n      <td>-1.489344e-02</td>\n      <td>-1.097278e-02</td>\n      <td>-1.695233e-02</td>\n      <td>0.014570</td>\n      <td>-0.022823</td>\n      <td>-0.003188</td>\n      <td>-1.927292e-02</td>\n      <td>2.980473e-02</td>\n      <td>-2.010422e-02</td>\n      <td>-0.016862</td>\n      <td>-0.016633</td>\n      <td>3.118284e-02</td>\n      <td>...</td>\n      <td>2.217400e-03</td>\n      <td>-0.034672</td>\n      <td>1.997468e-02</td>\n      <td>8.071515e-02</td>\n      <td>8.293375e-03</td>\n      <td>9.014839e-23</td>\n      <td>-0.067016</td>\n      <td>-2.783186e-02</td>\n      <td>3.703513e-02</td>\n      <td>-0.005532</td>\n      <td>-0.065787</td>\n      <td>1.831685e-02</td>\n      <td>5.713584e-04</td>\n      <td>-4.563721e-03</td>\n      <td>-5.214263e-02</td>\n      <td>1.000000</td>\n      <td>3.686874e-03</td>\n      <td>0.043860</td>\n      <td>-1.071408e-03</td>\n      <td>0.004530</td>\n      <td>1.215510e-02</td>\n      <td>1.027356e-02</td>\n      <td>-0.038886</td>\n      <td>0.004425</td>\n      <td>-3.912377e-03</td>\n      <td>0.042399</td>\n      <td>-0.010516</td>\n      <td>-1.102839e-03</td>\n      <td>0.000576</td>\n      <td>0.019261</td>\n      <td>0.039884</td>\n      <td>0.006939</td>\n      <td>-0.030866</td>\n      <td>-1.463006e-02</td>\n      <td>6.089816e-02</td>\n      <td>-0.009424</td>\n      <td>-0.071638</td>\n      <td>-0.000404</td>\n      <td>2.542629e-02</td>\n      <td>0.013240</td>\n    </tr>\n    <tr>\n      <th>587</th>\n      <td>-0.043080</td>\n      <td>-4.196319e-23</td>\n      <td>-1.332141e-02</td>\n      <td>-0.040373</td>\n      <td>9.256750e-02</td>\n      <td>0.006238</td>\n      <td>1.320107e-02</td>\n      <td>2.431043e-04</td>\n      <td>-3.367791e-02</td>\n      <td>-3.118961e-02</td>\n      <td>0.009063</td>\n      <td>0.034848</td>\n      <td>1.020964e-02</td>\n      <td>4.328977e-02</td>\n      <td>0.012013</td>\n      <td>-0.006345</td>\n      <td>0.033815</td>\n      <td>0.024207</td>\n      <td>-0.002401</td>\n      <td>-1.140566e-02</td>\n      <td>0.011423</td>\n      <td>-8.657915e-03</td>\n      <td>-9.466653e-03</td>\n      <td>0.009078</td>\n      <td>2.110467e-04</td>\n      <td>3.758652e-02</td>\n      <td>2.795466e-02</td>\n      <td>0.008898</td>\n      <td>-2.092596e-02</td>\n      <td>1.717960e-04</td>\n      <td>2.626014e-02</td>\n      <td>0.041034</td>\n      <td>-0.008079</td>\n      <td>0.009551</td>\n      <td>3.620328e-02</td>\n      <td>-5.424440e-05</td>\n      <td>1.932482e-02</td>\n      <td>0.006064</td>\n      <td>0.000000</td>\n      <td>2.008254e-02</td>\n      <td>...</td>\n      <td>-1.584156e-02</td>\n      <td>0.071333</td>\n      <td>1.913337e-02</td>\n      <td>-2.077985e-02</td>\n      <td>9.587511e-03</td>\n      <td>-1.824873e-02</td>\n      <td>-0.010937</td>\n      <td>-1.093677e-03</td>\n      <td>1.358559e-02</td>\n      <td>0.030667</td>\n      <td>0.003140</td>\n      <td>4.041688e-04</td>\n      <td>1.459924e-02</td>\n      <td>5.598577e-02</td>\n      <td>1.600227e-02</td>\n      <td>0.003687</td>\n      <td>1.000000e+00</td>\n      <td>0.038288</td>\n      <td>4.261103e-02</td>\n      <td>0.018990</td>\n      <td>5.246365e-03</td>\n      <td>2.582805e-02</td>\n      <td>0.062958</td>\n      <td>0.010612</td>\n      <td>-2.223676e-17</td>\n      <td>0.012838</td>\n      <td>0.040738</td>\n      <td>1.411257e-23</td>\n      <td>0.036524</td>\n      <td>0.020141</td>\n      <td>0.005536</td>\n      <td>0.028703</td>\n      <td>-0.019973</td>\n      <td>3.978664e-02</td>\n      <td>6.203859e-03</td>\n      <td>0.037374</td>\n      <td>-0.037082</td>\n      <td>0.020852</td>\n      <td>3.874710e-02</td>\n      <td>0.021618</td>\n    </tr>\n    <tr>\n      <th>588</th>\n      <td>-0.019135</td>\n      <td>-4.808986e-02</td>\n      <td>-1.425057e-02</td>\n      <td>0.007293</td>\n      <td>1.212817e-01</td>\n      <td>0.020798</td>\n      <td>4.042897e-02</td>\n      <td>3.499490e-02</td>\n      <td>1.916664e-02</td>\n      <td>-3.689303e-02</td>\n      <td>0.165058</td>\n      <td>0.000000</td>\n      <td>-7.942645e-03</td>\n      <td>1.609991e-01</td>\n      <td>0.040189</td>\n      <td>0.021010</td>\n      <td>0.096613</td>\n      <td>0.140603</td>\n      <td>-0.002565</td>\n      <td>-2.464139e-03</td>\n      <td>-0.000653</td>\n      <td>1.401012e-02</td>\n      <td>1.991480e-02</td>\n      <td>0.111918</td>\n      <td>7.758909e-02</td>\n      <td>2.575706e-01</td>\n      <td>-2.347001e-02</td>\n      <td>0.010753</td>\n      <td>-3.306792e-02</td>\n      <td>4.912365e-02</td>\n      <td>2.799586e-02</td>\n      <td>0.089636</td>\n      <td>0.105560</td>\n      <td>-0.045981</td>\n      <td>4.852108e-02</td>\n      <td>4.030023e-02</td>\n      <td>7.879168e-02</td>\n      <td>0.346530</td>\n      <td>0.066332</td>\n      <td>1.354293e-01</td>\n      <td>...</td>\n      <td>-3.041150e-02</td>\n      <td>0.021887</td>\n      <td>7.554804e-02</td>\n      <td>1.970504e-01</td>\n      <td>5.682696e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.099287</td>\n      <td>0.000000e+00</td>\n      <td>3.093952e-02</td>\n      <td>0.080800</td>\n      <td>0.065016</td>\n      <td>-1.192797e-03</td>\n      <td>-7.022492e-03</td>\n      <td>8.129503e-02</td>\n      <td>5.330798e-02</td>\n      <td>0.043860</td>\n      <td>3.828751e-02</td>\n      <td>1.000000</td>\n      <td>7.176703e-02</td>\n      <td>0.060012</td>\n      <td>1.628183e-02</td>\n      <td>6.404020e-02</td>\n      <td>0.074981</td>\n      <td>0.034947</td>\n      <td>4.874174e-02</td>\n      <td>0.021635</td>\n      <td>0.008210</td>\n      <td>-9.725819e-02</td>\n      <td>0.048331</td>\n      <td>0.007578</td>\n      <td>0.070824</td>\n      <td>0.225632</td>\n      <td>-0.004282</td>\n      <td>3.361266e-02</td>\n      <td>-4.488794e-02</td>\n      <td>0.059339</td>\n      <td>0.110273</td>\n      <td>0.066268</td>\n      <td>1.774925e-01</td>\n      <td>0.036834</td>\n    </tr>\n    <tr>\n      <th>589</th>\n      <td>-0.059246</td>\n      <td>-3.566801e-02</td>\n      <td>-2.408582e-02</td>\n      <td>-0.008866</td>\n      <td>1.027808e-01</td>\n      <td>0.107467</td>\n      <td>3.480403e-02</td>\n      <td>7.401687e-02</td>\n      <td>-6.910616e-23</td>\n      <td>-2.379638e-02</td>\n      <td>-0.005096</td>\n      <td>0.000905</td>\n      <td>5.950003e-02</td>\n      <td>9.556360e-02</td>\n      <td>0.064274</td>\n      <td>-0.010232</td>\n      <td>0.087877</td>\n      <td>0.061513</td>\n      <td>0.002625</td>\n      <td>-3.286370e-03</td>\n      <td>0.015356</td>\n      <td>4.644809e-02</td>\n      <td>1.340265e-02</td>\n      <td>0.116625</td>\n      <td>1.815763e-02</td>\n      <td>1.017234e-01</td>\n      <td>1.003522e-02</td>\n      <td>0.073196</td>\n      <td>-1.679977e-03</td>\n      <td>1.334364e-02</td>\n      <td>-8.994551e-02</td>\n      <td>0.019176</td>\n      <td>0.100656</td>\n      <td>0.010500</td>\n      <td>2.032223e-03</td>\n      <td>2.598165e-02</td>\n      <td>4.891112e-02</td>\n      <td>0.084325</td>\n      <td>0.030576</td>\n      <td>2.827083e-02</td>\n      <td>...</td>\n      <td>-1.214441e-23</td>\n      <td>0.139100</td>\n      <td>1.960878e-02</td>\n      <td>1.517027e-01</td>\n      <td>4.116299e-02</td>\n      <td>-1.011505e-22</td>\n      <td>0.100137</td>\n      <td>8.044164e-23</td>\n      <td>6.085813e-02</td>\n      <td>0.023576</td>\n      <td>0.074006</td>\n      <td>-8.640104e-04</td>\n      <td>1.648939e-02</td>\n      <td>4.711650e-02</td>\n      <td>2.941821e-02</td>\n      <td>-0.001071</td>\n      <td>4.261103e-02</td>\n      <td>0.071767</td>\n      <td>1.000000e+00</td>\n      <td>0.068300</td>\n      <td>1.375950e-02</td>\n      <td>4.522121e-02</td>\n      <td>0.013927</td>\n      <td>0.013045</td>\n      <td>-9.731895e-03</td>\n      <td>0.011528</td>\n      <td>0.037957</td>\n      <td>-7.044962e-02</td>\n      <td>0.011099</td>\n      <td>0.048774</td>\n      <td>0.040483</td>\n      <td>0.141497</td>\n      <td>0.017695</td>\n      <td>7.659830e-02</td>\n      <td>-2.109893e-02</td>\n      <td>0.027582</td>\n      <td>0.034789</td>\n      <td>0.035153</td>\n      <td>2.082214e-01</td>\n      <td>0.008520</td>\n    </tr>\n    <tr>\n      <th>590</th>\n      <td>0.095191</td>\n      <td>2.702576e-03</td>\n      <td>-2.840994e-02</td>\n      <td>0.087103</td>\n      <td>5.030224e-02</td>\n      <td>0.012956</td>\n      <td>1.290286e-01</td>\n      <td>4.941401e-02</td>\n      <td>5.563656e-02</td>\n      <td>1.159523e-02</td>\n      <td>0.059528</td>\n      <td>0.001730</td>\n      <td>7.215087e-02</td>\n      <td>7.036630e-02</td>\n      <td>0.076337</td>\n      <td>-0.020190</td>\n      <td>0.089391</td>\n      <td>0.218372</td>\n      <td>0.164386</td>\n      <td>1.450238e-01</td>\n      <td>-0.023523</td>\n      <td>3.643345e-02</td>\n      <td>1.230182e-03</td>\n      <td>0.073468</td>\n      <td>9.074274e-02</td>\n      <td>1.083260e-02</td>\n      <td>8.818167e-02</td>\n      <td>0.114570</td>\n      <td>8.205702e-02</td>\n      <td>-1.116743e-02</td>\n      <td>2.548214e-02</td>\n      <td>0.107942</td>\n      <td>0.042469</td>\n      <td>0.019928</td>\n      <td>1.777722e-02</td>\n      <td>3.376259e-02</td>\n      <td>2.059899e-02</td>\n      <td>0.076353</td>\n      <td>0.019647</td>\n      <td>8.645194e-02</td>\n      <td>...</td>\n      <td>2.731913e-02</td>\n      <td>0.133942</td>\n      <td>7.047780e-02</td>\n      <td>2.032138e-03</td>\n      <td>1.932621e-02</td>\n      <td>2.445450e-03</td>\n      <td>0.166301</td>\n      <td>6.438804e-24</td>\n      <td>8.254534e-03</td>\n      <td>-0.011719</td>\n      <td>0.038908</td>\n      <td>1.701189e-02</td>\n      <td>-7.608499e-03</td>\n      <td>7.868814e-02</td>\n      <td>1.219962e-02</td>\n      <td>0.004530</td>\n      <td>1.898974e-02</td>\n      <td>0.060012</td>\n      <td>6.829984e-02</td>\n      <td>1.000000</td>\n      <td>3.370071e-02</td>\n      <td>7.766689e-02</td>\n      <td>0.118547</td>\n      <td>0.010021</td>\n      <td>4.457346e-02</td>\n      <td>0.138577</td>\n      <td>0.142976</td>\n      <td>-1.997830e-02</td>\n      <td>0.194096</td>\n      <td>0.102269</td>\n      <td>0.077673</td>\n      <td>0.087757</td>\n      <td>0.100057</td>\n      <td>3.943524e-02</td>\n      <td>7.572630e-02</td>\n      <td>0.175392</td>\n      <td>0.051402</td>\n      <td>0.084470</td>\n      <td>3.934268e-02</td>\n      <td>0.142449</td>\n    </tr>\n    <tr>\n      <th>591</th>\n      <td>0.038276</td>\n      <td>2.482889e-03</td>\n      <td>3.576149e-25</td>\n      <td>-0.018269</td>\n      <td>-1.363111e-24</td>\n      <td>0.023963</td>\n      <td>2.155651e-02</td>\n      <td>-7.475262e-03</td>\n      <td>7.430191e-03</td>\n      <td>-1.576617e-02</td>\n      <td>-0.001419</td>\n      <td>-0.000331</td>\n      <td>1.335447e-01</td>\n      <td>7.370511e-03</td>\n      <td>0.095505</td>\n      <td>-0.014941</td>\n      <td>0.094689</td>\n      <td>0.023855</td>\n      <td>0.049884</td>\n      <td>3.353346e-02</td>\n      <td>0.022584</td>\n      <td>1.304773e-02</td>\n      <td>-1.896195e-03</td>\n      <td>0.038351</td>\n      <td>6.275972e-02</td>\n      <td>-7.311230e-03</td>\n      <td>5.177299e-02</td>\n      <td>0.043647</td>\n      <td>2.465420e-02</td>\n      <td>5.108759e-02</td>\n      <td>5.426223e-23</td>\n      <td>0.043879</td>\n      <td>0.074291</td>\n      <td>0.006812</td>\n      <td>2.343916e-24</td>\n      <td>2.601751e-04</td>\n      <td>-3.011438e-03</td>\n      <td>0.031861</td>\n      <td>0.052238</td>\n      <td>1.006975e-02</td>\n      <td>...</td>\n      <td>-3.032170e-02</td>\n      <td>0.034498</td>\n      <td>2.674213e-02</td>\n      <td>-1.433456e-23</td>\n      <td>3.743506e-02</td>\n      <td>3.429113e-23</td>\n      <td>0.056565</td>\n      <td>-2.726979e-23</td>\n      <td>8.239975e-02</td>\n      <td>0.023198</td>\n      <td>-0.059272</td>\n      <td>3.977850e-02</td>\n      <td>1.659697e-02</td>\n      <td>7.649857e-03</td>\n      <td>-1.922146e-02</td>\n      <td>0.012155</td>\n      <td>5.246365e-03</td>\n      <td>0.016282</td>\n      <td>1.375950e-02</td>\n      <td>0.033701</td>\n      <td>1.000000e+00</td>\n      <td>-6.363210e-03</td>\n      <td>0.035023</td>\n      <td>0.007842</td>\n      <td>3.742734e-02</td>\n      <td>0.014412</td>\n      <td>0.047852</td>\n      <td>1.294762e-23</td>\n      <td>0.029073</td>\n      <td>-0.002609</td>\n      <td>-0.028835</td>\n      <td>0.000907</td>\n      <td>-0.012417</td>\n      <td>6.019106e-03</td>\n      <td>7.701111e-02</td>\n      <td>-0.006871</td>\n      <td>-0.011590</td>\n      <td>0.028452</td>\n      <td>2.318105e-02</td>\n      <td>0.020651</td>\n    </tr>\n    <tr>\n      <th>592</th>\n      <td>-0.059507</td>\n      <td>-1.031647e-02</td>\n      <td>-3.792476e-25</td>\n      <td>-0.016764</td>\n      <td>-5.476072e-02</td>\n      <td>0.144834</td>\n      <td>5.612566e-02</td>\n      <td>2.556147e-02</td>\n      <td>-2.482392e-23</td>\n      <td>1.940330e-03</td>\n      <td>0.196915</td>\n      <td>0.016718</td>\n      <td>7.263767e-03</td>\n      <td>3.454851e-02</td>\n      <td>0.045352</td>\n      <td>-0.007744</td>\n      <td>0.055450</td>\n      <td>0.006090</td>\n      <td>0.045853</td>\n      <td>4.252218e-03</td>\n      <td>0.063692</td>\n      <td>3.117772e-02</td>\n      <td>8.489739e-03</td>\n      <td>0.018874</td>\n      <td>2.607683e-02</td>\n      <td>8.239422e-02</td>\n      <td>2.945069e-02</td>\n      <td>0.013684</td>\n      <td>1.889268e-04</td>\n      <td>-4.122883e-02</td>\n      <td>5.448690e-02</td>\n      <td>0.059897</td>\n      <td>0.075726</td>\n      <td>-0.035502</td>\n      <td>-8.073389e-04</td>\n      <td>5.384657e-02</td>\n      <td>-3.580530e-02</td>\n      <td>0.001495</td>\n      <td>0.023899</td>\n      <td>8.055360e-02</td>\n      <td>...</td>\n      <td>1.315530e-02</td>\n      <td>-0.007484</td>\n      <td>5.587773e-02</td>\n      <td>1.154938e-01</td>\n      <td>1.105540e-02</td>\n      <td>-3.636225e-23</td>\n      <td>0.034395</td>\n      <td>2.891802e-23</td>\n      <td>1.044388e-01</td>\n      <td>0.015655</td>\n      <td>0.015197</td>\n      <td>-7.913588e-04</td>\n      <td>5.566185e-02</td>\n      <td>5.954190e-02</td>\n      <td>6.336468e-03</td>\n      <td>0.010274</td>\n      <td>2.582805e-02</td>\n      <td>0.064040</td>\n      <td>4.522121e-02</td>\n      <td>0.077667</td>\n      <td>-6.363210e-03</td>\n      <td>1.000000e+00</td>\n      <td>-0.004539</td>\n      <td>0.068633</td>\n      <td>-8.084404e-03</td>\n      <td>0.015931</td>\n      <td>-0.001927</td>\n      <td>-6.452576e-02</td>\n      <td>0.056840</td>\n      <td>-0.010853</td>\n      <td>0.005436</td>\n      <td>0.116998</td>\n      <td>-0.017932</td>\n      <td>1.364349e-03</td>\n      <td>2.794843e-02</td>\n      <td>0.002674</td>\n      <td>0.058910</td>\n      <td>0.011246</td>\n      <td>4.307486e-02</td>\n      <td>0.028104</td>\n    </tr>\n    <tr>\n      <th>593</th>\n      <td>0.082403</td>\n      <td>-2.612039e-02</td>\n      <td>1.091954e-02</td>\n      <td>0.057924</td>\n      <td>4.175486e-02</td>\n      <td>-0.017848</td>\n      <td>6.563430e-02</td>\n      <td>5.215305e-02</td>\n      <td>3.921935e-02</td>\n      <td>1.549868e-02</td>\n      <td>0.034674</td>\n      <td>0.050658</td>\n      <td>4.534522e-02</td>\n      <td>3.182765e-02</td>\n      <td>0.093685</td>\n      <td>-0.124402</td>\n      <td>0.169074</td>\n      <td>0.114465</td>\n      <td>0.016781</td>\n      <td>4.709864e-03</td>\n      <td>-0.059050</td>\n      <td>2.412814e-02</td>\n      <td>-8.394365e-03</td>\n      <td>0.044944</td>\n      <td>8.677418e-02</td>\n      <td>2.252911e-02</td>\n      <td>3.120218e-02</td>\n      <td>0.063942</td>\n      <td>3.982577e-02</td>\n      <td>6.115464e-02</td>\n      <td>1.794910e-02</td>\n      <td>0.109311</td>\n      <td>0.018267</td>\n      <td>-0.000970</td>\n      <td>6.569348e-03</td>\n      <td>2.337285e-02</td>\n      <td>-4.333658e-02</td>\n      <td>0.031255</td>\n      <td>0.042090</td>\n      <td>4.213167e-02</td>\n      <td>...</td>\n      <td>5.338483e-03</td>\n      <td>0.122230</td>\n      <td>-1.961040e-02</td>\n      <td>-1.427195e-02</td>\n      <td>-8.122698e-03</td>\n      <td>-5.444690e-02</td>\n      <td>0.112533</td>\n      <td>5.160462e-23</td>\n      <td>1.187976e-02</td>\n      <td>0.030997</td>\n      <td>0.052924</td>\n      <td>-3.524260e-02</td>\n      <td>9.167737e-03</td>\n      <td>4.609024e-02</td>\n      <td>3.941327e-02</td>\n      <td>-0.038886</td>\n      <td>6.295819e-02</td>\n      <td>0.074981</td>\n      <td>1.392725e-02</td>\n      <td>0.118547</td>\n      <td>3.502296e-02</td>\n      <td>-4.538837e-03</td>\n      <td>1.000000</td>\n      <td>0.007719</td>\n      <td>6.877947e-02</td>\n      <td>0.034844</td>\n      <td>0.044112</td>\n      <td>-1.224503e-02</td>\n      <td>0.051177</td>\n      <td>0.048809</td>\n      <td>0.030923</td>\n      <td>0.077330</td>\n      <td>0.045507</td>\n      <td>5.807951e-02</td>\n      <td>3.880695e-03</td>\n      <td>0.099880</td>\n      <td>-0.059337</td>\n      <td>0.029299</td>\n      <td>6.486151e-02</td>\n      <td>0.033001</td>\n    </tr>\n    <tr>\n      <th>594</th>\n      <td>-0.006195</td>\n      <td>3.381087e-04</td>\n      <td>-7.822934e-03</td>\n      <td>-0.007381</td>\n      <td>7.495573e-03</td>\n      <td>0.052900</td>\n      <td>2.787376e-02</td>\n      <td>-5.331690e-02</td>\n      <td>3.162818e-04</td>\n      <td>-9.615981e-04</td>\n      <td>-0.006758</td>\n      <td>-0.005465</td>\n      <td>1.751679e-02</td>\n      <td>2.730632e-03</td>\n      <td>0.011749</td>\n      <td>-0.027261</td>\n      <td>0.029361</td>\n      <td>-0.009535</td>\n      <td>0.007658</td>\n      <td>5.659948e-03</td>\n      <td>0.018500</td>\n      <td>-1.510430e-02</td>\n      <td>-1.226339e-02</td>\n      <td>0.004705</td>\n      <td>8.546339e-03</td>\n      <td>1.329523e-02</td>\n      <td>3.024039e-03</td>\n      <td>-0.006811</td>\n      <td>4.760250e-03</td>\n      <td>-4.351693e-02</td>\n      <td>1.912237e-02</td>\n      <td>0.047331</td>\n      <td>0.031415</td>\n      <td>-0.029335</td>\n      <td>-1.742472e-02</td>\n      <td>-1.451209e-02</td>\n      <td>-1.122197e-02</td>\n      <td>0.036887</td>\n      <td>0.019165</td>\n      <td>-2.506364e-03</td>\n      <td>...</td>\n      <td>-6.290486e-02</td>\n      <td>-0.016507</td>\n      <td>1.260991e-02</td>\n      <td>2.670533e-02</td>\n      <td>6.556875e-03</td>\n      <td>-9.646390e-03</td>\n      <td>0.004037</td>\n      <td>7.235276e-02</td>\n      <td>3.826444e-02</td>\n      <td>0.001736</td>\n      <td>-0.015143</td>\n      <td>9.915435e-03</td>\n      <td>1.500660e-02</td>\n      <td>4.954854e-02</td>\n      <td>6.701806e-04</td>\n      <td>0.004425</td>\n      <td>1.061201e-02</td>\n      <td>0.034947</td>\n      <td>1.304549e-02</td>\n      <td>0.010021</td>\n      <td>7.841941e-03</td>\n      <td>6.863330e-02</td>\n      <td>0.007719</td>\n      <td>1.000000</td>\n      <td>-1.699989e-02</td>\n      <td>-0.001510</td>\n      <td>-0.026121</td>\n      <td>-2.288162e-02</td>\n      <td>-0.002863</td>\n      <td>-0.031434</td>\n      <td>0.006986</td>\n      <td>0.027729</td>\n      <td>-0.028177</td>\n      <td>-2.229867e-02</td>\n      <td>-9.609319e-05</td>\n      <td>-0.024605</td>\n      <td>-0.002425</td>\n      <td>-0.029827</td>\n      <td>-6.963011e-02</td>\n      <td>-0.002394</td>\n    </tr>\n    <tr>\n      <th>595</th>\n      <td>0.036529</td>\n      <td>-4.207526e-23</td>\n      <td>3.916421e-25</td>\n      <td>0.009840</td>\n      <td>-9.246321e-03</td>\n      <td>-0.056440</td>\n      <td>1.775516e-02</td>\n      <td>4.759797e-02</td>\n      <td>2.557875e-23</td>\n      <td>1.106440e-23</td>\n      <td>-0.001625</td>\n      <td>0.000000</td>\n      <td>1.171442e-23</td>\n      <td>5.021591e-02</td>\n      <td>-0.000218</td>\n      <td>0.065087</td>\n      <td>0.014559</td>\n      <td>0.018689</td>\n      <td>0.015767</td>\n      <td>2.822777e-02</td>\n      <td>-0.002553</td>\n      <td>3.395412e-24</td>\n      <td>8.785065e-04</td>\n      <td>0.015137</td>\n      <td>-9.320653e-23</td>\n      <td>-2.474082e-23</td>\n      <td>-4.042528e-03</td>\n      <td>-0.007552</td>\n      <td>-4.482113e-02</td>\n      <td>-1.147570e-22</td>\n      <td>3.311449e-02</td>\n      <td>0.070069</td>\n      <td>0.072931</td>\n      <td>0.001484</td>\n      <td>4.761749e-02</td>\n      <td>4.339403e-03</td>\n      <td>-1.888673e-23</td>\n      <td>0.013449</td>\n      <td>0.026243</td>\n      <td>9.765786e-03</td>\n      <td>...</td>\n      <td>2.491276e-02</td>\n      <td>-0.002173</td>\n      <td>2.419001e-02</td>\n      <td>-1.565698e-23</td>\n      <td>-2.655185e-23</td>\n      <td>3.746468e-23</td>\n      <td>0.010816</td>\n      <td>-2.977957e-23</td>\n      <td>-4.234415e-02</td>\n      <td>0.019773</td>\n      <td>0.000000</td>\n      <td>1.903176e-23</td>\n      <td>4.857129e-23</td>\n      <td>3.053116e-23</td>\n      <td>-1.061504e-23</td>\n      <td>-0.003912</td>\n      <td>-2.223676e-17</td>\n      <td>0.048742</td>\n      <td>-9.731895e-03</td>\n      <td>0.044573</td>\n      <td>3.742734e-02</td>\n      <td>-8.084404e-03</td>\n      <td>0.068779</td>\n      <td>-0.017000</td>\n      <td>1.000000e+00</td>\n      <td>0.021680</td>\n      <td>0.109613</td>\n      <td>1.414785e-23</td>\n      <td>0.011447</td>\n      <td>0.063954</td>\n      <td>0.027797</td>\n      <td>-0.011453</td>\n      <td>0.083929</td>\n      <td>1.544483e-02</td>\n      <td>-1.312758e-03</td>\n      <td>0.011642</td>\n      <td>0.005677</td>\n      <td>0.016516</td>\n      <td>3.974119e-23</td>\n      <td>0.009556</td>\n    </tr>\n    <tr>\n      <th>596</th>\n      <td>0.035349</td>\n      <td>3.126313e-02</td>\n      <td>1.837690e-02</td>\n      <td>-0.001616</td>\n      <td>2.064108e-02</td>\n      <td>0.013312</td>\n      <td>1.075566e-01</td>\n      <td>4.278900e-02</td>\n      <td>-8.878356e-03</td>\n      <td>-2.893804e-02</td>\n      <td>0.051866</td>\n      <td>0.008394</td>\n      <td>2.459547e-02</td>\n      <td>2.532094e-02</td>\n      <td>-0.023045</td>\n      <td>0.026997</td>\n      <td>0.042188</td>\n      <td>0.066303</td>\n      <td>0.091810</td>\n      <td>6.806793e-02</td>\n      <td>-0.040467</td>\n      <td>-3.080933e-02</td>\n      <td>1.600379e-02</td>\n      <td>0.032042</td>\n      <td>8.345520e-03</td>\n      <td>1.301269e-02</td>\n      <td>2.982327e-02</td>\n      <td>0.044200</td>\n      <td>-1.842820e-03</td>\n      <td>3.625914e-02</td>\n      <td>1.772626e-02</td>\n      <td>0.029070</td>\n      <td>0.040571</td>\n      <td>0.037028</td>\n      <td>-1.851990e-02</td>\n      <td>2.338949e-02</td>\n      <td>3.762238e-02</td>\n      <td>0.010963</td>\n      <td>0.013924</td>\n      <td>1.451401e-02</td>\n      <td>...</td>\n      <td>-4.842317e-03</td>\n      <td>0.025203</td>\n      <td>1.450307e-01</td>\n      <td>2.431777e-02</td>\n      <td>4.221648e-03</td>\n      <td>-2.701428e-02</td>\n      <td>0.012191</td>\n      <td>1.601141e-04</td>\n      <td>2.692805e-02</td>\n      <td>0.045530</td>\n      <td>-0.012036</td>\n      <td>2.872779e-02</td>\n      <td>1.410262e-02</td>\n      <td>3.143411e-03</td>\n      <td>-4.791984e-02</td>\n      <td>0.042399</td>\n      <td>1.283793e-02</td>\n      <td>0.021635</td>\n      <td>1.152842e-02</td>\n      <td>0.138577</td>\n      <td>1.441235e-02</td>\n      <td>1.593083e-02</td>\n      <td>0.034844</td>\n      <td>-0.001510</td>\n      <td>2.168011e-02</td>\n      <td>1.000000</td>\n      <td>0.041155</td>\n      <td>-5.056019e-02</td>\n      <td>0.091562</td>\n      <td>0.087523</td>\n      <td>0.066026</td>\n      <td>0.003527</td>\n      <td>0.034815</td>\n      <td>1.240774e-02</td>\n      <td>6.883439e-02</td>\n      <td>0.053692</td>\n      <td>0.027025</td>\n      <td>0.032732</td>\n      <td>3.605659e-03</td>\n      <td>0.130250</td>\n    </tr>\n    <tr>\n      <th>597</th>\n      <td>0.102631</td>\n      <td>6.004722e-03</td>\n      <td>-2.066180e-02</td>\n      <td>0.044671</td>\n      <td>3.051674e-02</td>\n      <td>-0.029519</td>\n      <td>2.068865e-02</td>\n      <td>2.400432e-02</td>\n      <td>2.058321e-02</td>\n      <td>-1.796459e-02</td>\n      <td>0.079575</td>\n      <td>-0.008951</td>\n      <td>2.513574e-02</td>\n      <td>1.297831e-02</td>\n      <td>0.049969</td>\n      <td>-0.008551</td>\n      <td>0.083246</td>\n      <td>0.076056</td>\n      <td>0.115589</td>\n      <td>3.172197e-02</td>\n      <td>-0.013875</td>\n      <td>1.030110e-02</td>\n      <td>-1.110004e-03</td>\n      <td>0.065708</td>\n      <td>1.422944e-02</td>\n      <td>7.167399e-02</td>\n      <td>1.160754e-02</td>\n      <td>0.049987</td>\n      <td>4.770322e-02</td>\n      <td>-2.079756e-02</td>\n      <td>-2.578105e-02</td>\n      <td>0.081383</td>\n      <td>0.089662</td>\n      <td>-0.005941</td>\n      <td>1.293150e-02</td>\n      <td>4.824366e-02</td>\n      <td>5.602623e-02</td>\n      <td>0.038675</td>\n      <td>0.048270</td>\n      <td>2.830185e-03</td>\n      <td>...</td>\n      <td>4.732424e-02</td>\n      <td>0.108358</td>\n      <td>5.820434e-02</td>\n      <td>1.890359e-02</td>\n      <td>2.627359e-02</td>\n      <td>-2.852757e-02</td>\n      <td>0.110797</td>\n      <td>-3.628913e-24</td>\n      <td>-1.166187e-02</td>\n      <td>0.031405</td>\n      <td>-0.001947</td>\n      <td>-5.000065e-04</td>\n      <td>-1.345463e-02</td>\n      <td>5.817048e-03</td>\n      <td>1.171912e-02</td>\n      <td>-0.010516</td>\n      <td>4.073837e-02</td>\n      <td>0.008210</td>\n      <td>3.795744e-02</td>\n      <td>0.142976</td>\n      <td>4.785247e-02</td>\n      <td>-1.927203e-03</td>\n      <td>0.044112</td>\n      <td>-0.026121</td>\n      <td>1.096132e-01</td>\n      <td>0.041155</td>\n      <td>1.000000</td>\n      <td>-8.065908e-03</td>\n      <td>0.122024</td>\n      <td>0.042922</td>\n      <td>0.031452</td>\n      <td>0.042246</td>\n      <td>0.120366</td>\n      <td>-1.102725e-02</td>\n      <td>3.055046e-02</td>\n      <td>0.089620</td>\n      <td>0.044123</td>\n      <td>0.044470</td>\n      <td>3.021940e-02</td>\n      <td>0.053947</td>\n    </tr>\n    <tr>\n      <th>598</th>\n      <td>0.004179</td>\n      <td>1.917654e-03</td>\n      <td>2.830972e-25</td>\n      <td>-0.015857</td>\n      <td>-1.082644e-24</td>\n      <td>-0.014783</td>\n      <td>-5.784777e-02</td>\n      <td>-2.835102e-02</td>\n      <td>1.859309e-23</td>\n      <td>-3.034223e-02</td>\n      <td>-0.072245</td>\n      <td>0.000000</td>\n      <td>8.512745e-24</td>\n      <td>-3.773755e-02</td>\n      <td>0.095703</td>\n      <td>-0.031050</td>\n      <td>0.023099</td>\n      <td>-0.037690</td>\n      <td>-0.016930</td>\n      <td>-2.007013e-02</td>\n      <td>-0.016150</td>\n      <td>-5.108802e-02</td>\n      <td>-4.716189e-02</td>\n      <td>-0.065664</td>\n      <td>1.615745e-02</td>\n      <td>-1.197889e-01</td>\n      <td>-2.051078e-02</td>\n      <td>-0.007878</td>\n      <td>-4.603576e-25</td>\n      <td>1.315247e-02</td>\n      <td>-2.663845e-02</td>\n      <td>-0.090694</td>\n      <td>-0.041066</td>\n      <td>-0.034677</td>\n      <td>1.864604e-24</td>\n      <td>-2.257560e-23</td>\n      <td>-9.251270e-02</td>\n      <td>0.010369</td>\n      <td>-0.058468</td>\n      <td>-1.805581e-23</td>\n      <td>...</td>\n      <td>3.266342e-24</td>\n      <td>0.004172</td>\n      <td>1.549075e-02</td>\n      <td>-1.053009e-01</td>\n      <td>-1.929678e-23</td>\n      <td>2.726373e-23</td>\n      <td>-0.008556</td>\n      <td>-2.165808e-23</td>\n      <td>-1.358629e-23</td>\n      <td>-0.059629</td>\n      <td>0.041646</td>\n      <td>3.446837e-02</td>\n      <td>6.885209e-02</td>\n      <td>4.763654e-03</td>\n      <td>-1.177095e-02</td>\n      <td>-0.001103</td>\n      <td>1.411257e-23</td>\n      <td>-0.097258</td>\n      <td>-7.044962e-02</td>\n      <td>-0.019978</td>\n      <td>1.294762e-23</td>\n      <td>-6.452576e-02</td>\n      <td>-0.012245</td>\n      <td>-0.022882</td>\n      <td>1.414785e-23</td>\n      <td>-0.050560</td>\n      <td>-0.008066</td>\n      <td>1.000000e+00</td>\n      <td>-0.018091</td>\n      <td>-0.023527</td>\n      <td>-0.045710</td>\n      <td>-0.062377</td>\n      <td>-0.031738</td>\n      <td>-9.008414e-02</td>\n      <td>-2.697714e-02</td>\n      <td>-0.023366</td>\n      <td>-0.040498</td>\n      <td>-0.052905</td>\n      <td>2.892179e-23</td>\n      <td>-0.008280</td>\n    </tr>\n    <tr>\n      <th>599</th>\n      <td>0.052614</td>\n      <td>-2.129451e-04</td>\n      <td>2.234260e-04</td>\n      <td>0.014862</td>\n      <td>1.274779e-02</td>\n      <td>0.011388</td>\n      <td>5.693041e-02</td>\n      <td>2.550067e-02</td>\n      <td>2.965369e-02</td>\n      <td>-3.504578e-02</td>\n      <td>0.050224</td>\n      <td>0.000202</td>\n      <td>5.246509e-02</td>\n      <td>-4.617436e-03</td>\n      <td>0.060774</td>\n      <td>-0.008643</td>\n      <td>0.047476</td>\n      <td>0.126159</td>\n      <td>0.157420</td>\n      <td>6.027080e-02</td>\n      <td>-0.025060</td>\n      <td>2.363530e-02</td>\n      <td>2.576191e-02</td>\n      <td>0.060016</td>\n      <td>1.065334e-02</td>\n      <td>4.349723e-02</td>\n      <td>1.725596e-03</td>\n      <td>0.071202</td>\n      <td>4.152703e-02</td>\n      <td>-1.660661e-02</td>\n      <td>-3.742821e-03</td>\n      <td>0.064131</td>\n      <td>0.030817</td>\n      <td>0.037113</td>\n      <td>-5.245782e-03</td>\n      <td>9.346262e-03</td>\n      <td>2.098259e-03</td>\n      <td>0.028256</td>\n      <td>0.054391</td>\n      <td>3.372247e-02</td>\n      <td>...</td>\n      <td>2.937034e-02</td>\n      <td>0.031871</td>\n      <td>6.758493e-02</td>\n      <td>1.495899e-02</td>\n      <td>2.364647e-02</td>\n      <td>2.469808e-03</td>\n      <td>0.017315</td>\n      <td>1.008753e-02</td>\n      <td>4.026800e-02</td>\n      <td>0.110112</td>\n      <td>-0.020311</td>\n      <td>6.880803e-03</td>\n      <td>1.171004e-02</td>\n      <td>6.246108e-03</td>\n      <td>3.706989e-02</td>\n      <td>0.000576</td>\n      <td>3.652444e-02</td>\n      <td>0.048331</td>\n      <td>1.109942e-02</td>\n      <td>0.194096</td>\n      <td>2.907302e-02</td>\n      <td>5.683999e-02</td>\n      <td>0.051177</td>\n      <td>-0.002863</td>\n      <td>1.144652e-02</td>\n      <td>0.091562</td>\n      <td>0.122024</td>\n      <td>-1.809112e-02</td>\n      <td>1.000000</td>\n      <td>0.110105</td>\n      <td>0.023424</td>\n      <td>0.051949</td>\n      <td>0.072917</td>\n      <td>5.169169e-03</td>\n      <td>4.514527e-02</td>\n      <td>0.098601</td>\n      <td>-0.008380</td>\n      <td>0.163429</td>\n      <td>2.847442e-02</td>\n      <td>0.205007</td>\n    </tr>\n    <tr>\n      <th>600</th>\n      <td>0.069528</td>\n      <td>-9.797327e-04</td>\n      <td>-4.668597e-03</td>\n      <td>0.023748</td>\n      <td>2.143515e-02</td>\n      <td>0.016769</td>\n      <td>7.363877e-02</td>\n      <td>-4.654827e-04</td>\n      <td>7.673555e-02</td>\n      <td>-1.580218e-02</td>\n      <td>-0.004012</td>\n      <td>0.008060</td>\n      <td>1.965422e-02</td>\n      <td>2.715700e-02</td>\n      <td>0.020008</td>\n      <td>0.035247</td>\n      <td>0.076654</td>\n      <td>0.083236</td>\n      <td>0.129392</td>\n      <td>8.954556e-02</td>\n      <td>-0.051094</td>\n      <td>2.924223e-02</td>\n      <td>1.765388e-02</td>\n      <td>0.018555</td>\n      <td>2.221571e-02</td>\n      <td>4.368813e-02</td>\n      <td>1.373951e-02</td>\n      <td>-0.028752</td>\n      <td>7.671959e-03</td>\n      <td>4.922007e-02</td>\n      <td>2.855381e-02</td>\n      <td>0.070179</td>\n      <td>0.020687</td>\n      <td>0.039760</td>\n      <td>-1.092986e-02</td>\n      <td>7.641807e-04</td>\n      <td>3.465767e-03</td>\n      <td>-0.006751</td>\n      <td>0.020855</td>\n      <td>-6.689286e-03</td>\n      <td>...</td>\n      <td>3.315335e-02</td>\n      <td>0.042311</td>\n      <td>5.560248e-02</td>\n      <td>1.125356e-02</td>\n      <td>3.634840e-02</td>\n      <td>1.810868e-02</td>\n      <td>0.008740</td>\n      <td>1.419912e-02</td>\n      <td>-6.938897e-03</td>\n      <td>0.047354</td>\n      <td>0.011509</td>\n      <td>1.707992e-03</td>\n      <td>-3.873070e-03</td>\n      <td>-3.261924e-02</td>\n      <td>1.554689e-02</td>\n      <td>0.019261</td>\n      <td>2.014136e-02</td>\n      <td>0.007578</td>\n      <td>4.877406e-02</td>\n      <td>0.102269</td>\n      <td>-2.608946e-03</td>\n      <td>-1.085255e-02</td>\n      <td>0.048809</td>\n      <td>-0.031434</td>\n      <td>6.395367e-02</td>\n      <td>0.087523</td>\n      <td>0.042922</td>\n      <td>-2.352728e-02</td>\n      <td>0.110105</td>\n      <td>1.000000</td>\n      <td>0.082663</td>\n      <td>0.034540</td>\n      <td>0.035915</td>\n      <td>4.014901e-02</td>\n      <td>4.332832e-02</td>\n      <td>0.087073</td>\n      <td>-0.046159</td>\n      <td>0.043206</td>\n      <td>7.313385e-02</td>\n      <td>0.080934</td>\n    </tr>\n    <tr>\n      <th>601</th>\n      <td>0.018127</td>\n      <td>-5.055136e-02</td>\n      <td>-4.904202e-03</td>\n      <td>-0.037687</td>\n      <td>1.596401e-02</td>\n      <td>-0.015571</td>\n      <td>-3.707982e-02</td>\n      <td>7.582634e-02</td>\n      <td>2.426672e-02</td>\n      <td>-9.992805e-02</td>\n      <td>-0.026649</td>\n      <td>-0.014097</td>\n      <td>-1.736352e-02</td>\n      <td>-3.555161e-02</td>\n      <td>0.015158</td>\n      <td>-0.019244</td>\n      <td>0.084115</td>\n      <td>0.082769</td>\n      <td>0.017747</td>\n      <td>-1.406056e-03</td>\n      <td>-0.030766</td>\n      <td>-1.727842e-02</td>\n      <td>1.336632e-02</td>\n      <td>0.065139</td>\n      <td>1.073848e-01</td>\n      <td>-2.551661e-02</td>\n      <td>-2.780894e-02</td>\n      <td>0.033904</td>\n      <td>3.247194e-02</td>\n      <td>9.355356e-02</td>\n      <td>1.815736e-03</td>\n      <td>0.040396</td>\n      <td>0.049062</td>\n      <td>-0.012062</td>\n      <td>2.729968e-02</td>\n      <td>6.270101e-02</td>\n      <td>-4.430127e-03</td>\n      <td>0.073076</td>\n      <td>0.036109</td>\n      <td>3.831106e-03</td>\n      <td>...</td>\n      <td>8.566324e-24</td>\n      <td>0.049570</td>\n      <td>4.226791e-02</td>\n      <td>7.824589e-03</td>\n      <td>1.473085e-02</td>\n      <td>7.144376e-23</td>\n      <td>0.045977</td>\n      <td>-1.703471e-02</td>\n      <td>-1.102939e-02</td>\n      <td>0.046226</td>\n      <td>0.057718</td>\n      <td>4.954810e-02</td>\n      <td>7.436704e-03</td>\n      <td>-1.668653e-02</td>\n      <td>-1.615112e-03</td>\n      <td>0.039884</td>\n      <td>5.535617e-03</td>\n      <td>0.070824</td>\n      <td>4.048319e-02</td>\n      <td>0.077673</td>\n      <td>-2.883515e-02</td>\n      <td>5.436409e-03</td>\n      <td>0.030923</td>\n      <td>0.006986</td>\n      <td>2.779695e-02</td>\n      <td>0.066026</td>\n      <td>0.031452</td>\n      <td>-4.570978e-02</td>\n      <td>0.023424</td>\n      <td>0.082663</td>\n      <td>1.000000</td>\n      <td>0.040479</td>\n      <td>0.020511</td>\n      <td>6.059719e-03</td>\n      <td>6.429184e-02</td>\n      <td>0.053683</td>\n      <td>0.049059</td>\n      <td>0.069198</td>\n      <td>4.346543e-02</td>\n      <td>0.021603</td>\n    </tr>\n    <tr>\n      <th>602</th>\n      <td>-0.017172</td>\n      <td>-3.158078e-02</td>\n      <td>-1.611720e-02</td>\n      <td>0.063122</td>\n      <td>1.242680e-02</td>\n      <td>-0.002229</td>\n      <td>5.074589e-02</td>\n      <td>1.252818e-01</td>\n      <td>1.059646e-03</td>\n      <td>-2.585616e-02</td>\n      <td>0.086690</td>\n      <td>-0.025518</td>\n      <td>5.036910e-02</td>\n      <td>1.043054e-02</td>\n      <td>0.082104</td>\n      <td>-0.038124</td>\n      <td>0.092969</td>\n      <td>0.094587</td>\n      <td>0.043580</td>\n      <td>9.081020e-03</td>\n      <td>0.017155</td>\n      <td>1.258627e-02</td>\n      <td>-1.255916e-02</td>\n      <td>0.046402</td>\n      <td>8.183963e-02</td>\n      <td>2.498303e-01</td>\n      <td>-1.129088e-02</td>\n      <td>0.019269</td>\n      <td>-2.196803e-02</td>\n      <td>-1.969097e-02</td>\n      <td>2.814318e-02</td>\n      <td>0.079333</td>\n      <td>0.097870</td>\n      <td>-0.040117</td>\n      <td>5.228061e-03</td>\n      <td>3.427021e-02</td>\n      <td>3.154594e-02</td>\n      <td>0.231396</td>\n      <td>0.057451</td>\n      <td>1.197821e-01</td>\n      <td>...</td>\n      <td>-6.669272e-03</td>\n      <td>0.021815</td>\n      <td>2.638916e-02</td>\n      <td>-1.868502e-02</td>\n      <td>3.644608e-02</td>\n      <td>-8.110146e-03</td>\n      <td>0.031135</td>\n      <td>4.076973e-23</td>\n      <td>6.645900e-02</td>\n      <td>0.052896</td>\n      <td>0.058114</td>\n      <td>-1.530005e-03</td>\n      <td>3.421552e-02</td>\n      <td>9.726221e-02</td>\n      <td>8.165805e-03</td>\n      <td>0.006939</td>\n      <td>2.870276e-02</td>\n      <td>0.225632</td>\n      <td>1.414972e-01</td>\n      <td>0.087757</td>\n      <td>9.069069e-04</td>\n      <td>1.169984e-01</td>\n      <td>0.077330</td>\n      <td>0.027729</td>\n      <td>-1.145262e-02</td>\n      <td>0.003527</td>\n      <td>0.042246</td>\n      <td>-6.237673e-02</td>\n      <td>0.051949</td>\n      <td>0.034540</td>\n      <td>0.040479</td>\n      <td>1.000000</td>\n      <td>-0.014875</td>\n      <td>1.056909e-01</td>\n      <td>2.764016e-02</td>\n      <td>0.016384</td>\n      <td>0.038197</td>\n      <td>0.051388</td>\n      <td>6.239957e-02</td>\n      <td>0.030061</td>\n    </tr>\n    <tr>\n      <th>603</th>\n      <td>-0.015221</td>\n      <td>-1.688095e-03</td>\n      <td>1.774872e-02</td>\n      <td>0.027640</td>\n      <td>2.707632e-02</td>\n      <td>-0.014805</td>\n      <td>3.684593e-02</td>\n      <td>2.536171e-02</td>\n      <td>8.299559e-03</td>\n      <td>-3.476548e-02</td>\n      <td>-0.026121</td>\n      <td>-0.020093</td>\n      <td>6.813748e-02</td>\n      <td>1.515730e-02</td>\n      <td>-0.001537</td>\n      <td>0.015177</td>\n      <td>0.034110</td>\n      <td>0.070622</td>\n      <td>0.026347</td>\n      <td>1.600252e-02</td>\n      <td>-0.058559</td>\n      <td>1.842642e-02</td>\n      <td>7.056236e-03</td>\n      <td>0.023492</td>\n      <td>1.141148e-02</td>\n      <td>1.960123e-02</td>\n      <td>-4.749250e-02</td>\n      <td>0.008016</td>\n      <td>4.463735e-02</td>\n      <td>-1.984695e-02</td>\n      <td>3.556371e-03</td>\n      <td>0.031100</td>\n      <td>0.063568</td>\n      <td>0.017607</td>\n      <td>-1.848925e-02</td>\n      <td>4.717368e-03</td>\n      <td>7.099918e-02</td>\n      <td>-0.039887</td>\n      <td>0.019875</td>\n      <td>-8.276798e-03</td>\n      <td>...</td>\n      <td>-9.850812e-03</td>\n      <td>0.061014</td>\n      <td>1.734467e-02</td>\n      <td>1.757119e-02</td>\n      <td>1.569181e-03</td>\n      <td>1.466902e-02</td>\n      <td>0.014211</td>\n      <td>-5.780289e-24</td>\n      <td>6.481440e-04</td>\n      <td>0.005520</td>\n      <td>-0.007148</td>\n      <td>7.723535e-03</td>\n      <td>1.596503e-02</td>\n      <td>-6.898183e-03</td>\n      <td>2.701689e-03</td>\n      <td>-0.030866</td>\n      <td>-1.997288e-02</td>\n      <td>-0.004282</td>\n      <td>1.769539e-02</td>\n      <td>0.100057</td>\n      <td>-1.241700e-02</td>\n      <td>-1.793196e-02</td>\n      <td>0.045507</td>\n      <td>-0.028177</td>\n      <td>8.392932e-02</td>\n      <td>0.034815</td>\n      <td>0.120366</td>\n      <td>-3.173807e-02</td>\n      <td>0.072917</td>\n      <td>0.035915</td>\n      <td>0.020511</td>\n      <td>-0.014875</td>\n      <td>1.000000</td>\n      <td>2.458937e-02</td>\n      <td>-1.946200e-02</td>\n      <td>0.098011</td>\n      <td>0.049317</td>\n      <td>0.012801</td>\n      <td>1.533445e-02</td>\n      <td>0.051255</td>\n    </tr>\n    <tr>\n      <th>604</th>\n      <td>-0.037059</td>\n      <td>-3.345291e-23</td>\n      <td>3.085290e-25</td>\n      <td>-0.013782</td>\n      <td>1.246135e-02</td>\n      <td>-0.033564</td>\n      <td>2.024890e-02</td>\n      <td>1.894854e-01</td>\n      <td>6.073345e-03</td>\n      <td>-3.707362e-02</td>\n      <td>-0.001843</td>\n      <td>-0.025715</td>\n      <td>9.315813e-24</td>\n      <td>2.249223e-01</td>\n      <td>0.039493</td>\n      <td>-0.047917</td>\n      <td>0.045440</td>\n      <td>0.021771</td>\n      <td>0.017702</td>\n      <td>-1.674970e-02</td>\n      <td>0.024183</td>\n      <td>1.746862e-02</td>\n      <td>-1.490645e-02</td>\n      <td>0.022829</td>\n      <td>-7.411563e-23</td>\n      <td>1.709167e-01</td>\n      <td>1.541072e-02</td>\n      <td>-0.000886</td>\n      <td>-1.265272e-02</td>\n      <td>-5.388175e-03</td>\n      <td>2.400612e-02</td>\n      <td>0.065951</td>\n      <td>0.026424</td>\n      <td>0.015109</td>\n      <td>7.984313e-02</td>\n      <td>-1.852538e-02</td>\n      <td>1.268625e-01</td>\n      <td>-0.055935</td>\n      <td>0.032410</td>\n      <td>2.278375e-01</td>\n      <td>...</td>\n      <td>4.578432e-02</td>\n      <td>0.008348</td>\n      <td>1.702640e-02</td>\n      <td>-1.266185e-01</td>\n      <td>5.263524e-02</td>\n      <td>-2.027905e-02</td>\n      <td>-0.016724</td>\n      <td>-2.369515e-23</td>\n      <td>-7.472489e-03</td>\n      <td>-0.001987</td>\n      <td>0.000000</td>\n      <td>-1.104813e-03</td>\n      <td>-3.576897e-02</td>\n      <td>4.625324e-02</td>\n      <td>3.526485e-02</td>\n      <td>-0.014630</td>\n      <td>3.978664e-02</td>\n      <td>0.033613</td>\n      <td>7.659830e-02</td>\n      <td>0.039435</td>\n      <td>6.019106e-03</td>\n      <td>1.364349e-03</td>\n      <td>0.058080</td>\n      <td>-0.022299</td>\n      <td>1.544483e-02</td>\n      <td>0.012408</td>\n      <td>-0.011027</td>\n      <td>-9.008414e-02</td>\n      <td>0.005169</td>\n      <td>0.040149</td>\n      <td>0.006060</td>\n      <td>0.105691</td>\n      <td>0.024589</td>\n      <td>1.000000e+00</td>\n      <td>-2.687098e-02</td>\n      <td>0.061078</td>\n      <td>0.002355</td>\n      <td>0.006319</td>\n      <td>9.403771e-02</td>\n      <td>0.015621</td>\n    </tr>\n    <tr>\n      <th>605</th>\n      <td>-0.029121</td>\n      <td>-3.088913e-23</td>\n      <td>-1.430628e-03</td>\n      <td>0.040037</td>\n      <td>-3.627206e-02</td>\n      <td>0.016815</td>\n      <td>1.086215e-01</td>\n      <td>-2.122042e-02</td>\n      <td>7.340770e-02</td>\n      <td>2.778959e-02</td>\n      <td>0.015392</td>\n      <td>0.023402</td>\n      <td>-7.799120e-04</td>\n      <td>-1.929689e-02</td>\n      <td>0.031208</td>\n      <td>-0.018452</td>\n      <td>0.014480</td>\n      <td>0.011316</td>\n      <td>0.052647</td>\n      <td>1.486656e-02</td>\n      <td>0.008466</td>\n      <td>-2.674431e-02</td>\n      <td>1.681381e-03</td>\n      <td>-0.010129</td>\n      <td>6.026395e-02</td>\n      <td>-4.704243e-02</td>\n      <td>3.837496e-02</td>\n      <td>0.012725</td>\n      <td>7.748637e-03</td>\n      <td>5.260133e-02</td>\n      <td>3.794461e-02</td>\n      <td>0.048414</td>\n      <td>-0.017848</td>\n      <td>0.016758</td>\n      <td>1.173968e-02</td>\n      <td>9.431189e-03</td>\n      <td>5.580949e-02</td>\n      <td>0.004051</td>\n      <td>0.048762</td>\n      <td>5.577501e-03</td>\n      <td>...</td>\n      <td>2.822482e-03</td>\n      <td>0.019222</td>\n      <td>4.262291e-02</td>\n      <td>1.493034e-02</td>\n      <td>-5.810745e-02</td>\n      <td>1.713728e-03</td>\n      <td>0.022240</td>\n      <td>-3.378921e-03</td>\n      <td>4.206642e-02</td>\n      <td>0.013160</td>\n      <td>0.030831</td>\n      <td>1.005562e-02</td>\n      <td>3.590781e-02</td>\n      <td>1.562600e-02</td>\n      <td>-1.145570e-02</td>\n      <td>0.060898</td>\n      <td>6.203859e-03</td>\n      <td>-0.044888</td>\n      <td>-2.109893e-02</td>\n      <td>0.075726</td>\n      <td>7.701111e-02</td>\n      <td>2.794843e-02</td>\n      <td>0.003881</td>\n      <td>-0.000096</td>\n      <td>-1.312758e-03</td>\n      <td>0.068834</td>\n      <td>0.030550</td>\n      <td>-2.697714e-02</td>\n      <td>0.045145</td>\n      <td>0.043328</td>\n      <td>0.064292</td>\n      <td>0.027640</td>\n      <td>-0.019462</td>\n      <td>-2.687098e-02</td>\n      <td>1.000000e+00</td>\n      <td>0.019678</td>\n      <td>-0.029381</td>\n      <td>-0.007978</td>\n      <td>-5.472183e-02</td>\n      <td>0.069837</td>\n    </tr>\n    <tr>\n      <th>606</th>\n      <td>0.012016</td>\n      <td>6.225827e-03</td>\n      <td>-3.728895e-02</td>\n      <td>0.020590</td>\n      <td>2.631939e-02</td>\n      <td>-0.009137</td>\n      <td>2.832591e-02</td>\n      <td>2.227734e-02</td>\n      <td>3.163273e-02</td>\n      <td>-3.994577e-02</td>\n      <td>0.027605</td>\n      <td>0.003277</td>\n      <td>7.966100e-03</td>\n      <td>4.124999e-02</td>\n      <td>0.021381</td>\n      <td>-0.006709</td>\n      <td>0.049376</td>\n      <td>0.141273</td>\n      <td>0.047589</td>\n      <td>4.928008e-02</td>\n      <td>-0.046887</td>\n      <td>1.859264e-02</td>\n      <td>1.635189e-02</td>\n      <td>0.026197</td>\n      <td>4.774389e-02</td>\n      <td>7.945159e-03</td>\n      <td>-3.758968e-03</td>\n      <td>0.023718</td>\n      <td>1.353891e-02</td>\n      <td>2.334111e-02</td>\n      <td>-1.066414e-02</td>\n      <td>0.075005</td>\n      <td>0.075751</td>\n      <td>0.011689</td>\n      <td>3.635873e-02</td>\n      <td>2.753608e-02</td>\n      <td>1.735701e-02</td>\n      <td>0.016093</td>\n      <td>0.039560</td>\n      <td>5.261250e-02</td>\n      <td>...</td>\n      <td>3.309881e-02</td>\n      <td>0.061505</td>\n      <td>-6.456929e-04</td>\n      <td>-2.380733e-02</td>\n      <td>2.406061e-02</td>\n      <td>3.595955e-02</td>\n      <td>0.032869</td>\n      <td>6.369000e-03</td>\n      <td>2.861890e-02</td>\n      <td>0.032515</td>\n      <td>0.003842</td>\n      <td>4.279548e-03</td>\n      <td>1.616185e-02</td>\n      <td>-1.194837e-02</td>\n      <td>1.432236e-02</td>\n      <td>-0.009424</td>\n      <td>3.737446e-02</td>\n      <td>0.059339</td>\n      <td>2.758209e-02</td>\n      <td>0.175392</td>\n      <td>-6.870771e-03</td>\n      <td>2.674373e-03</td>\n      <td>0.099880</td>\n      <td>-0.024605</td>\n      <td>1.164162e-02</td>\n      <td>0.053692</td>\n      <td>0.089620</td>\n      <td>-2.336611e-02</td>\n      <td>0.098601</td>\n      <td>0.087073</td>\n      <td>0.053683</td>\n      <td>0.016384</td>\n      <td>0.098011</td>\n      <td>6.107833e-02</td>\n      <td>1.967830e-02</td>\n      <td>1.000000</td>\n      <td>0.017927</td>\n      <td>0.056676</td>\n      <td>3.842210e-02</td>\n      <td>0.075464</td>\n    </tr>\n    <tr>\n      <th>607</th>\n      <td>0.055261</td>\n      <td>-2.050374e-02</td>\n      <td>-7.789105e-03</td>\n      <td>0.014628</td>\n      <td>3.189637e-02</td>\n      <td>0.045501</td>\n      <td>3.098070e-02</td>\n      <td>4.882182e-02</td>\n      <td>-1.216061e-02</td>\n      <td>-1.765576e-02</td>\n      <td>0.079421</td>\n      <td>0.021000</td>\n      <td>-1.483908e-02</td>\n      <td>3.056445e-02</td>\n      <td>-0.005414</td>\n      <td>0.013760</td>\n      <td>0.009034</td>\n      <td>0.036393</td>\n      <td>-0.015030</td>\n      <td>1.458221e-02</td>\n      <td>-0.026727</td>\n      <td>-5.139135e-03</td>\n      <td>2.650854e-03</td>\n      <td>0.024607</td>\n      <td>2.216714e-02</td>\n      <td>4.232392e-02</td>\n      <td>1.857559e-05</td>\n      <td>-0.025664</td>\n      <td>2.165693e-02</td>\n      <td>-5.394942e-02</td>\n      <td>4.298992e-02</td>\n      <td>-0.016737</td>\n      <td>0.058030</td>\n      <td>-0.049263</td>\n      <td>3.775816e-02</td>\n      <td>5.635460e-02</td>\n      <td>3.502538e-02</td>\n      <td>0.061138</td>\n      <td>0.022223</td>\n      <td>1.833552e-02</td>\n      <td>...</td>\n      <td>6.218536e-02</td>\n      <td>0.004577</td>\n      <td>4.623006e-02</td>\n      <td>1.064229e-01</td>\n      <td>2.048794e-02</td>\n      <td>1.574318e-02</td>\n      <td>0.100440</td>\n      <td>9.049207e-24</td>\n      <td>4.342707e-02</td>\n      <td>0.005558</td>\n      <td>0.035489</td>\n      <td>1.819249e-02</td>\n      <td>-1.925438e-02</td>\n      <td>8.199059e-02</td>\n      <td>-6.171532e-03</td>\n      <td>-0.071638</td>\n      <td>-3.708225e-02</td>\n      <td>0.110273</td>\n      <td>3.478898e-02</td>\n      <td>0.051402</td>\n      <td>-1.158973e-02</td>\n      <td>5.891031e-02</td>\n      <td>-0.059337</td>\n      <td>-0.002425</td>\n      <td>5.677481e-03</td>\n      <td>0.027025</td>\n      <td>0.044123</td>\n      <td>-4.049794e-02</td>\n      <td>-0.008380</td>\n      <td>-0.046159</td>\n      <td>0.049059</td>\n      <td>0.038197</td>\n      <td>0.049317</td>\n      <td>2.355180e-03</td>\n      <td>-2.938114e-02</td>\n      <td>0.017927</td>\n      <td>1.000000</td>\n      <td>0.044514</td>\n      <td>1.904919e-02</td>\n      <td>0.021860</td>\n    </tr>\n    <tr>\n      <th>608</th>\n      <td>0.075224</td>\n      <td>-6.000828e-03</td>\n      <td>-1.300064e-02</td>\n      <td>-0.037569</td>\n      <td>-1.751121e-03</td>\n      <td>0.021727</td>\n      <td>2.841409e-02</td>\n      <td>7.175865e-02</td>\n      <td>3.278284e-02</td>\n      <td>-5.199963e-02</td>\n      <td>0.048957</td>\n      <td>-0.007228</td>\n      <td>6.567801e-02</td>\n      <td>3.939168e-02</td>\n      <td>0.086489</td>\n      <td>-0.041041</td>\n      <td>0.082083</td>\n      <td>0.100433</td>\n      <td>0.069046</td>\n      <td>9.093518e-03</td>\n      <td>0.011608</td>\n      <td>2.401809e-02</td>\n      <td>-5.658527e-04</td>\n      <td>0.037568</td>\n      <td>3.120296e-02</td>\n      <td>3.742614e-02</td>\n      <td>5.136510e-03</td>\n      <td>0.051794</td>\n      <td>-6.579689e-04</td>\n      <td>2.666243e-03</td>\n      <td>1.199044e-02</td>\n      <td>0.058146</td>\n      <td>0.027607</td>\n      <td>0.030741</td>\n      <td>6.308566e-03</td>\n      <td>2.549955e-02</td>\n      <td>2.728026e-02</td>\n      <td>0.039872</td>\n      <td>0.084627</td>\n      <td>6.981855e-02</td>\n      <td>...</td>\n      <td>4.702359e-03</td>\n      <td>0.013574</td>\n      <td>3.231520e-02</td>\n      <td>3.739976e-02</td>\n      <td>4.061313e-02</td>\n      <td>-2.269625e-03</td>\n      <td>0.035479</td>\n      <td>1.494936e-23</td>\n      <td>-1.338939e-02</td>\n      <td>0.157425</td>\n      <td>0.010186</td>\n      <td>4.622835e-03</td>\n      <td>3.434656e-04</td>\n      <td>-1.076345e-02</td>\n      <td>1.525341e-03</td>\n      <td>-0.000404</td>\n      <td>2.085220e-02</td>\n      <td>0.066268</td>\n      <td>3.515264e-02</td>\n      <td>0.084470</td>\n      <td>2.845207e-02</td>\n      <td>1.124566e-02</td>\n      <td>0.029299</td>\n      <td>-0.029827</td>\n      <td>1.651649e-02</td>\n      <td>0.032732</td>\n      <td>0.044470</td>\n      <td>-5.290514e-02</td>\n      <td>0.163429</td>\n      <td>0.043206</td>\n      <td>0.069198</td>\n      <td>0.051388</td>\n      <td>0.012801</td>\n      <td>6.318649e-03</td>\n      <td>-7.977761e-03</td>\n      <td>0.056676</td>\n      <td>0.044514</td>\n      <td>1.000000</td>\n      <td>5.071449e-02</td>\n      <td>0.054454</td>\n    </tr>\n    <tr>\n      <th>609</th>\n      <td>-0.025713</td>\n      <td>-6.009100e-02</td>\n      <td>7.905178e-25</td>\n      <td>-0.017884</td>\n      <td>9.382892e-02</td>\n      <td>0.053017</td>\n      <td>8.754391e-03</td>\n      <td>7.718048e-02</td>\n      <td>5.226205e-23</td>\n      <td>-4.009050e-02</td>\n      <td>0.047666</td>\n      <td>0.000000</td>\n      <td>2.391257e-23</td>\n      <td>-1.007497e-02</td>\n      <td>0.070662</td>\n      <td>-0.033461</td>\n      <td>0.124335</td>\n      <td>0.048829</td>\n      <td>-0.008841</td>\n      <td>5.972991e-24</td>\n      <td>0.034290</td>\n      <td>1.012680e-01</td>\n      <td>1.110310e-02</td>\n      <td>0.083864</td>\n      <td>4.295918e-02</td>\n      <td>1.609377e-01</td>\n      <td>4.257213e-02</td>\n      <td>0.050655</td>\n      <td>5.578511e-03</td>\n      <td>5.300944e-02</td>\n      <td>-1.339980e-02</td>\n      <td>0.080138</td>\n      <td>0.065460</td>\n      <td>0.081966</td>\n      <td>2.132710e-02</td>\n      <td>4.850420e-02</td>\n      <td>3.651511e-02</td>\n      <td>0.042972</td>\n      <td>0.025756</td>\n      <td>9.191738e-02</td>\n      <td>...</td>\n      <td>5.195757e-02</td>\n      <td>0.089212</td>\n      <td>1.747549e-02</td>\n      <td>1.838349e-02</td>\n      <td>6.934855e-02</td>\n      <td>7.653875e-23</td>\n      <td>0.120477</td>\n      <td>1.690958e-02</td>\n      <td>8.185487e-03</td>\n      <td>0.040138</td>\n      <td>0.068008</td>\n      <td>3.888094e-23</td>\n      <td>6.361259e-02</td>\n      <td>-1.322189e-02</td>\n      <td>3.974755e-02</td>\n      <td>0.025426</td>\n      <td>3.874710e-02</td>\n      <td>0.177493</td>\n      <td>2.082214e-01</td>\n      <td>0.039343</td>\n      <td>2.318105e-02</td>\n      <td>4.307486e-02</td>\n      <td>0.064862</td>\n      <td>-0.069630</td>\n      <td>3.974119e-23</td>\n      <td>0.003606</td>\n      <td>0.030219</td>\n      <td>2.892179e-23</td>\n      <td>0.028474</td>\n      <td>0.073134</td>\n      <td>0.043465</td>\n      <td>0.062400</td>\n      <td>0.015334</td>\n      <td>9.403771e-02</td>\n      <td>-5.472183e-02</td>\n      <td>0.038422</td>\n      <td>0.019049</td>\n      <td>0.050714</td>\n      <td>1.000000e+00</td>\n      <td>-0.012471</td>\n    </tr>\n    <tr>\n      <th>610</th>\n      <td>0.010932</td>\n      <td>2.499921e-02</td>\n      <td>1.954996e-02</td>\n      <td>-0.000995</td>\n      <td>-2.780932e-04</td>\n      <td>0.009603</td>\n      <td>6.842957e-02</td>\n      <td>1.714428e-02</td>\n      <td>5.189754e-02</td>\n      <td>-2.600368e-02</td>\n      <td>0.026876</td>\n      <td>-0.009941</td>\n      <td>7.700778e-02</td>\n      <td>1.992160e-03</td>\n      <td>-0.014099</td>\n      <td>0.008550</td>\n      <td>0.042927</td>\n      <td>0.100803</td>\n      <td>0.081843</td>\n      <td>6.926544e-02</td>\n      <td>0.000528</td>\n      <td>2.041157e-02</td>\n      <td>2.286889e-03</td>\n      <td>0.031640</td>\n      <td>3.390672e-02</td>\n      <td>4.666057e-02</td>\n      <td>-1.062886e-02</td>\n      <td>0.063953</td>\n      <td>2.759384e-02</td>\n      <td>-3.172400e-02</td>\n      <td>1.591904e-02</td>\n      <td>0.040629</td>\n      <td>0.025745</td>\n      <td>0.032900</td>\n      <td>-9.551916e-04</td>\n      <td>2.153246e-02</td>\n      <td>-1.090970e-02</td>\n      <td>0.006051</td>\n      <td>0.051777</td>\n      <td>7.258027e-03</td>\n      <td>...</td>\n      <td>1.248027e-02</td>\n      <td>0.013822</td>\n      <td>1.208663e-01</td>\n      <td>4.715562e-03</td>\n      <td>1.583434e-02</td>\n      <td>5.173192e-03</td>\n      <td>0.015327</td>\n      <td>1.257079e-02</td>\n      <td>4.821149e-03</td>\n      <td>0.110500</td>\n      <td>-0.014157</td>\n      <td>5.473041e-02</td>\n      <td>-5.361930e-03</td>\n      <td>1.809930e-02</td>\n      <td>1.245387e-02</td>\n      <td>0.013240</td>\n      <td>2.161799e-02</td>\n      <td>0.036834</td>\n      <td>8.520046e-03</td>\n      <td>0.142449</td>\n      <td>2.065136e-02</td>\n      <td>2.810368e-02</td>\n      <td>0.033001</td>\n      <td>-0.002394</td>\n      <td>9.555995e-03</td>\n      <td>0.130250</td>\n      <td>0.053947</td>\n      <td>-8.280090e-03</td>\n      <td>0.205007</td>\n      <td>0.080934</td>\n      <td>0.021603</td>\n      <td>0.030061</td>\n      <td>0.051255</td>\n      <td>1.562147e-02</td>\n      <td>6.983700e-02</td>\n      <td>0.075464</td>\n      <td>0.021860</td>\n      <td>0.054454</td>\n      <td>-1.247143e-02</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>610 rows × 610 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "dfce34de27d4353959ebd3251fcb379bc8163457"
      },
      "cell_type": "code",
      "source": "user_corr_mat = user_util_matrix.T.corr()\ncorr_user_1 = user_corr_mat.iloc[0]",
      "execution_count": 35,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "fc083259ff8f7b4bf2e39f72ecee268d629998ee"
      },
      "cell_type": "markdown",
      "source": "For convinience, I will be be considering only the correlation of all users with the first user only."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5478a6bea4d6711b9a9791b199f695791c54590e"
      },
      "cell_type": "code",
      "source": "corr_user_1.sort_values(ascending=False, inplace=True)",
      "execution_count": 36,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "f375e6f2f18dbf23544582113752d733bf481689"
      },
      "cell_type": "code",
      "source": "corr_user_1",
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 37,
          "data": {
            "text/plain": "userId\n1      1.000000\n301    0.124799\n597    0.102631\n414    0.101348\n477    0.099217\n57     0.099070\n369    0.098295\n206    0.096852\n535    0.096493\n590    0.095191\n418    0.094153\n120    0.092770\n75     0.091987\n577    0.089396\n198    0.088883\n160    0.088133\n226    0.088068\n266    0.086064\n312    0.086017\n19     0.085249\n135    0.084672\n484    0.084350\n469    0.084184\n72     0.083613\n593    0.082403\n44     0.081400\n297    0.080839\n434    0.078361\n483    0.078085\n449    0.077631\n         ...   \n243   -0.043482\n363   -0.043838\n94    -0.044296\n6     -0.045497\n361   -0.045970\n134   -0.046780\n230   -0.047011\n541   -0.047762\n321   -0.048953\n37    -0.049349\n569   -0.049434\n98    -0.052508\n11    -0.056206\n402   -0.057089\n284   -0.058938\n589   -0.059246\n592   -0.059507\n486   -0.068572\n411   -0.068986\n329   -0.071189\n203   -0.071565\n39    -0.072666\n102   -0.074549\n557   -0.074859\n395   -0.075800\n71    -0.082932\n370   -0.086678\n501   -0.090018\n579   -0.105003\n53          NaN\nName: 1, Length: 610, dtype: float64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "9bf4e612acaf357f1b200a3ab6a61d5dcc31b069"
      },
      "cell_type": "code",
      "source": "# NaN values are generated in corr() as the std dev is zero, which is required in calculating Pearson Similarity.\ncorr_user_1.dropna(inplace = True)",
      "execution_count": 38,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "76358689a137051ba86eacff4f07af19bd0a141f"
      },
      "cell_type": "code",
      "source": "# Neglect the 1st corr value as it is user1 itself\ntop50_corr_users = corr_user_1[1:51]",
      "execution_count": 39,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "151a8e82a6c2b4b8dfb424a411dda175612e0099"
      },
      "cell_type": "markdown",
      "source": "Below, we have list of all movies that user 1 has ever rated."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "508eb4150c3b0697f8e017ee9fc10d1b0c5b432b"
      },
      "cell_type": "code",
      "source": "df_combined[ df_combined['userId'] == 1]",
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 40,
          "data": {
            "text/plain": "       userId                     ...                                                            genres\n0           1                     ...                       Adventure|Animation|Children|Comedy|Fantasy\n215         1                     ...                                                    Comedy|Romance\n267         1                     ...                                             Action|Crime|Thriller\n369         1                     ...                                                  Mystery|Thriller\n572         1                     ...                                            Crime|Mystery|Thriller\n776         1                     ...                                     Action|Comedy|Horror|Thriller\n831         1                     ...                                    Adventure|Comedy|Crime|Romance\n854         1                     ...                                                  Action|Drama|War\n1091        1                     ...                                          Action|Drama|Romance|War\n1135        1                     ...                                                        Comedy|War\n1146        1                     ...                                            Action|Romance|Western\n1212        1                     ...                                                            Comedy\n1261        1                     ...                                                            Comedy\n1365        1                     ...                                                  Adventure|Comedy\n1498        1                     ...                                                      Comedy|Drama\n1568        1                     ...                                           Action|Adventure|Sci-Fi\n1819        1                     ...                                       Comedy|Crime|Drama|Thriller\n2126        1                     ...                                           Action|Adventure|Sci-Fi\n2266        1                     ...                                                            Comedy\n2316        1                     ...                                       Action|Crime|Drama|Thriller\n2426        1                     ...                                          Comedy|Drama|Romance|War\n2755        1                     ...                                        Adventure|Children|Romance\n2789        1                     ...                                       Action|Comedy|Crime|Fantasy\n2946        1                     ...                                                   Action|Thriller\n2956        1                     ...                                                            Comedy\n2998        1                     ...                                                          Thriller\n3188        1                     ...                                  Action|Adventure|Sci-Fi|Thriller\n3426        1                     ...                                                      Comedy|Drama\n3570        1                     ...                                                         Drama|War\n3790        1                     ...                                           Comedy|Romance|Thriller\n...       ...                     ...                                                               ...\n15039       1                     ...                                                  Action|Drama|War\n15050       1                     ...                                                       Crime|Drama\n15161       1                     ...                                                   Adventure|Drama\n15190       1                     ...                                            Drama|Mystery|Thriller\n15239       1                     ...                                                            Comedy\n15254       1                     ...                                                      Comedy|Crime\n15290       1                     ...                                                            Comedy\n15351       1                     ...                                    Comedy|Horror|Mystery|Thriller\n15380       1                     ...                                            Drama|Mystery|Thriller\n15411       1                     ...                                           Action|Children|Fantasy\n15424       1                     ...                          Action|Adventure|Children|Comedy|Fantasy\n15438       1                     ...                                                  Action|Drama|War\n15452       1                     ...                                                  Comedy|Drama|War\n15495       1                     ...                                                            Comedy\n15524       1                     ...                                         Adventure|Fantasy|Romance\n15542       1                     ...                                          Adventure|Comedy|Fantasy\n15595       1                     ...                                            Action|Sci-Fi|Thriller\n15656       1                     ...                                            Action|Adventure|Drama\n15826       1                     ...                                                            Comedy\n15867       1                     ...                                         Action|Adventure|Thriller\n15885       1                     ...                                                    Comedy|Western\n15947       1                     ...                                           Action|Adventure|Sci-Fi\n15985       1                     ...                                  Action|Adventure|Sci-Fi|Thriller\n16025       1                     ...                                       Action|Crime|Drama|Thriller\n16028       1                     ...                                   Action|Adventure|Comedy|Fantasy\n16056       1                     ...                                             Action|Crime|Thriller\n16075       1                     ...                                           Action|Adventure|Sci-Fi\n16208       1                     ...                                                            Comedy\n16243       1                     ...                               Adventure|Animation|Children|Sci-Fi\n16250       1                     ...                                                  Comedy|Drama|War\n\n[232 rows x 5 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>1</td>\n      <td>4.0</td>\n      <td>Toy Story (1995)</td>\n      <td>Adventure|Animation|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>215</th>\n      <td>1</td>\n      <td>3</td>\n      <td>4.0</td>\n      <td>Grumpier Old Men (1995)</td>\n      <td>Comedy|Romance</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>1</td>\n      <td>6</td>\n      <td>4.0</td>\n      <td>Heat (1995)</td>\n      <td>Action|Crime|Thriller</td>\n    </tr>\n    <tr>\n      <th>369</th>\n      <td>1</td>\n      <td>47</td>\n      <td>5.0</td>\n      <td>Seven (a.k.a. Se7en) (1995)</td>\n      <td>Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>572</th>\n      <td>1</td>\n      <td>50</td>\n      <td>5.0</td>\n      <td>Usual Suspects, The (1995)</td>\n      <td>Crime|Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>776</th>\n      <td>1</td>\n      <td>70</td>\n      <td>3.0</td>\n      <td>From Dusk Till Dawn (1996)</td>\n      <td>Action|Comedy|Horror|Thriller</td>\n    </tr>\n    <tr>\n      <th>831</th>\n      <td>1</td>\n      <td>101</td>\n      <td>5.0</td>\n      <td>Bottle Rocket (1996)</td>\n      <td>Adventure|Comedy|Crime|Romance</td>\n    </tr>\n    <tr>\n      <th>854</th>\n      <td>1</td>\n      <td>110</td>\n      <td>4.0</td>\n      <td>Braveheart (1995)</td>\n      <td>Action|Drama|War</td>\n    </tr>\n    <tr>\n      <th>1091</th>\n      <td>1</td>\n      <td>151</td>\n      <td>5.0</td>\n      <td>Rob Roy (1995)</td>\n      <td>Action|Drama|Romance|War</td>\n    </tr>\n    <tr>\n      <th>1135</th>\n      <td>1</td>\n      <td>157</td>\n      <td>5.0</td>\n      <td>Canadian Bacon (1995)</td>\n      <td>Comedy|War</td>\n    </tr>\n    <tr>\n      <th>1146</th>\n      <td>1</td>\n      <td>163</td>\n      <td>5.0</td>\n      <td>Desperado (1995)</td>\n      <td>Action|Romance|Western</td>\n    </tr>\n    <tr>\n      <th>1212</th>\n      <td>1</td>\n      <td>216</td>\n      <td>5.0</td>\n      <td>Billy Madison (1995)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>1261</th>\n      <td>1</td>\n      <td>223</td>\n      <td>3.0</td>\n      <td>Clerks (1994)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>1365</th>\n      <td>1</td>\n      <td>231</td>\n      <td>5.0</td>\n      <td>Dumb &amp; Dumber (Dumb and Dumber) (1994)</td>\n      <td>Adventure|Comedy</td>\n    </tr>\n    <tr>\n      <th>1498</th>\n      <td>1</td>\n      <td>235</td>\n      <td>4.0</td>\n      <td>Ed Wood (1994)</td>\n      <td>Comedy|Drama</td>\n    </tr>\n    <tr>\n      <th>1568</th>\n      <td>1</td>\n      <td>260</td>\n      <td>5.0</td>\n      <td>Star Wars: Episode IV - A New Hope (1977)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>1819</th>\n      <td>1</td>\n      <td>296</td>\n      <td>3.0</td>\n      <td>Pulp Fiction (1994)</td>\n      <td>Comedy|Crime|Drama|Thriller</td>\n    </tr>\n    <tr>\n      <th>2126</th>\n      <td>1</td>\n      <td>316</td>\n      <td>3.0</td>\n      <td>Stargate (1994)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>2266</th>\n      <td>1</td>\n      <td>333</td>\n      <td>5.0</td>\n      <td>Tommy Boy (1995)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>2316</th>\n      <td>1</td>\n      <td>349</td>\n      <td>4.0</td>\n      <td>Clear and Present Danger (1994)</td>\n      <td>Action|Crime|Drama|Thriller</td>\n    </tr>\n    <tr>\n      <th>2426</th>\n      <td>1</td>\n      <td>356</td>\n      <td>4.0</td>\n      <td>Forrest Gump (1994)</td>\n      <td>Comedy|Drama|Romance|War</td>\n    </tr>\n    <tr>\n      <th>2755</th>\n      <td>1</td>\n      <td>362</td>\n      <td>5.0</td>\n      <td>Jungle Book, The (1994)</td>\n      <td>Adventure|Children|Romance</td>\n    </tr>\n    <tr>\n      <th>2789</th>\n      <td>1</td>\n      <td>367</td>\n      <td>4.0</td>\n      <td>Mask, The (1994)</td>\n      <td>Action|Comedy|Crime|Fantasy</td>\n    </tr>\n    <tr>\n      <th>2946</th>\n      <td>1</td>\n      <td>423</td>\n      <td>3.0</td>\n      <td>Blown Away (1994)</td>\n      <td>Action|Thriller</td>\n    </tr>\n    <tr>\n      <th>2956</th>\n      <td>1</td>\n      <td>441</td>\n      <td>4.0</td>\n      <td>Dazed and Confused (1993)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>2998</th>\n      <td>1</td>\n      <td>457</td>\n      <td>5.0</td>\n      <td>Fugitive, The (1993)</td>\n      <td>Thriller</td>\n    </tr>\n    <tr>\n      <th>3188</th>\n      <td>1</td>\n      <td>480</td>\n      <td>4.0</td>\n      <td>Jurassic Park (1993)</td>\n      <td>Action|Adventure|Sci-Fi|Thriller</td>\n    </tr>\n    <tr>\n      <th>3426</th>\n      <td>1</td>\n      <td>500</td>\n      <td>3.0</td>\n      <td>Mrs. Doubtfire (1993)</td>\n      <td>Comedy|Drama</td>\n    </tr>\n    <tr>\n      <th>3570</th>\n      <td>1</td>\n      <td>527</td>\n      <td>5.0</td>\n      <td>Schindler's List (1993)</td>\n      <td>Drama|War</td>\n    </tr>\n    <tr>\n      <th>3790</th>\n      <td>1</td>\n      <td>543</td>\n      <td>4.0</td>\n      <td>So I Married an Axe Murderer (1993)</td>\n      <td>Comedy|Romance|Thriller</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>15039</th>\n      <td>1</td>\n      <td>3062</td>\n      <td>4.0</td>\n      <td>Longest Day, The (1962)</td>\n      <td>Action|Drama|War</td>\n    </tr>\n    <tr>\n      <th>15050</th>\n      <td>1</td>\n      <td>3147</td>\n      <td>5.0</td>\n      <td>Green Mile, The (1999)</td>\n      <td>Crime|Drama</td>\n    </tr>\n    <tr>\n      <th>15161</th>\n      <td>1</td>\n      <td>3168</td>\n      <td>4.0</td>\n      <td>Easy Rider (1969)</td>\n      <td>Adventure|Drama</td>\n    </tr>\n    <tr>\n      <th>15190</th>\n      <td>1</td>\n      <td>3176</td>\n      <td>1.0</td>\n      <td>Talented Mr. Ripley, The (1999)</td>\n      <td>Drama|Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>15239</th>\n      <td>1</td>\n      <td>3243</td>\n      <td>3.0</td>\n      <td>Encino Man (1992)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>15254</th>\n      <td>1</td>\n      <td>3247</td>\n      <td>3.0</td>\n      <td>Sister Act (1992)</td>\n      <td>Comedy|Crime</td>\n    </tr>\n    <tr>\n      <th>15290</th>\n      <td>1</td>\n      <td>3253</td>\n      <td>5.0</td>\n      <td>Wayne's World (1992)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>15351</th>\n      <td>1</td>\n      <td>3273</td>\n      <td>5.0</td>\n      <td>Scream 3 (2000)</td>\n      <td>Comedy|Horror|Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>15380</th>\n      <td>1</td>\n      <td>3386</td>\n      <td>5.0</td>\n      <td>JFK (1991)</td>\n      <td>Drama|Mystery|Thriller</td>\n    </tr>\n    <tr>\n      <th>15411</th>\n      <td>1</td>\n      <td>3439</td>\n      <td>4.0</td>\n      <td>Teenage Mutant Ninja Turtles II: The Secret of...</td>\n      <td>Action|Children|Fantasy</td>\n    </tr>\n    <tr>\n      <th>15424</th>\n      <td>1</td>\n      <td>3440</td>\n      <td>4.0</td>\n      <td>Teenage Mutant Ninja Turtles III (1993)</td>\n      <td>Action|Adventure|Children|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>15438</th>\n      <td>1</td>\n      <td>3441</td>\n      <td>5.0</td>\n      <td>Red Dawn (1984)</td>\n      <td>Action|Drama|War</td>\n    </tr>\n    <tr>\n      <th>15452</th>\n      <td>1</td>\n      <td>3448</td>\n      <td>5.0</td>\n      <td>Good Morning, Vietnam (1987)</td>\n      <td>Comedy|Drama|War</td>\n    </tr>\n    <tr>\n      <th>15495</th>\n      <td>1</td>\n      <td>3450</td>\n      <td>5.0</td>\n      <td>Grumpy Old Men (1993)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>15524</th>\n      <td>1</td>\n      <td>3479</td>\n      <td>4.0</td>\n      <td>Ladyhawke (1985)</td>\n      <td>Adventure|Fantasy|Romance</td>\n    </tr>\n    <tr>\n      <th>15542</th>\n      <td>1</td>\n      <td>3489</td>\n      <td>4.0</td>\n      <td>Hook (1991)</td>\n      <td>Adventure|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>15595</th>\n      <td>1</td>\n      <td>3527</td>\n      <td>4.0</td>\n      <td>Predator (1987)</td>\n      <td>Action|Sci-Fi|Thriller</td>\n    </tr>\n    <tr>\n      <th>15656</th>\n      <td>1</td>\n      <td>3578</td>\n      <td>5.0</td>\n      <td>Gladiator (2000)</td>\n      <td>Action|Adventure|Drama</td>\n    </tr>\n    <tr>\n      <th>15826</th>\n      <td>1</td>\n      <td>3617</td>\n      <td>4.0</td>\n      <td>Road Trip (2000)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>15867</th>\n      <td>1</td>\n      <td>3639</td>\n      <td>4.0</td>\n      <td>Man with the Golden Gun, The (1974)</td>\n      <td>Action|Adventure|Thriller</td>\n    </tr>\n    <tr>\n      <th>15885</th>\n      <td>1</td>\n      <td>3671</td>\n      <td>5.0</td>\n      <td>Blazing Saddles (1974)</td>\n      <td>Comedy|Western</td>\n    </tr>\n    <tr>\n      <th>15947</th>\n      <td>1</td>\n      <td>3702</td>\n      <td>5.0</td>\n      <td>Mad Max (1979)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>15985</th>\n      <td>1</td>\n      <td>3703</td>\n      <td>5.0</td>\n      <td>Road Warrior, The (Mad Max 2) (1981)</td>\n      <td>Action|Adventure|Sci-Fi|Thriller</td>\n    </tr>\n    <tr>\n      <th>16025</th>\n      <td>1</td>\n      <td>3729</td>\n      <td>5.0</td>\n      <td>Shaft (1971)</td>\n      <td>Action|Crime|Drama|Thriller</td>\n    </tr>\n    <tr>\n      <th>16028</th>\n      <td>1</td>\n      <td>3740</td>\n      <td>4.0</td>\n      <td>Big Trouble in Little China (1986)</td>\n      <td>Action|Adventure|Comedy|Fantasy</td>\n    </tr>\n    <tr>\n      <th>16056</th>\n      <td>1</td>\n      <td>3744</td>\n      <td>4.0</td>\n      <td>Shaft (2000)</td>\n      <td>Action|Crime|Thriller</td>\n    </tr>\n    <tr>\n      <th>16075</th>\n      <td>1</td>\n      <td>3793</td>\n      <td>5.0</td>\n      <td>X-Men (2000)</td>\n      <td>Action|Adventure|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>16208</th>\n      <td>1</td>\n      <td>3809</td>\n      <td>4.0</td>\n      <td>What About Bob? (1991)</td>\n      <td>Comedy</td>\n    </tr>\n    <tr>\n      <th>16243</th>\n      <td>1</td>\n      <td>4006</td>\n      <td>4.0</td>\n      <td>Transformers: The Movie (1986)</td>\n      <td>Adventure|Animation|Children|Sci-Fi</td>\n    </tr>\n    <tr>\n      <th>16250</th>\n      <td>1</td>\n      <td>5060</td>\n      <td>5.0</td>\n      <td>M*A*S*H (a.k.a. MASH) (1970)</td>\n      <td>Comedy|Drama|War</td>\n    </tr>\n  </tbody>\n</table>\n<p>232 rows × 5 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6509e470f100d49ecd0748a4a583f530a473d5f8"
      },
      "cell_type": "code",
      "source": "# user1 has not rated 32 movie\ndf_combined[ (df_combined['userId'] == 1) & (df_combined['movieId'] == 32) ] ",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 41,
          "data": {
            "text/plain": "Empty DataFrame\nColumns: [userId, movieId, rating, title, genres]\nIndex: []",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>userId</th>\n      <th>movieId</th>\n      <th>rating</th>\n      <th>title</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "73695b0c25ab08f66a0347c2703a959633b48c92"
      },
      "cell_type": "code",
      "source": "print('32nd Movie : ', movies['title'][ movies['movieId'] == 32 ].values)",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": "32nd Movie :  ['Twelve Monkeys (a.k.a. 12 Monkeys) (1995)']\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "9ae02f0c35676058bfcd618b7d1971b2e9f1a0af"
      },
      "cell_type": "markdown",
      "source": "So, let's calculate what ratings user 1 would give to the movie with the help of similarrity vector. And based on that rating, we can compare it with a threshold rating. If the rating is higher it will be visible to the active user in his/her recommended list."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "03956bd9ae1ad18af7e16f7008b83e0e01e46b9a"
      },
      "cell_type": "code",
      "source": "df_n_ratings.loc[['Twelve Monkeys (a.k.a. 12 Monkeys) (1995)']]",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 43,
          "data": {
            "text/plain": "                                           mean ratings  total ratings\ntitle                                                                 \nTwelve Monkeys (a.k.a. 12 Monkeys) (1995)      3.983051            177",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean ratings</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Twelve Monkeys (a.k.a. 12 Monkeys) (1995)</th>\n      <td>3.983051</td>\n      <td>177</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "bf2ca5d01c83ddb6c155e90a035d24266ae40967"
      },
      "cell_type": "code",
      "source": "top50_users = top50_corr_users.keys()\n\ncount = 0\nusers = list()\nfor user in top50_users:\n    if df_combined[ (df_combined['userId'] == user) & (df_combined['movieId'] == 32) ]['rating'].sum()  :\n        count +=1\n        users.append(user)\n\nprint(count)",
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": "30\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "73e66b57828d0a0923e010b7a25d8539459d3668"
      },
      "cell_type": "markdown",
      "source": "There are 30 similar users among the Top-50 similar users that have rated the movie \"The Twelve Monkeys\".\n\n* Now, let's calculate the rating user 1 would give to the movie, \n\n* **Predicted rating** = sum of [ (weights) * (ratings) ]  **/** sum of  (weights)\n\nHere, *weights* is the correlation of the corresponding user with the first user.\n\n"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "dbf19a02eac8489cabd89ce83daf841a78ec956e"
      },
      "cell_type": "code",
      "source": "# Let's define a function to calculate what user1 will rate the movie\n# We use Weighted average of k similar users\n\ndef predict_rating():\n    sum_similarity = 0\n    weighted_ratings = 0\n    for user in users:\n        weighted_ratings += top50_corr_users.ix[user] * df_combined[ (df_combined['userId'] == user) & \n                                                              (df_combined['movieId'] == 32) ]['rating'].sum()\n        sum_similarity += top50_corr_users.ix[user]\n\n    print(weighted_ratings / sum_similarity)\n    \n    \npredict_rating()",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": "4.1368953015974235\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "9a43ee866ebbd94da8defbc4b252aabc48f20c47"
      },
      "cell_type": "code",
      "source": "df_m[ df_m['movieId'] == 32]",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 46,
          "data": {
            "text/plain": "    movieId           ...                              genres\n31       32           ...             Mystery|Sci-Fi|Thriller\n\n[1 rows x 3 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>movieId</th>\n      <th>title</th>\n      <th>genres</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>31</th>\n      <td>32</td>\n      <td>Twelve Monkeys (a.k.a. 12 Monkeys) (1995)</td>\n      <td>Mystery|Sci-Fi|Thriller</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "30a998666b99034875994218079be771340114f0"
      },
      "cell_type": "markdown",
      "source": "Well there are pretty good changes of recommending this movie movie to the 1st user, since the rating is quite good.\n\nSo, this is how a naive User-based CF works. Predicted ratings are calculated similarly for every user, (obviously for the movies he's not rated) and depending upon the threshold rating, the movie is either displayed on his recommended list or discarded."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "783a1e8eb5c54ae8ba5a19e2d85fdf9301c7f1a5"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "34f1b131d985b318b02e106148e16b551c44f541"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.2.2\"></a>\n### 3.2.2) Item Based Collaborative Filtering\n\n\n* It is quite similar to previous algorithm, but instead of finding user's look-alike, we try finding movie's look-alike. \n* Once we have movie's look-alike matrix, we can easily recommend alike movies to user who have rated any movie from the dataset.\n\n![Imgur](https://i.imgur.com/wKMnQiU.jpg)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "e188f0797356d83f60ecfe87b08a456a696e312e"
      },
      "cell_type": "code",
      "source": "# Let's find similar movies to jurassic Park\ndf_n_ratings.loc[['Jurassic Park (1993)']]",
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 47,
          "data": {
            "text/plain": "                      mean ratings  total ratings\ntitle                                            \nJurassic Park (1993)          3.75            238",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean ratings</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Jurassic Park (1993)</th>\n      <td>3.75</td>\n      <td>238</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "acfecb492ed23b6d5ec21c5cb6a07f1ce8074eab"
      },
      "cell_type": "code",
      "source": "item_util_matrix = util_mat.copy()\nitem_util_matrix.head(10)",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 48,
          "data": {
            "text/plain": "title   '71 (2014)                    ...                      À nous la liberté (Freedom for Us) (1931)\nuserId                                ...                                                               \n1              NaN                    ...                                                            NaN\n2              NaN                    ...                                                            NaN\n3              NaN                    ...                                                            NaN\n4              NaN                    ...                                                            NaN\n5              NaN                    ...                                                            NaN\n6              NaN                    ...                                                            NaN\n7              NaN                    ...                                                            NaN\n8              NaN                    ...                                                            NaN\n9              NaN                    ...                                                            NaN\n10             NaN                    ...                                                            NaN\n\n[10 rows x 9719 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>title</th>\n      <th>'71 (2014)</th>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <th>'Round Midnight (1986)</th>\n      <th>'Salem's Lot (2004)</th>\n      <th>'Til There Was You (1997)</th>\n      <th>'Tis the Season for Love (2015)</th>\n      <th>'burbs, The (1989)</th>\n      <th>'night Mother (1986)</th>\n      <th>(500) Days of Summer (2009)</th>\n      <th>*batteries not included (1987)</th>\n      <th>...All the Marbles (1981)</th>\n      <th>...And Justice for All (1979)</th>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <th>1-900 (06) (1994)</th>\n      <th>10 (1979)</th>\n      <th>10 Cent Pistol (2015)</th>\n      <th>10 Cloverfield Lane (2016)</th>\n      <th>10 Items or Less (2006)</th>\n      <th>10 Things I Hate About You (1999)</th>\n      <th>10 Years (2011)</th>\n      <th>10,000 BC (2008)</th>\n      <th>100 Girls (2000)</th>\n      <th>100 Streets (2016)</th>\n      <th>101 Dalmatians (1996)</th>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <th>102 Dalmatians (2000)</th>\n      <th>10th &amp; Wolf (2006)</th>\n      <th>10th Kingdom, The (2000)</th>\n      <th>10th Victim, The (La decima vittima) (1965)</th>\n      <th>11'09\"01 - September 11 (2002)</th>\n      <th>11:14 (2003)</th>\n      <th>11th Hour, The (2007)</th>\n      <th>12 Angry Men (1957)</th>\n      <th>12 Angry Men (1997)</th>\n      <th>12 Chairs (1971)</th>\n      <th>12 Chairs (1976)</th>\n      <th>12 Rounds (2009)</th>\n      <th>12 Years a Slave (2013)</th>\n      <th>...</th>\n      <th>Zathura (2005)</th>\n      <th>Zatoichi and the Chest of Gold (Zatôichi senryô-kubi) (Zatôichi 6) (1964)</th>\n      <th>Zazie dans le métro (1960)</th>\n      <th>Zebraman (2004)</th>\n      <th>Zed &amp; Two Noughts, A (1985)</th>\n      <th>Zeitgeist: Addendum (2008)</th>\n      <th>Zeitgeist: Moving Forward (2011)</th>\n      <th>Zeitgeist: The Movie (2007)</th>\n      <th>Zelary (2003)</th>\n      <th>Zelig (1983)</th>\n      <th>Zero Dark Thirty (2012)</th>\n      <th>Zero Effect (1998)</th>\n      <th>Zero Theorem, The (2013)</th>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <th>Zeus and Roxanne (1997)</th>\n      <th>Zipper (2015)</th>\n      <th>Zodiac (2007)</th>\n      <th>Zombeavers (2014)</th>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <th>Zombie Strippers! (2008)</th>\n      <th>Zombieland (2009)</th>\n      <th>Zone 39 (1997)</th>\n      <th>Zone, The (La Zona) (2007)</th>\n      <th>Zookeeper (2011)</th>\n      <th>Zoolander (2001)</th>\n      <th>Zoolander 2 (2016)</th>\n      <th>Zoom (2006)</th>\n      <th>Zoom (2015)</th>\n      <th>Zootopia (2016)</th>\n      <th>Zulu (1964)</th>\n      <th>Zulu (2013)</th>\n      <th>[REC] (2007)</th>\n      <th>[REC]² (2009)</th>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <th>eXistenZ (1999)</th>\n      <th>xXx (2002)</th>\n      <th>xXx: State of the Union (2005)</th>\n      <th>¡Three Amigos! (1986)</th>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "669bf2591d64d3a80b7e895e63ec4d8bede667a0"
      },
      "cell_type": "code",
      "source": "# We will fill the column wise NaN's with the corresponding movie's mean ratings, so that we can carry out Pearson correlation.\n# Here we assume avg ratings for the user that has not a rated movie.\n\nitem_util_matrix = item_util_matrix.apply(lambda col : col.fillna(col.mean()), axis=0)\nitem_util_matrix.head(5)",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 49,
          "data": {
            "text/plain": "title   '71 (2014)                    ...                      À nous la liberté (Freedom for Us) (1931)\nuserId                                ...                                                               \n1              4.0                    ...                                                            1.0\n2              4.0                    ...                                                            1.0\n3              4.0                    ...                                                            1.0\n4              4.0                    ...                                                            1.0\n5              4.0                    ...                                                            1.0\n\n[5 rows x 9719 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>title</th>\n      <th>'71 (2014)</th>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <th>'Round Midnight (1986)</th>\n      <th>'Salem's Lot (2004)</th>\n      <th>'Til There Was You (1997)</th>\n      <th>'Tis the Season for Love (2015)</th>\n      <th>'burbs, The (1989)</th>\n      <th>'night Mother (1986)</th>\n      <th>(500) Days of Summer (2009)</th>\n      <th>*batteries not included (1987)</th>\n      <th>...All the Marbles (1981)</th>\n      <th>...And Justice for All (1979)</th>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <th>1-900 (06) (1994)</th>\n      <th>10 (1979)</th>\n      <th>10 Cent Pistol (2015)</th>\n      <th>10 Cloverfield Lane (2016)</th>\n      <th>10 Items or Less (2006)</th>\n      <th>10 Things I Hate About You (1999)</th>\n      <th>10 Years (2011)</th>\n      <th>10,000 BC (2008)</th>\n      <th>100 Girls (2000)</th>\n      <th>100 Streets (2016)</th>\n      <th>101 Dalmatians (1996)</th>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <th>102 Dalmatians (2000)</th>\n      <th>10th &amp; Wolf (2006)</th>\n      <th>10th Kingdom, The (2000)</th>\n      <th>10th Victim, The (La decima vittima) (1965)</th>\n      <th>11'09\"01 - September 11 (2002)</th>\n      <th>11:14 (2003)</th>\n      <th>11th Hour, The (2007)</th>\n      <th>12 Angry Men (1957)</th>\n      <th>12 Angry Men (1997)</th>\n      <th>12 Chairs (1971)</th>\n      <th>12 Chairs (1976)</th>\n      <th>12 Rounds (2009)</th>\n      <th>12 Years a Slave (2013)</th>\n      <th>...</th>\n      <th>Zathura (2005)</th>\n      <th>Zatoichi and the Chest of Gold (Zatôichi senryô-kubi) (Zatôichi 6) (1964)</th>\n      <th>Zazie dans le métro (1960)</th>\n      <th>Zebraman (2004)</th>\n      <th>Zed &amp; Two Noughts, A (1985)</th>\n      <th>Zeitgeist: Addendum (2008)</th>\n      <th>Zeitgeist: Moving Forward (2011)</th>\n      <th>Zeitgeist: The Movie (2007)</th>\n      <th>Zelary (2003)</th>\n      <th>Zelig (1983)</th>\n      <th>Zero Dark Thirty (2012)</th>\n      <th>Zero Effect (1998)</th>\n      <th>Zero Theorem, The (2013)</th>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <th>Zeus and Roxanne (1997)</th>\n      <th>Zipper (2015)</th>\n      <th>Zodiac (2007)</th>\n      <th>Zombeavers (2014)</th>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <th>Zombie Strippers! (2008)</th>\n      <th>Zombieland (2009)</th>\n      <th>Zone 39 (1997)</th>\n      <th>Zone, The (La Zona) (2007)</th>\n      <th>Zookeeper (2011)</th>\n      <th>Zoolander (2001)</th>\n      <th>Zoolander 2 (2016)</th>\n      <th>Zoom (2006)</th>\n      <th>Zoom (2015)</th>\n      <th>Zootopia (2016)</th>\n      <th>Zulu (1964)</th>\n      <th>Zulu (2013)</th>\n      <th>[REC] (2007)</th>\n      <th>[REC]² (2009)</th>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <th>eXistenZ (1999)</th>\n      <th>xXx (2002)</th>\n      <th>xXx: State of the Union (2005)</th>\n      <th>¡Three Amigos! (1986)</th>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n    </tr>\n    <tr>\n      <th>userId</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>3.176471</td>\n      <td>3.0</td>\n      <td>3.666667</td>\n      <td>3.285714</td>\n      <td>2.0</td>\n      <td>3.166667</td>\n      <td>4.5</td>\n      <td>4.0</td>\n      <td>3.375</td>\n      <td>1.25</td>\n      <td>3.678571</td>\n      <td>2.666667</td>\n      <td>3.527778</td>\n      <td>3.5</td>\n      <td>2.705882</td>\n      <td>3.25</td>\n      <td>2.5</td>\n      <td>3.074468</td>\n      <td>3.431818</td>\n      <td>2.5</td>\n      <td>3.5</td>\n      <td>2.777778</td>\n      <td>4.5</td>\n      <td>2.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.149123</td>\n      <td>5.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.625</td>\n      <td>...</td>\n      <td>3.375</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.5</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.107143</td>\n      <td>3.966667</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.710526</td>\n      <td>2.5</td>\n      <td>4.0</td>\n      <td>0.5</td>\n      <td>3.877358</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>2.75</td>\n      <td>3.509259</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>3.890625</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>4.0625</td>\n      <td>3.666667</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.863636</td>\n      <td>2.770833</td>\n      <td>2.0</td>\n      <td>4.000000</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>3.176471</td>\n      <td>3.0</td>\n      <td>3.666667</td>\n      <td>3.285714</td>\n      <td>2.0</td>\n      <td>3.166667</td>\n      <td>4.5</td>\n      <td>4.0</td>\n      <td>3.375</td>\n      <td>1.25</td>\n      <td>3.678571</td>\n      <td>2.666667</td>\n      <td>3.527778</td>\n      <td>3.5</td>\n      <td>2.705882</td>\n      <td>3.25</td>\n      <td>2.5</td>\n      <td>3.074468</td>\n      <td>3.431818</td>\n      <td>2.5</td>\n      <td>3.5</td>\n      <td>2.777778</td>\n      <td>4.5</td>\n      <td>2.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.149123</td>\n      <td>5.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.625</td>\n      <td>...</td>\n      <td>3.375</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.5</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.107143</td>\n      <td>3.966667</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.710526</td>\n      <td>2.5</td>\n      <td>4.0</td>\n      <td>0.5</td>\n      <td>3.000000</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>2.75</td>\n      <td>3.509259</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>3.890625</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>4.0625</td>\n      <td>3.666667</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.863636</td>\n      <td>2.770833</td>\n      <td>2.0</td>\n      <td>3.134615</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>3.176471</td>\n      <td>3.0</td>\n      <td>3.666667</td>\n      <td>3.285714</td>\n      <td>2.0</td>\n      <td>3.166667</td>\n      <td>4.5</td>\n      <td>4.0</td>\n      <td>3.375</td>\n      <td>1.25</td>\n      <td>3.678571</td>\n      <td>2.666667</td>\n      <td>3.527778</td>\n      <td>3.5</td>\n      <td>2.705882</td>\n      <td>3.25</td>\n      <td>2.5</td>\n      <td>3.074468</td>\n      <td>3.431818</td>\n      <td>2.5</td>\n      <td>3.5</td>\n      <td>2.777778</td>\n      <td>4.5</td>\n      <td>2.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.149123</td>\n      <td>5.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.625</td>\n      <td>...</td>\n      <td>3.375</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.5</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.107143</td>\n      <td>3.966667</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.710526</td>\n      <td>2.5</td>\n      <td>4.0</td>\n      <td>0.5</td>\n      <td>3.877358</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>2.75</td>\n      <td>3.509259</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>3.890625</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>4.0625</td>\n      <td>3.666667</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.863636</td>\n      <td>2.770833</td>\n      <td>2.0</td>\n      <td>3.134615</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>3.176471</td>\n      <td>3.0</td>\n      <td>3.666667</td>\n      <td>3.285714</td>\n      <td>2.0</td>\n      <td>3.166667</td>\n      <td>4.5</td>\n      <td>4.0</td>\n      <td>3.375</td>\n      <td>1.25</td>\n      <td>3.678571</td>\n      <td>2.666667</td>\n      <td>3.527778</td>\n      <td>3.5</td>\n      <td>2.705882</td>\n      <td>3.25</td>\n      <td>2.5</td>\n      <td>3.074468</td>\n      <td>3.431818</td>\n      <td>2.5</td>\n      <td>3.5</td>\n      <td>2.777778</td>\n      <td>4.5</td>\n      <td>2.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>5.000000</td>\n      <td>5.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.625</td>\n      <td>...</td>\n      <td>3.375</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.5</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.107143</td>\n      <td>3.966667</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.710526</td>\n      <td>2.5</td>\n      <td>4.0</td>\n      <td>0.5</td>\n      <td>3.877358</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>2.75</td>\n      <td>3.509259</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>3.890625</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>4.0625</td>\n      <td>3.666667</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.863636</td>\n      <td>2.770833</td>\n      <td>2.0</td>\n      <td>3.134615</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.5</td>\n      <td>5.0</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>3.176471</td>\n      <td>3.0</td>\n      <td>3.666667</td>\n      <td>3.285714</td>\n      <td>2.0</td>\n      <td>3.166667</td>\n      <td>4.5</td>\n      <td>4.0</td>\n      <td>3.375</td>\n      <td>1.25</td>\n      <td>3.678571</td>\n      <td>2.666667</td>\n      <td>3.527778</td>\n      <td>3.5</td>\n      <td>2.705882</td>\n      <td>3.25</td>\n      <td>2.5</td>\n      <td>3.074468</td>\n      <td>3.431818</td>\n      <td>2.5</td>\n      <td>3.5</td>\n      <td>2.777778</td>\n      <td>4.5</td>\n      <td>2.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.149123</td>\n      <td>5.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.0</td>\n      <td>3.625</td>\n      <td>...</td>\n      <td>3.375</td>\n      <td>4.0</td>\n      <td>3.0</td>\n      <td>3.5</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>5.0</td>\n      <td>3.75</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>4.107143</td>\n      <td>3.966667</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.0</td>\n      <td>2.0</td>\n      <td>3.710526</td>\n      <td>2.5</td>\n      <td>4.0</td>\n      <td>0.5</td>\n      <td>3.877358</td>\n      <td>2.0</td>\n      <td>3.5</td>\n      <td>2.75</td>\n      <td>3.509259</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>2.5</td>\n      <td>3.890625</td>\n      <td>4.0</td>\n      <td>1.5</td>\n      <td>4.0625</td>\n      <td>3.666667</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.863636</td>\n      <td>2.770833</td>\n      <td>2.0</td>\n      <td>3.134615</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "392dd321fdbe3cb448bd727f0b154d79e7d1feb5"
      },
      "cell_type": "code",
      "source": "item_util_matrix.isna().sum().sum()",
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 50,
          "data": {
            "text/plain": "0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "a2afd1c644d59e18a80e17eef20e4f2f839a3f26"
      },
      "cell_type": "markdown",
      "source": "This signifies that every Movie is rated by atleast 1 user."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d76ac6a96895185ab7ec02555ed9e043eaecccd2"
      },
      "cell_type": "code",
      "source": "item_util_matrix.corr()",
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 51,
          "data": {
            "text/plain": "title                                               '71 (2014)                    ...                      À nous la liberté (Freedom for Us) (1931)\ntitle                                                                             ...                                                               \n'71 (2014)                                                 NaN                    ...                                                            NaN\n'Hellboy': The Seeds of Creation (2004)                    NaN                    ...                                                            NaN\n'Round Midnight (1986)                                     NaN                    ...                                                            NaN\n'Salem's Lot (2004)                                        NaN                    ...                                                            NaN\n'Til There Was You (1997)                                  NaN                    ...                                                            NaN\n'Tis the Season for Love (2015)                            NaN                    ...                                                            NaN\n'burbs, The (1989)                                         NaN                    ...                                                            NaN\n'night Mother (1986)                                       NaN                    ...                                                            NaN\n(500) Days of Summer (2009)                                NaN                    ...                                                            NaN\n*batteries not included (1987)                             NaN                    ...                                                            NaN\n...All the Marbles (1981)                                  NaN                    ...                                                            NaN\n...And Justice for All (1979)                              NaN                    ...                                                            NaN\n00 Schneider - Jagd auf Nihil Baxter (1994)                NaN                    ...                                                            NaN\n1-900 (06) (1994)                                          NaN                    ...                                                            NaN\n10 (1979)                                                  NaN                    ...                                                            NaN\n10 Cent Pistol (2015)                                      NaN                    ...                                                            NaN\n10 Cloverfield Lane (2016)                                 NaN                    ...                                                            NaN\n10 Items or Less (2006)                                    NaN                    ...                                                            NaN\n10 Things I Hate About You (1999)                          NaN                    ...                                                            NaN\n10 Years (2011)                                            NaN                    ...                                                            NaN\n10,000 BC (2008)                                           NaN                    ...                                                            NaN\n100 Girls (2000)                                           NaN                    ...                                                            NaN\n100 Streets (2016)                                         NaN                    ...                                                            NaN\n101 Dalmatians (1996)                                      NaN                    ...                                                            NaN\n101 Dalmatians (One Hundred and One Dalmatians)...         NaN                    ...                                                            NaN\n101 Dalmatians II: Patch's London Adventure (2003)         NaN                    ...                                                            NaN\n101 Reykjavik (101 Reykjavík) (2000)                       NaN                    ...                                                            NaN\n102 Dalmatians (2000)                                      NaN                    ...                                                            NaN\n10th & Wolf (2006)                                         NaN                    ...                                                            NaN\n10th Kingdom, The (2000)                                   NaN                    ...                                                            NaN\n...                                                        ...                    ...                                                            ...\nZero Dark Thirty (2012)                                    NaN                    ...                                                            NaN\nZero Effect (1998)                                         NaN                    ...                                                            NaN\nZero Theorem, The (2013)                                   NaN                    ...                                                            NaN\nZero de conduite (Zero for Conduct) (Zéro de co...         NaN                    ...                                                            NaN\nZeus and Roxanne (1997)                                    NaN                    ...                                                            NaN\nZipper (2015)                                              NaN                    ...                                                            NaN\nZodiac (2007)                                              NaN                    ...                                                            NaN\nZombeavers (2014)                                          NaN                    ...                                                            NaN\nZombie (a.k.a. Zombie 2: The Dead Are Among Us)...         NaN                    ...                                                            NaN\nZombie Strippers! (2008)                                   NaN                    ...                                                            NaN\nZombieland (2009)                                          NaN                    ...                                                            NaN\nZone 39 (1997)                                             NaN                    ...                                                            NaN\nZone, The (La Zona) (2007)                                 NaN                    ...                                                            NaN\nZookeeper (2011)                                           NaN                    ...                                                            NaN\nZoolander (2001)                                           NaN                    ...                                                            NaN\nZoolander 2 (2016)                                         NaN                    ...                                                            NaN\nZoom (2006)                                                NaN                    ...                                                            NaN\nZoom (2015)                                                NaN                    ...                                                            NaN\nZootopia (2016)                                            NaN                    ...                                                            NaN\nZulu (1964)                                                NaN                    ...                                                            NaN\nZulu (2013)                                                NaN                    ...                                                            NaN\n[REC] (2007)                                               NaN                    ...                                                            NaN\n[REC]² (2009)                                              NaN                    ...                                                            NaN\n[REC]³ 3 Génesis (2012)                                    NaN                    ...                                                            NaN\nanohana: The Flower We Saw That Day - The Movie...         NaN                    ...                                                            NaN\neXistenZ (1999)                                            NaN                    ...                                                            NaN\nxXx (2002)                                                 NaN                    ...                                                            NaN\nxXx: State of the Union (2005)                             NaN                    ...                                                            NaN\n¡Three Amigos! (1986)                                      NaN                    ...                                                            NaN\nÀ nous la liberté (Freedom for Us) (1931)                  NaN                    ...                                                            NaN\n\n[9719 rows x 9719 columns]",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>title</th>\n      <th>'71 (2014)</th>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <th>'Round Midnight (1986)</th>\n      <th>'Salem's Lot (2004)</th>\n      <th>'Til There Was You (1997)</th>\n      <th>'Tis the Season for Love (2015)</th>\n      <th>'burbs, The (1989)</th>\n      <th>'night Mother (1986)</th>\n      <th>(500) Days of Summer (2009)</th>\n      <th>*batteries not included (1987)</th>\n      <th>...All the Marbles (1981)</th>\n      <th>...And Justice for All (1979)</th>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <th>1-900 (06) (1994)</th>\n      <th>10 (1979)</th>\n      <th>10 Cent Pistol (2015)</th>\n      <th>10 Cloverfield Lane (2016)</th>\n      <th>10 Items or Less (2006)</th>\n      <th>10 Things I Hate About You (1999)</th>\n      <th>10 Years (2011)</th>\n      <th>10,000 BC (2008)</th>\n      <th>100 Girls (2000)</th>\n      <th>100 Streets (2016)</th>\n      <th>101 Dalmatians (1996)</th>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <th>102 Dalmatians (2000)</th>\n      <th>10th &amp; Wolf (2006)</th>\n      <th>10th Kingdom, The (2000)</th>\n      <th>10th Victim, The (La decima vittima) (1965)</th>\n      <th>11'09\"01 - September 11 (2002)</th>\n      <th>11:14 (2003)</th>\n      <th>11th Hour, The (2007)</th>\n      <th>12 Angry Men (1957)</th>\n      <th>12 Angry Men (1997)</th>\n      <th>12 Chairs (1971)</th>\n      <th>12 Chairs (1976)</th>\n      <th>12 Rounds (2009)</th>\n      <th>12 Years a Slave (2013)</th>\n      <th>...</th>\n      <th>Zathura (2005)</th>\n      <th>Zatoichi and the Chest of Gold (Zatôichi senryô-kubi) (Zatôichi 6) (1964)</th>\n      <th>Zazie dans le métro (1960)</th>\n      <th>Zebraman (2004)</th>\n      <th>Zed &amp; Two Noughts, A (1985)</th>\n      <th>Zeitgeist: Addendum (2008)</th>\n      <th>Zeitgeist: Moving Forward (2011)</th>\n      <th>Zeitgeist: The Movie (2007)</th>\n      <th>Zelary (2003)</th>\n      <th>Zelig (1983)</th>\n      <th>Zero Dark Thirty (2012)</th>\n      <th>Zero Effect (1998)</th>\n      <th>Zero Theorem, The (2013)</th>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <th>Zeus and Roxanne (1997)</th>\n      <th>Zipper (2015)</th>\n      <th>Zodiac (2007)</th>\n      <th>Zombeavers (2014)</th>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <th>Zombie Strippers! (2008)</th>\n      <th>Zombieland (2009)</th>\n      <th>Zone 39 (1997)</th>\n      <th>Zone, The (La Zona) (2007)</th>\n      <th>Zookeeper (2011)</th>\n      <th>Zoolander (2001)</th>\n      <th>Zoolander 2 (2016)</th>\n      <th>Zoom (2006)</th>\n      <th>Zoom (2015)</th>\n      <th>Zootopia (2016)</th>\n      <th>Zulu (1964)</th>\n      <th>Zulu (2013)</th>\n      <th>[REC] (2007)</th>\n      <th>[REC]² (2009)</th>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <th>eXistenZ (1999)</th>\n      <th>xXx (2002)</th>\n      <th>xXx: State of the Union (2005)</th>\n      <th>¡Three Amigos! (1986)</th>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>'71 (2014)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      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<td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'Hellboy': The Seeds of Creation (2004)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'Round Midnight (1986)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'Salem's Lot (2004)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'Til There Was You (1997)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.445566</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      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<td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'burbs, The (1989)</th>\n      <td>NaN</td>\n      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     <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.548924e-03</td>\n      <td>3.577651e-01</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.006471</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.947352e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-1.849562e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.000865</td>\n      <td>0.219989</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>3.490843e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.069910e-02</td>\n      <td>-3.527098e-03</td>\n      <td>0.000000</td>\n      <td>1.683581e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>'night Mother (1986)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>(500) Days of Summer (2009)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>5.210830e-02</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>2.721766e-01</td>\n      <td>NaN</td>\n      <td>3.261631e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.012065</td>\n      <td>-0.121854</td>\n      <td>5.530715e-02</td>\n      <td>4.459957e-02</td>\n      <td>7.833834e-02</td>\n      <td>NaN</td>\n      <td>-3.518283e-02</td>\n      <td>0.014213</td>\n      <td>NaN</td>\n      <td>0.041124</td>\n      <td>2.849158e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.230149e-01</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.170561</td>\n      <td>NaN</td>\n      <td>3.939197e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.136212</td>\n      <td>...</td>\n      <td>-0.132658</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.017408</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.034816</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.425203e-03</td>\n      <td>1.009205e-01</td>\n      <td>0.150756</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.385835</td>\n      <td>0.104447</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.730610e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.010714</td>\n      <td>1.955755e-02</td>\n      <td>0.156670</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006648</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.091372</td>\n      <td>3.798686e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-7.527708e-02</td>\n      <td>1.558884e-01</td>\n      <td>0.110782</td>\n      <td>4.333034e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>*batteries not included (1987)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>8.030236e-02</td>\n      <td>NaN</td>\n      <td>2.721766e-01</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>-2.599073e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.046026</td>\n      <td>-0.464835</td>\n      <td>2.866830e-01</td>\n      <td>-1.291593e-25</td>\n      <td>9.900048e-02</td>\n      <td>NaN</td>\n      <td>-6.784953e-26</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.038991</td>\n      <td>5.318181e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.440596e-25</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-1.175432e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.011501</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.126773</td>\n      <td>2.854261e-02</td>\n      <td>1.260750e-01</td>\n      <td>0.536745</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.060059</td>\n      <td>0.464835</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.929451e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>6.989364e-02</td>\n      <td>0.464835</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-1.383986e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.947366e-02</td>\n      <td>2.511177e-01</td>\n      <td>0.000000</td>\n      <td>1.292903e-01</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...All the Marbles (1981)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...And Justice for All (1979)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>2.715601e-01</td>\n      <td>NaN</td>\n      <td>3.261631e-26</td>\n      <td>-2.599073e-25</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1.609353e-25</td>\n      <td>1.972050e-25</td>\n      <td>8.314612e-02</td>\n      <td>NaN</td>\n      <td>1.030543e-25</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.109041</td>\n      <td>-6.047545e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.195367e-25</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-3.034952e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-1.122009e-25</td>\n      <td>1.141176e-25</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.063541</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.035198e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>6.220017e-26</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.308607</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>2.110485e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-2.283020e-02</td>\n      <td>-6.707703e-26</td>\n      <td>0.000000</td>\n      <td>1.760288e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>00 Schneider - Jagd auf Nihil Baxter (1994)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1-900 (06) (1994)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 (1979)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.445566</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>-1.206538e-02</td>\n      <td>-4.602551e-02</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.049507</td>\n      <td>-3.053326e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>7.071746e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-0.049507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-3.039940e-03</td>\n      <td>0.000000e+00</td>\n      <td>-0.057166</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003776</td>\n      <td>-0.049507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.358466e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-4.863549e-03</td>\n      <td>-0.049507</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>-2.674537e-02</td>\n      <td>0.000000</td>\n      <td>-1.996794e-03</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 Cent Pistol (2015)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>-1.218544e-01</td>\n      <td>-4.648348e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.049507</td>\n      <td>1.000000</td>\n      <td>-3.083708e-01</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.442269</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-3.070189e-02</td>\n      <td>0.000000e+00</td>\n      <td>-0.577350</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.038131</td>\n      <td>-0.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.371983e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-4.911944e-02</td>\n      <td>-0.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.017137</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>-2.701150e-01</td>\n      <td>0.000000</td>\n      <td>-2.016663e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 Cloverfield Lane (2016)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-2.666399e-26</td>\n      <td>NaN</td>\n      <td>5.530715e-02</td>\n      <td>2.866830e-01</td>\n      <td>NaN</td>\n      <td>-1.609353e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.030533</td>\n      <td>-0.308371</td>\n      <td>1.000000e+00</td>\n      <td>-8.008406e-26</td>\n      <td>-4.330338e-04</td>\n      <td>NaN</td>\n      <td>2.801815e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.008322</td>\n      <td>-1.916372e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-8.932944e-26</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.727747e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.057224</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.084101</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.377100e-02</td>\n      <td>-4.644271e-26</td>\n      <td>0.404632</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.055067</td>\n      <td>0.392472</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.142808e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.196691</td>\n      <td>5.344430e-02</td>\n      <td>0.392472</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.044564</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.003422</td>\n      <td>-1.835237e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.546023e-02</td>\n      <td>1.655221e-01</td>\n      <td>-0.059468</td>\n      <td>1.243760e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 Items or Less (2006)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>3.263227e-26</td>\n      <td>NaN</td>\n      <td>4.459957e-02</td>\n      <td>-1.291593e-25</td>\n      <td>NaN</td>\n      <td>1.972050e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-8.008406e-26</td>\n      <td>1.000000e+00</td>\n      <td>3.363886e-26</td>\n      <td>NaN</td>\n      <td>-1.049146e-01</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.055455</td>\n      <td>2.289521e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-2.428610e-01</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.196116</td>\n      <td>NaN</td>\n      <td>5.440884e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-0.152535</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-5.554700e-26</td>\n      <td>5.687207e-26</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.012212</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.393862e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-2.416825e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.047731e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.772458e-02</td>\n      <td>1.002452e-01</td>\n      <td>0.113228</td>\n      <td>4.875771e-26</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 Things I Hate About You (1999)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.096436e-01</td>\n      <td>NaN</td>\n      <td>7.833834e-02</td>\n      <td>9.900048e-02</td>\n      <td>NaN</td>\n      <td>8.314612e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-4.330338e-04</td>\n      <td>3.363886e-26</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>9.789038e-02</td>\n      <td>0.095644</td>\n      <td>NaN</td>\n      <td>0.019246</td>\n      <td>1.042513e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.600227e-02</td>\n      <td>NaN</td>\n      <td>-0.136448</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.021021</td>\n      <td>NaN</td>\n      <td>-1.617572e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.031706</td>\n      <td>...</td>\n      <td>-0.055704</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.048915</td>\n      <td>-0.048915</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.048915</td>\n      <td>-4.911110e-02</td>\n      <td>3.390949e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.068450</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.846235e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.025986</td>\n      <td>-3.451590e-02</td>\n      <td>-0.002574</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.055426</td>\n      <td>0.067356</td>\n      <td>NaN</td>\n      <td>0.051225</td>\n      <td>3.614307e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.043713e-01</td>\n      <td>5.685969e-02</td>\n      <td>0.001820</td>\n      <td>1.883163e-01</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10 Years (2011)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10,000 BC (2008)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>6.722994e-03</td>\n      <td>NaN</td>\n      <td>-3.518283e-02</td>\n      <td>-6.784953e-26</td>\n      <td>NaN</td>\n      <td>1.030543e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>2.801815e-03</td>\n      <td>-1.049146e-01</td>\n      <td>9.789038e-02</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>0.015868</td>\n      <td>NaN</td>\n      <td>0.032264</td>\n      <td>-4.185468e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.406071e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.224412</td>\n      <td>NaN</td>\n      <td>5.723602e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.126940</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.055525</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>4.773198e-03</td>\n      <td>1.280076e-02</td>\n      <td>0.032057</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.179886</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.905330e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>6.665626e-02</td>\n      <td>0.055525</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.062149</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.023722</td>\n      <td>5.506376e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.584945e-03</td>\n      <td>2.586724e-02</td>\n      <td>-0.066745</td>\n      <td>6.872044e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>100 Girls (2000)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.421338e-02</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>9.564355e-02</td>\n      <td>NaN</td>\n      <td>1.586751e-02</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.016663</td>\n      <td>-2.528994e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.250000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.038210</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.127158e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>7.802412e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.056969</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>1.427075e-02</td>\n      <td>0.000000</td>\n      <td>5.292638e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>100 Streets (2016)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>101 Dalmatians (1996)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.618341e-01</td>\n      <td>NaN</td>\n      <td>4.112398e-02</td>\n      <td>-3.899093e-02</td>\n      <td>NaN</td>\n      <td>1.090412e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>8.321671e-03</td>\n      <td>-5.545548e-02</td>\n      <td>1.924567e-02</td>\n      <td>NaN</td>\n      <td>3.226399e-02</td>\n      <td>0.016663</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>3.145453e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.411867e-02</td>\n      <td>NaN</td>\n      <td>0.008658</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.020198</td>\n      <td>NaN</td>\n      <td>1.601612e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.005302</td>\n      <td>...</td>\n      <td>0.076751</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.049474</td>\n      <td>0.107606</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.008658</td>\n      <td>NaN</td>\n      <td>0.124922</td>\n      <td>1.359461e-02</td>\n      <td>1.770215e-01</td>\n      <td>-0.004999</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.002684</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.131558e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.044304</td>\n      <td>2.332939e-02</td>\n      <td>0.049474</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.149039</td>\n      <td>0.164423</td>\n      <td>NaN</td>\n      <td>-0.102315</td>\n      <td>-9.392654e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.158089e-02</td>\n      <td>2.910147e-02</td>\n      <td>0.088333</td>\n      <td>6.946323e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>101 Dalmatians (One Hundred and One Dalmatians) (1961)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-2.036913e-02</td>\n      <td>NaN</td>\n      <td>2.849158e-02</td>\n      <td>5.318181e-02</td>\n      <td>NaN</td>\n      <td>-6.047545e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1.916372e-01</td>\n      <td>2.289521e-26</td>\n      <td>1.042513e-02</td>\n      <td>NaN</td>\n      <td>-4.185468e-02</td>\n      <td>-0.025290</td>\n      <td>NaN</td>\n      <td>0.003145</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.279160e-01</td>\n      <td>NaN</td>\n      <td>0.081510</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003993</td>\n      <td>NaN</td>\n      <td>9.127720e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.045921</td>\n      <td>...</td>\n      <td>-0.042150</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.009781</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.061947</td>\n      <td>-8.288295e-02</td>\n      <td>2.763441e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.017134</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.604997e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.505979e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.073498</td>\n      <td>-0.159076</td>\n      <td>NaN</td>\n      <td>-0.041391</td>\n      <td>5.407216e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-4.548078e-02</td>\n      <td>-1.050186e-01</td>\n      <td>0.000000</td>\n      <td>-3.056488e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>101 Dalmatians II: Patch's London Adventure (2003)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>101 Reykjavik (101 Reykjavík) (2000)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>102 Dalmatians (2000)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>3.631905e-26</td>\n      <td>NaN</td>\n      <td>-1.230149e-01</td>\n      <td>-1.440596e-25</td>\n      <td>NaN</td>\n      <td>2.195367e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-8.932944e-26</td>\n      <td>-2.428610e-01</td>\n      <td>2.600227e-02</td>\n      <td>NaN</td>\n      <td>2.406071e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.024119</td>\n      <td>-1.279160e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>-0.066667</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.095258</td>\n      <td>NaN</td>\n      <td>2.279804e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.017010</td>\n      <td>...</td>\n      <td>0.018144</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.233333</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>2.490264e-02</td>\n      <td>6.336467e-26</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.043677</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-8.404275e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>4.167710e-04</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.023176</td>\n      <td>0.047140</td>\n      <td>NaN</td>\n      <td>0.126137</td>\n      <td>-5.819144e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.708162e-26</td>\n      <td>2.393960e-02</td>\n      <td>-0.164992</td>\n      <td>5.431266e-26</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10th &amp; Wolf (2006)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10th Kingdom, The (2000)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>-1.364476e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.008658</td>\n      <td>8.150980e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-6.666667e-02</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.204124</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.371983e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>4.733328e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>Zero Dark Thirty (2012)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.548924e-03</td>\n      <td>NaN</td>\n      <td>1.425203e-03</td>\n      <td>2.854261e-02</td>\n      <td>NaN</td>\n      <td>-1.122009e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.003040</td>\n      <td>-0.030702</td>\n      <td>1.377100e-02</td>\n      <td>-5.554700e-26</td>\n      <td>-4.911110e-02</td>\n      <td>NaN</td>\n      <td>4.773198e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.013595</td>\n      <td>-8.288295e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.490264e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-1.625944e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.035513</td>\n      <td>...</td>\n      <td>-0.020890</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>1.000000e+00</td>\n      <td>-5.204457e-04</td>\n      <td>0.035451</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.082374</td>\n      <td>0.030702</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-3.272591e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-9.487065e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.030967</td>\n      <td>-0.021710</td>\n      <td>NaN</td>\n      <td>-0.018739</td>\n      <td>3.059530e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.606033e-03</td>\n      <td>4.351407e-02</td>\n      <td>0.000000</td>\n      <td>2.476614e-03</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zero Effect (1998)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>3.577651e-01</td>\n      <td>NaN</td>\n      <td>1.009205e-01</td>\n      <td>1.260750e-01</td>\n      <td>NaN</td>\n      <td>1.141176e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-4.644271e-26</td>\n      <td>5.687207e-26</td>\n      <td>3.390949e-02</td>\n      <td>NaN</td>\n      <td>1.280076e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.177021</td>\n      <td>2.763441e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.336467e-26</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>3.848450e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.025375</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.262750</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.135613</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-5.204457e-04</td>\n      <td>1.000000e+00</td>\n      <td>0.078296</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.079681</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>7.905689e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>8.023742e-04</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000291</td>\n      <td>-0.005993</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>6.086388e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.040064e-01</td>\n      <td>1.185121e-03</td>\n      <td>0.000000</td>\n      <td>9.005475e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zero Theorem, The (2013)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.507557e-01</td>\n      <td>5.367450e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.057166</td>\n      <td>-0.577350</td>\n      <td>4.046318e-01</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>3.205721e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.004999</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-8.029727e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.288675</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.288675</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>3.545148e-02</td>\n      <td>7.829603e-02</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.076052</td>\n      <td>0.866025</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-3.229391e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>8.404612e-02</td>\n      <td>0.577350</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.009894</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.011746</td>\n      <td>-6.299408e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>2.440174e-01</td>\n      <td>-0.204124</td>\n      <td>2.328642e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zero de conduite (Zero for Conduct) (Zéro de conduite: Jeunes diables au collège) (1933)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zeus and Roxanne (1997)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zipper (2015)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zodiac (2007)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-6.470726e-03</td>\n      <td>NaN</td>\n      <td>3.858352e-01</td>\n      <td>6.005871e-02</td>\n      <td>NaN</td>\n      <td>-6.354125e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003776</td>\n      <td>0.038131</td>\n      <td>5.506738e-02</td>\n      <td>1.221171e-02</td>\n      <td>6.844968e-02</td>\n      <td>NaN</td>\n      <td>1.798864e-01</td>\n      <td>-0.038210</td>\n      <td>NaN</td>\n      <td>0.002684</td>\n      <td>1.713386e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.367739e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.084911</td>\n      <td>NaN</td>\n      <td>6.563524e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.099534</td>\n      <td>...</td>\n      <td>-0.036323</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.263451</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.131725</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-8.237438e-02</td>\n      <td>7.968126e-02</td>\n      <td>0.076052</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.131725</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.885846e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-2.804820e-03</td>\n      <td>0.065863</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.089464</td>\n      <td>-0.026963</td>\n      <td>NaN</td>\n      <td>0.027363</td>\n      <td>-3.706573e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.268057e-02</td>\n      <td>-2.701073e-02</td>\n      <td>-0.093144</td>\n      <td>6.351533e-03</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zombeavers (2014)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.044466e-01</td>\n      <td>4.648348e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.049507</td>\n      <td>-0.500000</td>\n      <td>3.924719e-01</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.500000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>3.070189e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.866025</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.131725</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.593469e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>9.645272e-02</td>\n      <td>0.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.017137</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.020345</td>\n      <td>-1.091089e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>1.525355e-01</td>\n      <td>-0.353553</td>\n      <td>2.016663e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zombie (a.k.a. Zombie 2: The Dead Are Among Us) (Zombi 2) (1979)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zombie Strippers! (2008)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zombieland (2009)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>5.947352e-02</td>\n      <td>NaN</td>\n      <td>-1.730610e-02</td>\n      <td>7.929451e-02</td>\n      <td>NaN</td>\n      <td>-5.035198e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.001358</td>\n      <td>0.013720</td>\n      <td>1.142808e-01</td>\n      <td>4.393862e-03</td>\n      <td>3.846235e-02</td>\n      <td>NaN</td>\n      <td>6.905330e-02</td>\n      <td>0.051272</td>\n      <td>NaN</td>\n      <td>-0.051316</td>\n      <td>9.604997e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-8.404275e-02</td>\n      <td>NaN</td>\n      <td>0.013720</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.011202</td>\n      <td>NaN</td>\n      <td>2.196174e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.007612</td>\n      <td>...</td>\n      <td>-0.079852</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.055935</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.013720</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-3.272591e-02</td>\n      <td>7.905689e-02</td>\n      <td>-0.032294</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.068858</td>\n      <td>-0.055935</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.041570</td>\n      <td>-3.608389e-02</td>\n      <td>0.055935</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.039490</td>\n      <td>-0.069402</td>\n      <td>NaN</td>\n      <td>-0.047752</td>\n      <td>9.488399e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.476541e-02</td>\n      <td>3.158594e-02</td>\n      <td>0.039552</td>\n      <td>6.667142e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zone 39 (1997)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zone, The (La Zona) (2007)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zookeeper (2011)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.071374e-02</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>1.966905e-01</td>\n      <td>0.000000e+00</td>\n      <td>2.598554e-02</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.044304</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-2.648477e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-4.157032e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>-1.879814e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.053640</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.022538</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zoolander (2001)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-1.849562e-02</td>\n      <td>NaN</td>\n      <td>1.955755e-02</td>\n      <td>6.989364e-02</td>\n      <td>NaN</td>\n      <td>6.220017e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.004864</td>\n      <td>-0.049119</td>\n      <td>5.344430e-02</td>\n      <td>-2.416825e-02</td>\n      <td>-3.451590e-02</td>\n      <td>NaN</td>\n      <td>6.665626e-02</td>\n      <td>0.078024</td>\n      <td>NaN</td>\n      <td>0.023329</td>\n      <td>1.505979e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.167710e-04</td>\n      <td>NaN</td>\n      <td>0.047333</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.058336</td>\n      <td>NaN</td>\n      <td>2.128938e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.003160</td>\n      <td>...</td>\n      <td>-0.045089</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.048226</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.095560</td>\n      <td>-9.487065e-03</td>\n      <td>8.023742e-04</td>\n      <td>0.084046</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.002805</td>\n      <td>0.096453</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-3.608389e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.018798</td>\n      <td>1.000000e+00</td>\n      <td>0.096453</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.010543</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.054254</td>\n      <td>4.131587e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-4.048854e-04</td>\n      <td>-3.359687e-02</td>\n      <td>0.000000</td>\n      <td>-2.572918e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zoolander 2 (2016)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.566699e-01</td>\n      <td>4.648348e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.049507</td>\n      <td>-0.500000</td>\n      <td>3.924719e-01</td>\n      <td>0.000000e+00</td>\n      <td>-2.574482e-03</td>\n      <td>NaN</td>\n      <td>5.552472e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.049474</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>7.935681e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>0.577350</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.065863</td>\n      <td>0.500000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.593469e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>9.645272e-02</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.095478</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>3.813388e-01</td>\n      <td>0.000000</td>\n      <td>2.016663e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zoom (2006)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zoom (2015)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zootopia (2016)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-8.645750e-04</td>\n      <td>NaN</td>\n      <td>6.648247e-03</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-0.017137</td>\n      <td>4.456437e-02</td>\n      <td>0.000000e+00</td>\n      <td>-5.542630e-02</td>\n      <td>NaN</td>\n      <td>-6.214870e-02</td>\n      <td>-0.056969</td>\n      <td>NaN</td>\n      <td>0.149039</td>\n      <td>7.349785e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.317590e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-2.471938e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.105110</td>\n      <td>...</td>\n      <td>0.118269</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.017137</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>3.096719e-02</td>\n      <td>2.905012e-04</td>\n      <td>0.009894</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.089464</td>\n      <td>0.017137</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.948952e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.053640</td>\n      <td>1.054284e-02</td>\n      <td>0.095478</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>0.122909</td>\n      <td>NaN</td>\n      <td>0.105392</td>\n      <td>-1.554615e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>8.366882e-03</td>\n      <td>4.204212e-02</td>\n      <td>-0.012118</td>\n      <td>-3.687316e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zulu (1964)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>2.199888e-01</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>3.086067e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>6.735606e-02</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.164423</td>\n      <td>-1.590757e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4.714045e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>9.834367e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.240563</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-2.170951e-02</td>\n      <td>-5.993291e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.026963</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-6.940221e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.122909</td>\n      <td>1.000000</td>\n      <td>NaN</td>\n      <td>0.215788</td>\n      <td>-3.086067e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.849457e-02</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>1.425996e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Zulu (2013)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[REC] (2007)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>-9.137224e-02</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-3.422027e-03</td>\n      <td>0.000000e+00</td>\n      <td>5.122480e-02</td>\n      <td>NaN</td>\n      <td>-2.372232e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.102315</td>\n      <td>-4.139100e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.261372e-01</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.074751</td>\n      <td>NaN</td>\n      <td>2.729656e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.207642</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.162758</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-1.873863e-02</td>\n      <td>0.000000e+00</td>\n      <td>-0.011746</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.027363</td>\n      <td>-0.020345</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-4.775201e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.022538</td>\n      <td>-5.425404e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.105392</td>\n      <td>0.215788</td>\n      <td>NaN</td>\n      <td>1.000000</td>\n      <td>-2.619352e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>-2.067568e-01</td>\n      <td>-0.201402</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[REC]² (2009)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>3.490843e-26</td>\n      <td>NaN</td>\n      <td>3.798686e-03</td>\n      <td>-1.383986e-25</td>\n      <td>NaN</td>\n      <td>2.110485e-25</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-1.835237e-02</td>\n      <td>1.047731e-25</td>\n      <td>3.614307e-26</td>\n      <td>NaN</td>\n      <td>5.506376e-26</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.093927</td>\n      <td>5.407216e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.819144e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>5.821771e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-0.296957</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.109109</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>3.059530e-01</td>\n      <td>6.086388e-26</td>\n      <td>-0.062994</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.037066</td>\n      <td>-0.109109</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9.488399e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>4.131587e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.155461</td>\n      <td>-0.308607</td>\n      <td>NaN</td>\n      <td>-0.261935</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.480866e-26</td>\n      <td>-4.854206e-03</td>\n      <td>0.077152</td>\n      <td>5.220386e-26</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>[REC]³ 3 Génesis (2012)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>anohana: The Flower We Saw That Day - The Movie (2013)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>eXistenZ (1999)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>6.069910e-02</td>\n      <td>NaN</td>\n      <td>-7.527708e-02</td>\n      <td>2.947366e-02</td>\n      <td>NaN</td>\n      <td>-2.283020e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>4.546023e-02</td>\n      <td>9.772458e-02</td>\n      <td>-1.043713e-01</td>\n      <td>NaN</td>\n      <td>9.584945e-03</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.041581</td>\n      <td>-4.548078e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-5.708162e-26</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.010678</td>\n      <td>NaN</td>\n      <td>-1.659053e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.017796</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.217961</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-1.606033e-03</td>\n      <td>3.040064e-01</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.062681</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.476541e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-4.048854e-04</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.008367</td>\n      <td>-0.018495</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-5.480866e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.000000e+00</td>\n      <td>3.784587e-02</td>\n      <td>0.000000</td>\n      <td>-3.410933e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>xXx (2002)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-3.527098e-03</td>\n      <td>NaN</td>\n      <td>1.558884e-01</td>\n      <td>2.511177e-01</td>\n      <td>NaN</td>\n      <td>-6.707703e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.026745</td>\n      <td>-0.270115</td>\n      <td>1.655221e-01</td>\n      <td>1.002452e-01</td>\n      <td>5.685969e-02</td>\n      <td>NaN</td>\n      <td>2.586724e-02</td>\n      <td>0.014271</td>\n      <td>NaN</td>\n      <td>0.029101</td>\n      <td>-1.050186e-01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.393960e-02</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.059678</td>\n      <td>NaN</td>\n      <td>2.471903e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.022703</td>\n      <td>...</td>\n      <td>-0.009514</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.076268</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>4.351407e-02</td>\n      <td>1.185121e-03</td>\n      <td>0.244017</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.027011</td>\n      <td>0.152536</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.158594e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-3.359687e-02</td>\n      <td>0.381339</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.042042</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.206757</td>\n      <td>-4.854206e-03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.784587e-02</td>\n      <td>1.000000e+00</td>\n      <td>0.244930</td>\n      <td>1.854832e-02</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>xXx: State of the Union (2005)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>1.107823e-01</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>-5.946848e-02</td>\n      <td>1.132277e-01</td>\n      <td>1.820434e-03</td>\n      <td>NaN</td>\n      <td>-6.674524e-02</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.088333</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-1.649916e-01</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.433013</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>-0.336788</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.353553</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000e+00</td>\n      <td>-0.204124</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.093144</td>\n      <td>-0.353553</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.955180e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000e+00</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.012118</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>-0.201402</td>\n      <td>7.715167e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000e+00</td>\n      <td>2.449296e-01</td>\n      <td>1.000000</td>\n      <td>0.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>¡Three Amigos! (1986)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.683581e-02</td>\n      <td>NaN</td>\n      <td>4.333034e-02</td>\n      <td>1.292903e-01</td>\n      <td>NaN</td>\n      <td>1.760288e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.001997</td>\n      <td>-0.020167</td>\n      <td>1.243760e-02</td>\n      <td>4.875771e-26</td>\n      <td>1.883163e-01</td>\n      <td>NaN</td>\n      <td>6.872044e-02</td>\n      <td>0.052926</td>\n      <td>NaN</td>\n      <td>0.069463</td>\n      <td>-3.056488e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5.431266e-26</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>1.243048e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006469</td>\n      <td>...</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>2.476614e-03</td>\n      <td>9.005475e-02</td>\n      <td>0.023286</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.006352</td>\n      <td>0.020167</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>6.667142e-02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>-2.572918e-02</td>\n      <td>0.020167</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-0.036873</td>\n      <td>0.014260</td>\n      <td>NaN</td>\n      <td>0.000000</td>\n      <td>5.220386e-26</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-3.410933e-02</td>\n      <td>1.854832e-02</td>\n      <td>0.000000</td>\n      <td>1.000000e+00</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>À nous la liberté (Freedom for Us) (1931)</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>9719 rows × 9719 columns</p>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "168a49cf049528b49cf2bdc2a096efb127cf15c0"
      },
      "cell_type": "markdown",
      "source": "* There are lot of NaN values and that is because when we are calculating the Pearson correlation, if the rating vector has all the values same for eg -> [3.0 , 3.0, 3.0, 3.0, ....], then the **Standard Deviation** is zero and division by zero is undefined, and thus its correlation with any other rating vector is NaN.\n\n* Since there are many movies that are rated only by 1 user , there the whole column mean is filled with the rating of that user, and therefore it's Pearson correlation gives NaN values with any other column."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c1609da96c0e281fdba2a31ceacde92b19bf0d95"
      },
      "cell_type": "code",
      "source": "item_corr_matrix = item_util_matrix.corr()",
      "execution_count": 52,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "1a0b2bc73139ff14d1736cfafd96a5eed9a65534"
      },
      "cell_type": "code",
      "source": "jurassic_park_corr = item_corr_matrix['Jurassic Park (1993)']\njurassic_park_corr = jurassic_park_corr.sort_values(ascending=False)\njurassic_park_corr.dropna(inplace=True)",
      "execution_count": 53,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "580e0de18bb414166273d1f20e68e5533103cd8a"
      },
      "cell_type": "code",
      "source": "movies_similar_to_jurassic_park = pd.DataFrame(data=jurassic_park_corr.values, columns=['Correlation'], \n                                               index = jurassic_park_corr.index)\nmovies_similar_to_jurassic_park = movies_similar_to_jurassic_park.join(df_n_ratings['total ratings'])\nmovies_similar_to_jurassic_park.head(10)",
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 54,
          "data": {
            "text/plain": "                                             Correlation  total ratings\ntitle                                                                  \nJurassic Park (1993)                            1.000000            238\nFugitive, The (1993)                            0.324717            190\nLethal Weapon (1987)                            0.318646             75\nIndependence Day (a.k.a. ID4) (1996)            0.263629            202\nMission: Impossible (1996)                      0.258080            162\nGhostbusters (a.k.a. Ghost Busters) (1984)      0.256527            120\nMulan (1998)                                    0.255672             45\nRise of the Planet of the Apes (2011)           0.248134             27\nBug's Life, A (1998)                            0.240964             92\nIndiana Jones and the Temple of Doom (1984)     0.239826            108",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Correlation</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Jurassic Park (1993)</th>\n      <td>1.000000</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>Fugitive, The (1993)</th>\n      <td>0.324717</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>Lethal Weapon (1987)</th>\n      <td>0.318646</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>Independence Day (a.k.a. ID4) (1996)</th>\n      <td>0.263629</td>\n      <td>202</td>\n    </tr>\n    <tr>\n      <th>Mission: Impossible (1996)</th>\n      <td>0.258080</td>\n      <td>162</td>\n    </tr>\n    <tr>\n      <th>Ghostbusters (a.k.a. Ghost Busters) (1984)</th>\n      <td>0.256527</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>Mulan (1998)</th>\n      <td>0.255672</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>Rise of the Planet of the Apes (2011)</th>\n      <td>0.248134</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>Bug's Life, A (1998)</th>\n      <td>0.240964</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>Indiana Jones and the Temple of Doom (1984)</th>\n      <td>0.239826</td>\n      <td>108</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "786d7fe64730c72a1f2ae15b4713f9732b3205c0"
      },
      "cell_type": "code",
      "source": "movies_similar_to_jurassic_park = movies_similar_to_jurassic_park[1:]\nmovies_similar_to_jurassic_park[ movies_similar_to_jurassic_park['total ratings'] > 100 ].sort_values(ascending=False,\n                                                                                          by=['Correlation']).head(10)",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 55,
          "data": {
            "text/plain": "                                             Correlation  total ratings\ntitle                                                                  \nFugitive, The (1993)                            0.324717            190\nIndependence Day (a.k.a. ID4) (1996)            0.263629            202\nMission: Impossible (1996)                      0.258080            162\nGhostbusters (a.k.a. Ghost Busters) (1984)      0.256527            120\nIndiana Jones and the Temple of Doom (1984)     0.239826            108\nDie Hard (1988)                                 0.239294            145\nAladdin (1992)                                  0.227711            183\nBraveheart (1995)                               0.222085            237\nSpeed (1994)                                    0.220720            171\nTerminator 2: Judgment Day (1991)               0.219382            224",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Correlation</th>\n      <th>total ratings</th>\n    </tr>\n    <tr>\n      <th>title</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Fugitive, The (1993)</th>\n      <td>0.324717</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>Independence Day (a.k.a. ID4) (1996)</th>\n      <td>0.263629</td>\n      <td>202</td>\n    </tr>\n    <tr>\n      <th>Mission: Impossible (1996)</th>\n      <td>0.258080</td>\n      <td>162</td>\n    </tr>\n    <tr>\n      <th>Ghostbusters (a.k.a. Ghost Busters) (1984)</th>\n      <td>0.256527</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>Indiana Jones and the Temple of Doom (1984)</th>\n      <td>0.239826</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>Die Hard (1988)</th>\n      <td>0.239294</td>\n      <td>145</td>\n    </tr>\n    <tr>\n      <th>Aladdin (1992)</th>\n      <td>0.227711</td>\n      <td>183</td>\n    </tr>\n    <tr>\n      <th>Braveheart (1995)</th>\n      <td>0.222085</td>\n      <td>237</td>\n    </tr>\n    <tr>\n      <th>Speed (1994)</th>\n      <td>0.220720</td>\n      <td>171</td>\n    </tr>\n    <tr>\n      <th>Terminator 2: Judgment Day (1991)</th>\n      <td>0.219382</td>\n      <td>224</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "21ebcaf33da5d820195c38e2578aaca04e5d22a0"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "ae4476ba564b485600e9123ae57aa5b6fdfb812f"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.3\"></a>\n## 3.3) Model Based Collaborative Filtering"
    },
    {
      "metadata": {
        "_uuid": "064b203ebb86ffdede5677e4cf9426447ceadc06"
      },
      "cell_type": "markdown",
      "source": "Here we will be using dimensionality reduction methods to improve robustness and accuracy of Memory-Based CF. Basically, we compress user-item matrix into a low dimension matrix. We use techniques like SVD which is a low-rank factorization method, PCA which is used for dimensionaliry reduction etc.\n\nModel-based methods are based on matrix factorization and are better at dealing with sparsity. \n* We will be using a \"Surprise\" library to implement SVD, KNN and NMF.\n* You can find its documentation here >  [https://surprise.readthedocs.io/en/stable/](https://surprise.readthedocs.io/en/stable/)\n* Surprise Library has almost all the algorithms implemented that are required for model-based Recommendation systems."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0e3611072c125c64152237b86c355ac22754ae23"
      },
      "cell_type": "code",
      "source": "from surprise import Reader, Dataset, evaluate, KNNBasic, SVD, NMF\nfrom surprise.model_selection import GridSearchCV, cross_validate",
      "execution_count": 56,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "a44b128f8dfb546899d4c1bd4b27013ea050d6a3"
      },
      "cell_type": "markdown",
      "source": "To load a dataset from a pandas dataframe, you will need the **load_from_df()** method. You will also need a **Reader** object, but only the rating_scale parameter must be specified.\n\nThe Reader class is used to parse a file containing ratings."
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "0bf01bbc08a439cce0bbf1ed60b13e21a47559ff"
      },
      "cell_type": "code",
      "source": "reader = Reader(rating_scale=(0.5, 5.0))\n\ndata = Dataset.load_from_df( ratings[['userId', 'movieId', 'rating']], reader = reader )",
      "execution_count": 57,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "1207eb620682aad721b471f7a4c60b070cfcb525"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.3.1\"></a>\n### 3.3.1) K-Nearest Neighbours (KNN)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a1808c50c2184758606f7bfe6255721cb5c43ac0"
      },
      "cell_type": "code",
      "source": "# Split data into k-folds\n# data.split(n_folds=5)",
      "execution_count": 58,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c7851ba00d89084bbfdf5a141ca60231a855b6dc"
      },
      "cell_type": "code",
      "source": "# Compute Mean Squared Distance Similarity\nsim_options = {'name' : 'msd'}\n\nalgo = KNNBasic(k=20, sim_options=sim_options )\ncross_validate(algo=algo, data=data, measures=['RMSE'], cv=5, verbose=True)",
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Computing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nEvaluating RMSE of algorithm KNNBasic on 5 split(s).\n\n                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \nRMSE (testset)    0.9357  0.9470  0.9392  0.9415  0.9413  0.9409  0.0037  \nFit time          0.08    0.11    0.11    0.10    0.10    0.10    0.01    \nTest time         1.03    1.15    1.05    1.03    1.04    1.06    0.05    \n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 59,
          "data": {
            "text/plain": "{'test_rmse': array([0.93573192, 0.94698395, 0.93921905, 0.941499  , 0.94131549]),\n 'fit_time': (0.08251333236694336,\n  0.10508251190185547,\n  0.11417102813720703,\n  0.10291433334350586,\n  0.1047666072845459),\n 'test_time': (1.0274007320404053,\n  1.1480109691619873,\n  1.0450000762939453,\n  1.0250494480133057,\n  1.0391650199890137)}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "36bd64d20d57e6d57709f98eb8d6b380cd49894c"
      },
      "cell_type": "markdown",
      "source": "**Tuning KNN using GridSearchCV**"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8cdc6cc95681aa214fb1631da7a9810576cabd00"
      },
      "cell_type": "code",
      "source": "n_neighbours = [10, 20, 30]\nparam_grid = {'n_neighbours' : n_neighbours}\n\ngs = GridSearchCV(KNNBasic, measures=['RMSE'], param_grid=param_grid)\ngs.fit(data)\n\nprint('\\n\\n###############')\n# Best RMSE score\nprint('Best Score :', gs.best_score['rmse'])\n\n# Combination of parameters that gave the best RMSE score\nprint('Best Parameters :', gs.best_params['rmse'])\nprint('###############')",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Computing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\nComputing the msd similarity matrix...\nDone computing similarity matrix.\n\n\n###############\nBest Score : 0.9467024391466696\nBest Parameters : {'n_neighbours': 10}\n###############\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "f472339bd459eed99690d0b825e9cecc53cdd6c2"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.3.2\"></a>\n### 3.3.2) Singular Value Decomposition (SVD)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a56b1de2fa83fa7a5e5096ff8e82ab18daa4fc83"
      },
      "cell_type": "code",
      "source": "algo = SVD()\ncross_validate(algo=algo, data=data, measures=['RMSE'], cv=5, verbose=True)",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Evaluating RMSE of algorithm SVD on 5 split(s).\n\n                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \nRMSE (testset)    0.8643  0.8714  0.8710  0.8724  0.8865  0.8731  0.0073  \nFit time          3.96    4.00    4.64    4.68    4.03    4.26    0.32    \nTest time         0.12    0.14    0.14    0.12    0.23    0.15    0.04    \n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 61,
          "data": {
            "text/plain": "{'test_rmse': array([0.86432593, 0.87138103, 0.87100005, 0.87241928, 0.88648528]),\n 'fit_time': (3.964674472808838,\n  4.001863241195679,\n  4.635687828063965,\n  4.678292274475098,\n  4.025472640991211),\n 'test_time': (0.12459993362426758,\n  0.13807463645935059,\n  0.1396491527557373,\n  0.1237175464630127,\n  0.23362040519714355)}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "9e4b565ea64505d6344dc7720b49bbb4597e672c"
      },
      "cell_type": "markdown",
      "source": "**Fine Tuning SVD using GridSearchCV**\n\n[Click here to view the parameters](https://surprise.readthedocs.io/en/stable/matrix_factorization.html)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "80b0898392b78475299fa0e57b0c529147e7e02b"
      },
      "cell_type": "code",
      "source": "param_grid = {'n_factors' : [50, 75], 'lr_all' : [0.5, 0.05], 'reg_all' : [0.06, 0.04]}\n\ngs = GridSearchCV(algo_class=SVD, measures=['RMSE'], param_grid=param_grid)\ngs.fit(data)\n\nprint('\\n###############')\n# Best RMSE score\nprint('Best Score :', gs.best_score['rmse'])\n\n# Combination of parameters that gave the best RMSE score\nprint('Best Parameters :', gs.best_params['rmse'])\nprint('###############')",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": "\n###############\nBest Score : 0.8636195525206292\nBest Parameters : {'n_factors': 75, 'lr_all': 0.05, 'reg_all': 0.06}\n###############\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "a6e2139dd63e0dd6eb2749022301d3e1fa59193b"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_3.3.3\"></a>\n### 3.3.3) Non-Negative Matrix Factorization (NMF)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8b6b979d7ea7448ecaf3bb29b4d83fdfdf63ba97"
      },
      "cell_type": "code",
      "source": "algo = NMF()\ncross_validate(data=data, algo=algo, measures=['RMSE'], cv=5, verbose=True)",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Evaluating RMSE of algorithm NMF on 5 split(s).\n\n                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \nRMSE (testset)    0.9400  0.9196  0.9187  0.9220  0.9110  0.9222  0.0096  \nFit time          4.86    5.32    5.09    5.24    5.32    5.17    0.18    \nTest time         0.12    0.12    0.11    0.11    0.10    0.11    0.01    \n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 63,
          "data": {
            "text/plain": "{'test_rmse': array([0.9399784 , 0.91963814, 0.9186779 , 0.92196156, 0.91097375]),\n 'fit_time': (4.855257987976074,\n  5.319264650344849,\n  5.094705820083618,\n  5.2402050495147705,\n  5.324464559555054),\n 'test_time': (0.11967062950134277,\n  0.11708450317382812,\n  0.10543513298034668,\n  0.11367964744567871,\n  0.10481405258178711)}"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "c2474a77d53e5ade18122c1fa1a19e9ebee458a9"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "ce42dd608233fb22615f05c702fc9eb20f3ce4ed"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4\"></a>\n# 4) Matrix Factorization using Deep Learning (Keras)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8368a5686303cd2079f4abc987dfbc972aab477a"
      },
      "cell_type": "code",
      "source": "from keras.layers import Embedding, Input, dot, concatenate\nfrom keras.models import Model\nfrom IPython.display import SVG\nfrom keras.utils.vis_utils import model_to_dot",
      "execution_count": 64,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "fb98e82422fe3453d62918952cba0c30cbbaff4e"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.1\"></a>\n## 4.1)Splitting Data into Train and Validation Set"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "6ddeb883e5fe1879c6c08b98bdd0967d556d0b6f"
      },
      "cell_type": "code",
      "source": "X = ratings.iloc[:,:2]\nY = ratings.iloc[:,2]\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 66)",
      "execution_count": 65,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "7f128e596382ff4165b63523d01275af6d708dff"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.2\"></a>\n## 4.2) Building the Model using Embedding Layers\n\nAn embedding is a mapping from discrete objects, such as words or movies in our case, to a vector of continuous values. These are used to find similarities between discrete objects. \n\nThe concept behind matrix factorization models is that the preferences of a user can be determined by a small number of hidden factors. And these are called as **Embeddings**.\n\n![Imgur](https://i.imgur.com/zGQJFLD.png)\n\nAs you can see in the image, there are 2 features both for user and the items. These are the latent factors or the hidden factors. These factors have a value for the corresponding user and determines to what extend that user likes the feature.\n\nFor Eg. the features for the user could be ->\n* How much he likes action movies?\n* Whether he likes old movies?\n\nAnd the features for the movies could be ->\n* To what scale is it an action movie?\n* Whether the movie is recently released?\n\nAnd finally we take **dot product** which gives us the user's rating for the movie, \n\n**Estimated Rating** = [ (How much he likes action movies?) x (To what scale is it an action movie) ]  +  [ (Whether he likes old movies?) x (Whether the movie is recently released) ]"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "79c2c73e0e8ac1e0dd35c48acedfe4840329e759"
      },
      "cell_type": "code",
      "source": "# The number of latent factors for the embedding\nn_latent_factors = 50\n\n# no of users and movies\nn_users, n_movies = len(ratings['userId'].unique()), len(ratings['movieId'].unique()) ",
      "execution_count": 66,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "247040fe84e0161a6002b11213a799acbcf48567"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.3\"></a>\n## 4.3) Architecture 1"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "207734899b02951feb88e6f7add7393414fe8c7a"
      },
      "cell_type": "code",
      "source": "# Model Architecture\n\n\n# User Embeddings\nuser_input = Input(shape=(1,), name='User_Input')\nuser_embeddings = Embedding(input_dim = n_users, output_dim=n_latent_factors, input_length=1, \n                              name='User_Embedding') (user_input)\nuser_vector = Flatten(name='User_Vector') (user_embeddings)\n\n\n# Movie Embeddings\nmovie_input = Input(shape=(1,), name='Movie_Input')\nmovie_embeddings = Embedding(input_dim = n_movies, output_dim=n_latent_factors, input_length=1, \n                               name='Movie_Embedding') (movie_input)\nmovie_vector = Flatten(name='Movie_Vector') (movie_embeddings)\n\n\n# Dot Product\nmerged_vectors = dot([user_vector, movie_vector], name='Dot_Product', axes=1)\nmodel = Model([user_input, movie_input], merged_vectors)",
      "execution_count": 67,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "5f12876e26b764b1f8abe1256a290df21af2bc28"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.4\"></a>\n## 4.4) Visualizing the Model Architecture"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2fddfaba193fdf928910665fbc1b406f6c466310"
      },
      "cell_type": "code",
      "source": "SVG(model_to_dot( model,  show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/plain": "<IPython.core.display.SVG object>",
            "image/svg+xml": "<svg height=\"304pt\" viewBox=\"0.00 0.00 690.50 304.00\" width=\"691pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 300)\">\n<title>G</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-300 686.5,-300 686.5,4 -4,4\" stroke=\"none\"/>\n<!-- 139765824705704 -->\n<g class=\"node\" id=\"node1\"><title>139765824705704</title>\n<polygon fill=\"none\" points=\"29,-249.5 29,-295.5 298,-295.5 298,-249.5 29,-249.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"101.5\" y=\"-268.8\">User_Input: InputLayer</text>\n<polyline fill=\"none\" points=\"174,-249.5 174,-295.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"201.5\" y=\"-280.3\">input:</text>\n<polyline fill=\"none\" points=\"174,-272.5 229,-272.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"201.5\" y=\"-257.3\">output:</text>\n<polyline fill=\"none\" points=\"229,-249.5 229,-295.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"263.5\" y=\"-280.3\">(None, 1)</text>\n<polyline fill=\"none\" points=\"229,-272.5 298,-272.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"263.5\" y=\"-257.3\">(None, 1)</text>\n</g>\n<!-- 139765824706544 -->\n<g class=\"node\" id=\"node3\"><title>139765824706544</title>\n<polygon fill=\"none\" points=\"-2.84217e-14,-166.5 -2.84217e-14,-212.5 327,-212.5 327,-166.5 -2.84217e-14,-166.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"91\" y=\"-185.8\">User_Embedding: Embedding</text>\n<polyline fill=\"none\" points=\"182,-166.5 182,-212.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" 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stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"637.5\" y=\"-197.3\">(None, 1)</text>\n<polyline fill=\"none\" points=\"592.5,-189.5 682.5,-189.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"637.5\" y=\"-174.3\">(None, 1, 50)</text>\n</g>\n<!-- 139765824706152&#45;&gt;139765824706040 -->\n<g class=\"edge\" id=\"edge2\"><title>139765824706152-&gt;139765824706040</title>\n<path d=\"M513.5,-249.366C513.5,-241.152 513.5,-231.658 513.5,-222.725\" fill=\"none\" stroke=\"black\"/>\n<polygon fill=\"black\" points=\"517,-222.607 513.5,-212.607 510,-222.607 517,-222.607\" stroke=\"black\"/>\n</g>\n<!-- 139765824705760 -->\n<g class=\"node\" id=\"node5\"><title>139765824705760</title>\n<polygon fill=\"none\" points=\"50.5,-83.5 50.5,-129.5 326.5,-129.5 326.5,-83.5 50.5,-83.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"116\" 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points=\"553.5,-83.5 553.5,-129.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598.5\" y=\"-114.3\">(None, 1, 50)</text>\n<polyline fill=\"none\" points=\"553.5,-106.5 643.5,-106.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"598.5\" y=\"-91.3\">(None, 50)</text>\n</g>\n<!-- 139765824706040&#45;&gt;139765824707272 -->\n<g class=\"edge\" id=\"edge4\"><title>139765824706040-&gt;139765824707272</title>\n<path d=\"M509.948,-166.366C508.629,-158.152 507.106,-148.658 505.672,-139.725\" fill=\"none\" stroke=\"black\"/>\n<polygon fill=\"black\" points=\"509.089,-138.926 504.048,-129.607 502.177,-140.035 509.089,-138.926\" stroke=\"black\"/>\n</g>\n<!-- 139765824706824 -->\n<g class=\"node\" id=\"node7\"><title>139765824706824</title>\n<polygon fill=\"none\" points=\"184.5,-0.5 184.5,-46.5 504.5,-46.5 504.5,-0.5 184.5,-0.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d8a7cc21d7625d6d4b6c4563f3eef637ad88e0d6"
      },
      "cell_type": "code",
      "source": "model.summary()",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": "__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\nUser_Input (InputLayer)         (None, 1)            0                                            \n__________________________________________________________________________________________________\nMovie_Input (InputLayer)        (None, 1)            0                                            \n__________________________________________________________________________________________________\nUser_Embedding (Embedding)      (None, 1, 50)        30500       User_Input[0][0]                 \n__________________________________________________________________________________________________\nMovie_Embedding (Embedding)     (None, 1, 50)        486200      Movie_Input[0][0]                \n__________________________________________________________________________________________________\nUser_Vector (Flatten)           (None, 50)           0           User_Embedding[0][0]             \n__________________________________________________________________________________________________\nMovie_Vector (Flatten)          (None, 50)           0           Movie_Embedding[0][0]            \n__________________________________________________________________________________________________\nDot_Product (Dot)               (None, 1)            0           User_Vector[0][0]                \n                                                                 Movie_Vector[0][0]               \n==================================================================================================\nTotal params: 516,700\nTrainable params: 516,700\nNon-trainable params: 0\n__________________________________________________________________________________________________\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "89d4a53b62018704b524b2653de6650b87bad224"
      },
      "cell_type": "markdown",
      "source": "\n<a id=\"there_you_go_4.5\"></a>\n## 4.5) Compiling the Model"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b499d9b0492c7c59e0696c816c0f35e6c7fd9665"
      },
      "cell_type": "code",
      "source": "optimizer = Adam(lr = 0.0005)\n\nmodel.compile(loss='mean_squared_error', optimizer = optimizer)",
      "execution_count": 72,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "78572c2c1b376327d3e8cdc38df16b5339c94392"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.6\"></a>\n## 4.6) Fitting the Model"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5263657ef6f47f98c63cb8a89ffe38740c7db561"
      },
      "cell_type": "code",
      "source": "batch_size = 128\nepochs = 20",
      "execution_count": 73,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "7b974f90f2c029a094f0fe207c30fbdd07835a19"
      },
      "cell_type": "code",
      "source": "history = model.fit(x=[x_train['userId'], x_train['movieId']], y=y_train, batch_size= batch_size, epochs=epochs, \n                    verbose= 2, validation_data=([x_test['userId'], x_test['movieId']], y_test))",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Train on 80668 samples, validate on 20168 samples\nEpoch 1/20\n - 3s - loss: 13.3415 - val_loss: 13.0194\nEpoch 2/20\n - 2s - loss: 11.1875 - val_loss: 8.1774\nEpoch 3/20\n - 2s - loss: 6.3392 - val_loss: 5.2511\nEpoch 4/20\n - 2s - loss: 4.7887 - val_loss: 4.5418\nEpoch 5/20\n - 2s - loss: 4.2839 - val_loss: 4.2237\nEpoch 6/20\n - 2s - loss: 4.0371 - val_loss: 4.0537\nEpoch 7/20\n - 2s - loss: 3.8953 - val_loss: 3.9520\nEpoch 8/20\n - 2s - loss: 3.8062 - val_loss: 3.8897\nEpoch 9/20\n - 2s - loss: 3.7464 - val_loss: 3.8494\nEpoch 10/20\n - 2s - loss: 3.7033 - val_loss: 3.8216\nEpoch 11/20\n - 2s - loss: 3.6704 - val_loss: 3.8029\nEpoch 12/20\n - 2s - loss: 3.6445 - val_loss: 3.7901\nEpoch 13/20\n - 2s - loss: 3.6224 - val_loss: 3.7826\nEpoch 14/20\n - 2s - loss: 3.6030 - val_loss: 3.7765\nEpoch 15/20\n - 2s - loss: 3.5860 - val_loss: 3.7715\nEpoch 16/20\n - 2s - loss: 3.5699 - val_loss: 3.7698\nEpoch 17/20\n - 2s - loss: 3.5548 - val_loss: 3.7678\nEpoch 18/20\n - 2s - loss: 3.5405 - val_loss: 3.7672\nEpoch 19/20\n - 2s - loss: 3.5263 - val_loss: 3.7665\nEpoch 20/20\n - 2s - loss: 3.5120 - val_loss: 3.7673\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "f16afd148fdf381bd40f21fc8580b58f28ad1f22"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.7\"></a>\n## 4.7) Plotting Validation Curves"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2bc86f3cf8e6e462090564ec4ad776273ba6f4c4"
      },
      "cell_type": "code",
      "source": "# Get training and test loss histories\ntraining_loss = history.history['loss']\ntest_loss = history.history['val_loss']\n\n# Create count of the number of epochs\nepoch_count = range(1, len(training_loss) + 1)\n\n# Visualize loss history\nplt.figure(figsize = (8,4))\nplt.plot(epoch_count, training_loss, 'r--')\nplt.plot(epoch_count, test_loss, 'b-')\nplt.legend(['Training Loss', 'Test Loss'])\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.show()",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x600 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "4e7c5b699c6d3971a93c74abfe8525a6bde4c96b"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.\"></a>\n## 4.8) Evaluating RMSE"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4fa0deab0c21c0ddd328f7fcb81d31340b136d8c"
      },
      "cell_type": "code",
      "source": "score = model.evaluate([x_test['userId'], x_test['movieId']], y_test)\nprint()\nprint('RMSE: {:.4f}'.format(np.sqrt(score)))",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "text": "20168/20168 [==============================] - 1s 30us/step\n\nRMSE: 1.9410\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2e97eaba3c2b57e1398dbdca5b4374a2b2affb3e"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "a611ec33e331eec0cd2552e8779b56144416c3fe"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.9\"></a>\n## 4.9) Architecture 2\nIn this architecture we will be concatenating the latent factors and use Dense layers to calculate the predicted ratings. \n\nHave a look at the image below ->\n![Imgur](https://i.imgur.com/GIe3gTZ.png)"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "d1b5338b38493f9eb74972306794f4dc921315ef"
      },
      "cell_type": "code",
      "source": "# Model Architecture\n\n\n# User Embeddings\nuser_input = Input(shape=(1,), name='User_Input')\nuser_embeddings = Embedding(input_dim = n_users, output_dim=n_latent_factors, input_length=1, \n                              name='User_Embedding') (user_input)\nuser_vector = Flatten(name='User_Vector') (user_embeddings)\n\n\n\n# Movie Embeddings\nmovie_input = Input(shape=(1,), name='Movie_Input')\nmovie_embeddings = Embedding(input_dim = n_movies, output_dim=n_latent_factors, input_length=1, \n                               name='Movie_Embedding') (movie_input)\nmovie_vector = Flatten(name='Movie_Vector') (movie_embeddings)\n\n\n\n\n# Concatenate Product\nmerged_vectors = concatenate([user_vector, movie_vector], name='Concantenate')\ndense_layer_1 = Dense(100, activation='relu')(merged_vectors) \n# dense_layer_1 = Dropout(0.25) (dense_layer_1)\n# batchnorm_layer_1 = BatchNormalization()(dense_layer_1)\n# dense_layer_2 = Dense(64, activation='relu')(merged_vectors)\n\n\nresult = Dense(1)(dense_layer_1)\nmodel = Model([user_input, movie_input], result)",
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "20f8edc7eca8003c9c7c6c866a7c32f2fd66ee0e"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.10\"></a>\n## 4.10) Visualizing the Model Architecture"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "ac7b1f2fc3cc7e02e1de677ae63d3845e0750ce9"
      },
      "cell_type": "code",
      "source": "SVG(model_to_dot( model,  show_shapes=True, show_layer_names=True).create(prog='dot', format='svg'))",
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 79,
          "data": {
            "text/plain": "<IPython.core.display.SVG object>",
            "image/svg+xml": "<svg height=\"470pt\" viewBox=\"0.00 0.00 690.50 470.00\" width=\"691pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 466)\">\n<title>G</title>\n<polygon fill=\"white\" points=\"-4,4 -4,-466 686.5,-466 686.5,4 -4,4\" stroke=\"none\"/>\n<!-- 139765823475216 -->\n<g class=\"node\" id=\"node1\"><title>139765823475216</title>\n<polygon fill=\"none\" points=\"29,-415.5 29,-461.5 298,-461.5 298,-415.5 29,-415.5\" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"101.5\" y=\"-434.8\">User_Input: InputLayer</text>\n<polyline fill=\"none\" points=\"174,-415.5 174,-461.5 \" stroke=\"black\"/>\n<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"201.5\" y=\"-446.3\">input:</text>\n<polyline fill=\"none\" points=\"174,-438.5 229,-438.5 \" stroke=\"black\"/>\n<text 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        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a5719bb8ba6c2a03f9a4cd7cdd293ebf94a6ff87"
      },
      "cell_type": "code",
      "source": "model.summary()",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "text": "__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\nUser_Input (InputLayer)         (None, 1)            0                                            \n__________________________________________________________________________________________________\nMovie_Input (InputLayer)        (None, 1)            0                                            \n__________________________________________________________________________________________________\nUser_Embedding (Embedding)      (None, 1, 50)        30500       User_Input[0][0]                 \n__________________________________________________________________________________________________\nMovie_Embedding (Embedding)     (None, 1, 50)        486200      Movie_Input[0][0]                \n__________________________________________________________________________________________________\nUser_Vector (Flatten)           (None, 50)           0           User_Embedding[0][0]             \n__________________________________________________________________________________________________\nMovie_Vector (Flatten)          (None, 50)           0           Movie_Embedding[0][0]            \n__________________________________________________________________________________________________\nConcantenate (Concatenate)      (None, 100)          0           User_Vector[0][0]                \n                                                                 Movie_Vector[0][0]               \n__________________________________________________________________________________________________\ndense_1 (Dense)                 (None, 100)          10100       Concantenate[0][0]               \n__________________________________________________________________________________________________\ndense_2 (Dense)                 (None, 1)            101         dense_1[0][0]                    \n==================================================================================================\nTotal params: 526,901\nTrainable params: 526,901\nNon-trainable params: 0\n__________________________________________________________________________________________________\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "331d31a25df4513629e5921a92f147538b2db770"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.11\"></a>\n## 4.11) Compiling the Model"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "cf43f5b99b030c0281b8331789c9d9a2dc1a3798"
      },
      "cell_type": "code",
      "source": "optimizer = Adam(lr=0.0002)",
      "execution_count": 81,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "2e3f9ef79fbef9798e42aeaba4e0c7720ab189d5"
      },
      "cell_type": "code",
      "source": "model.compile(loss='mean_squared_error', optimizer=optimizer)",
      "execution_count": 82,
      "outputs": []
    },
    {
      "metadata": {
        "_uuid": "0096da22916b4714d98df9c254c2e14de67cd46d"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.12\"></a>\n## 4.12) Fitting the Model"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "45445039d83b70b00afaf3d3b47037ad14f6e947"
      },
      "cell_type": "code",
      "source": "batch_size = 128\nepochs = 20",
      "execution_count": 83,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "4fa96023bcb4b56835003bc29eb0362d34903048"
      },
      "cell_type": "code",
      "source": "history = model.fit(x=[x_train['userId'], x_train['movieId']], y=y_train, batch_size= batch_size, epochs=epochs, \n                    verbose= 2, validation_data=([x_test['userId'], x_test['movieId']], y_test))",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Train on 80668 samples, validate on 20168 samples\nEpoch 1/20\n - 2s - loss: 4.8239 - val_loss: 1.0488\nEpoch 2/20\n - 2s - loss: 0.9364 - val_loss: 0.8895\nEpoch 3/20\n - 2s - loss: 0.8471 - val_loss: 0.8607\nEpoch 4/20\n - 2s - loss: 0.8145 - val_loss: 0.8461\nEpoch 5/20\n - 2s - loss: 0.7923 - val_loss: 0.8318\nEpoch 6/20\n - 2s - loss: 0.7727 - val_loss: 0.8228\nEpoch 7/20\n - 2s - loss: 0.7554 - val_loss: 0.8125\nEpoch 8/20\n - 2s - loss: 0.7404 - val_loss: 0.8049\nEpoch 9/20\n - 2s - loss: 0.7281 - val_loss: 0.7953\nEpoch 10/20\n - 2s - loss: 0.7172 - val_loss: 0.7880\nEpoch 11/20\n - 2s - loss: 0.7073 - val_loss: 0.7831\nEpoch 12/20\n - 2s - loss: 0.6986 - val_loss: 0.7790\nEpoch 13/20\n - 2s - loss: 0.6902 - val_loss: 0.7739\nEpoch 14/20\n - 2s - loss: 0.6833 - val_loss: 0.7767\nEpoch 15/20\n - 2s - loss: 0.6774 - val_loss: 0.7672\nEpoch 16/20\n - 2s - loss: 0.6715 - val_loss: 0.7707\nEpoch 17/20\n - 2s - loss: 0.6660 - val_loss: 0.7666\nEpoch 18/20\n - 2s - loss: 0.6608 - val_loss: 0.7676\nEpoch 19/20\n - 2s - loss: 0.6558 - val_loss: 0.7682\nEpoch 20/20\n - 2s - loss: 0.6511 - val_loss: 0.7664\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "b31a012b86d17c819d16ebb966aedcfe39050853"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.13\"></a>\n## 4.13) Plotting Validation Curves"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "8435fd0af309608e37ffb6bcb775757ac1d4c0e4",
        "scrolled": true
      },
      "cell_type": "code",
      "source": "# Get training and test loss histories\ntraining_loss = history.history['loss']\ntest_loss = history.history['val_loss']\n\n# Create count of the number of epochs\nepoch_count = range(1, len(training_loss) + 1)\n\n# Visualize loss history\nplt.figure(figsize = (8,4))\nplt.plot(epoch_count, training_loss, 'r--')\nplt.plot(epoch_count, test_loss, 'b-')\nplt.legend(['Training Loss', 'Test Loss'])\nplt.xlabel('Epoch')\nplt.ylabel('Loss')\nplt.show()",
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1200x600 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "_uuid": "8ee3d4045052b3f991b216954d58376771f06feb"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_4.14\"></a>\n## 4.14) Evaluating RMSE"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "5772d56a06df2a89602e26c744d9c7e5c3658a0a"
      },
      "cell_type": "code",
      "source": "score = model.evaluate([x_test['userId'], x_test['movieId']], y_test)\n\nprint()\nprint('RMSE: {:.4f}'.format(np.sqrt(score)))",
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": "20168/20168 [==============================] - 1s 36us/step\n\nRMSE: 0.8754\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "a15316487e0e9b6f59c8b720a433d5b2b95cf7cb"
      },
      "cell_type": "markdown",
      "source": "So, as you could see the latter architecture did a pretty good job in predicting the user ratings. You can try tuning the number of latent factors, also try changing the architecture, adding more layers (make sure it doesn't overfit) and introducing Dropout and BatchNorm layers to acheive even lower RMSE."
    },
    {
      "metadata": {
        "_uuid": "6b5fdda29c5a20b8ffdb595451a2b7f4c2b87735"
      },
      "cell_type": "markdown",
      "source": "<a id=\"there_you_go_5\"></a>\n# 5) References\n1.  Linear Algebra - [Good for quick revision](https://www.kaggle.com/mjbahmani/linear-algebra-for-data-scientists)\n2. Stanford lectures - [Starts at Lecure 41 and you can proceed as much you need, covers CF, SVD and much more](https://www.youtube.com/watch?v=1JRrCEgiyHM&index=41&list=PLLssT5z_DsK9JDLcT8T62VtzwyW9LNepV)\n3. Blogs - [Various Implementations of Collaborative Filtering](https://towardsdatascience.com/various-implementations-of-collaborative-filtering-100385c6dfe0)\n4. Deep Learning Blog - [Implementing recommendation systems in keras](https://nipunbatra.github.io/blog/2017/recommend-keras.html)\n5. Matrix Factorization - [Understanding how Matrix Factorization works](https://lazyprogrammer.me/tutorial-on-collaborative-filtering-and-matrix-factorization-in-python/)\n6. Surprise Package - [Library to implement Model-Based CF Algorithms](https://surprise.readthedocs.io/en/stable/)"
    },
    {
      "metadata": {
        "_uuid": "e328d29d2b8abe726e318b1d25d085eb1e126608"
      },
      "cell_type": "markdown",
      "source": "*Thank you...*\n\n*Do star/upvote if you like it ;)*"
    },
    {
      "metadata": {
        "trusted": true,
        "_uuid": "097a187c90edc2aded7b60387308862ae9bffedb"
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
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      "language": "python",
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