notebook/explore_data_BQ_python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "DGPlYumZnO1t"
   },
   "source": [
    "# Exploratory Data Analysis Using Python and BigQuery\n",
    "\n",
    "\n",
    "\n",
    "## Objectives\n",
    "\n",
    "1. Analyze a Pandas Dataframe\n",
    "2. Create Seaborn plots for Exploratory Data Analysis in Python \n",
    "3. Write a SQL query to pick up specific fields from a BigQuery dataset\n",
    "4. Exploratory Analysis in BigQuery\n",
    "\n",
    "\n",
    "## Introduction \n",
    "This notebook is an introduction to linear regression using Python and Scikit-Learn.  This notebook serves as a foundation for more complex algorithms and machine learning models. We will train a linear regression model to predict housing price."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AsHg6SD2nO1v"
   },
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "!pip install --user google-cloud-bigquery==1.25.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
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      "  Attempting uninstall: google-resumable-media\n",
      "    Found existing installation: google-resumable-media 0.5.1\n",
      "    Uninstalling google-resumable-media-0.5.1:\n",
      "      Successfully uninstalled google-resumable-media-0.5.1\n",
      "  Rolling back uninstall of google-resumable-media\n",
      "  Moving to /home/jupyter/.local/lib/python3.7/site-packages/google/resumable_media/\n",
      "   from /home/jupyter/.local/lib/python3.7/site-packages/google/~esumable_media\n",
      "  Moving to /home/jupyter/.local/lib/python3.7/site-packages/google_resumable_media-0.5.1-py3.8-nspkg.pth\n",
      "   from /var/tmp/pip-uninstall-9qef078e/google_resumable_media-0.5.1-py3.8-nspkg.pth\n",
      "  Moving to /home/jupyter/.local/lib/python3.7/site-packages/google_resumable_media-0.5.1.dist-info/\n",
      "   from /home/jupyter/.local/lib/python3.7/site-packages/~oogle_resumable_media-0.5.1.dist-info\n",
      "\u001b[31mERROR: Could not install packages due to an OSError: [Errno 13] Permission denied: '/opt/conda/lib/python3.7/site-packages/google/_async_resumable_media/__init__.py'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\u001b[0m\u001b[31m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -U google-cloud-bigquery google-api-core==1.33.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gEXV-RxPnO1w"
   },
   "outputs": [],
   "source": [
    "import os \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# delete me from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # Seaborn is a Python data visualization library based on matplotlib. \n",
    "%matplotlib inline   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dr2TkzKRnO1z"
   },
   "source": [
    "###  Load the Dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we create a directory called usahousing.  This directory will hold the dataset that we copy from Google Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(\"../data/explore\"):\n",
    "    os.makedirs(\"../data/explore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we copy the Usahousing dataset from Google Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Copying gs://cloud-training/mlongcp/v3.0_MLonGC/toy_data/housing_pre-proc_toy.csv...\n",
      "/ [1 files][138.8 KiB/138.8 KiB]                                                \n",
      "Operation completed over 1 objects/138.8 KiB.                                    \n"
     ]
    }
   ],
   "source": [
    "!gsutil cp gs://cloud-training/mlongcp/v3.0_MLonGC/toy_data/housing_pre-proc_toy.csv ../data/explore  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we use the \"ls\" command to list files in the directory.  This ensures that the dataset was copied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 140\n",
      "-rw-r--r-- 1 jupyter jupyter 142150 Apr  9 07:11 housing_pre-proc_toy.csv\n"
     ]
    }
   ],
   "source": [
    "!ls -l ../data/explore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we read the dataset into a Pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CzrXJI8VnO10"
   },
   "outputs": [],
   "source": [
    "df_USAhousing = pd.read_csv('../data/explore/housing_pre-proc_toy.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Inspect the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 272
    },
    "colab_type": "code",
    "id": "Y6VJQ1tdnO12",
    "outputId": "7a1d4eed-3e83-44a8-f495-a9b74444d3ec"
   },
   "outputs": [
    {
     "data": {
      "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>ocean_proximity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "      <td>NEAR BAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880             129   \n",
       "1    -122.22     37.86                  21         7099            1106   \n",
       "2    -122.24     37.85                  52         1467             190   \n",
       "3    -122.25     37.85                  52         1274             235   \n",
       "4    -122.25     37.85                  52         1627             280   \n",
       "\n",
       "   population  households  median_income  median_house_value ocean_proximity  \n",
       "0         322         126         8.3252              452600        NEAR BAY  \n",
       "1        2401        1138         8.3014              358500        NEAR BAY  \n",
       "2         496         177         7.2574              352100        NEAR BAY  \n",
       "3         558         219         5.6431              341300        NEAR BAY  \n",
       "4         565         259         3.8462              342200        NEAR BAY  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the first five row.\n",
    "\n",
    "df_USAhousing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check for any null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             0\n",
       "latitude              0\n",
       "housing_median_age    0\n",
       "total_rooms           0\n",
       "total_bedrooms        0\n",
       "population            0\n",
       "households            0\n",
       "median_income         0\n",
       "median_house_value    0\n",
       "ocean_proximity       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_USAhousing.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "nRTsvSzqnO17",
    "outputId": "f44ad14e-5fb4-4c70-e71c-9d149bca4869"
   },
   "outputs": [
    {
     "data": {
      "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>-121.501836</td>\n",
       "      <td>1.015963</td>\n",
       "      <td>-124.3000</td>\n",
       "      <td>-122.200000</td>\n",
       "      <td>-122.0300</td>\n",
       "      <td>-120.697500</td>\n",
       "      <td>-118.9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>37.802288</td>\n",
       "      <td>0.803090</td>\n",
       "      <td>36.1300</td>\n",
       "      <td>37.600000</td>\n",
       "      <td>37.8000</td>\n",
       "      <td>37.960000</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>30.088400</td>\n",
       "      <td>13.878416</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>2522.734000</td>\n",
       "      <td>1988.411988</td>\n",
       "      <td>12.0000</td>\n",
       "      <td>1420.750000</td>\n",
       "      <td>2052.0000</td>\n",
       "      <td>3007.250000</td>\n",
       "      <td>28258.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>491.862400</td>\n",
       "      <td>362.499497</td>\n",
       "      <td>4.0000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>402.0000</td>\n",
       "      <td>581.250000</td>\n",
       "      <td>3864.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>1246.225200</td>\n",
       "      <td>925.075463</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>718.000000</td>\n",
       "      <td>1030.5000</td>\n",
       "      <td>1488.250000</td>\n",
       "      <td>12203.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>458.122000</td>\n",
       "      <td>341.744308</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>263.000000</td>\n",
       "      <td>374.5000</td>\n",
       "      <td>538.000000</td>\n",
       "      <td>3701.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>3.694312</td>\n",
       "      <td>1.859422</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.357875</td>\n",
       "      <td>3.2622</td>\n",
       "      <td>4.662975</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>2500.0</td>\n",
       "      <td>170288.731200</td>\n",
       "      <td>97550.278529</td>\n",
       "      <td>22500.0000</td>\n",
       "      <td>92950.000000</td>\n",
       "      <td>150800.0000</td>\n",
       "      <td>219650.000000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count           mean           std         min  \\\n",
       "longitude           2500.0    -121.501836      1.015963   -124.3000   \n",
       "latitude            2500.0      37.802288      0.803090     36.1300   \n",
       "housing_median_age  2500.0      30.088400     13.878416      2.0000   \n",
       "total_rooms         2500.0    2522.734000   1988.411988     12.0000   \n",
       "total_bedrooms      2500.0     491.862400    362.499497      4.0000   \n",
       "population          2500.0    1246.225200    925.075463     18.0000   \n",
       "households          2500.0     458.122000    341.744308      2.0000   \n",
       "median_income       2500.0       3.694312      1.859422      0.4999   \n",
       "median_house_value  2500.0  170288.731200  97550.278529  22500.0000   \n",
       "\n",
       "                             25%          50%            75%          max  \n",
       "longitude            -122.200000    -122.0300    -120.697500    -118.9100  \n",
       "latitude               37.600000      37.8000      37.960000      41.9500  \n",
       "housing_median_age     18.000000      30.0000      41.000000      52.0000  \n",
       "total_rooms          1420.750000    2052.0000    3007.250000   28258.0000  \n",
       "total_bedrooms        282.000000     402.0000     581.250000    3864.0000  \n",
       "population            718.000000    1030.5000    1488.250000   12203.0000  \n",
       "households            263.000000     374.5000     538.000000    3701.0000  \n",
       "median_income           2.357875       3.2622       4.662975      15.0001  \n",
       "median_house_value  92950.000000  150800.0000  219650.000000  500001.0000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stats = df_USAhousing.describe()\n",
    "df_stats = df_stats.transpose()\n",
    "df_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2500 entries, 0 to 2499\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           2500 non-null   float64\n",
      " 1   latitude            2500 non-null   float64\n",
      " 2   housing_median_age  2500 non-null   int64  \n",
      " 3   total_rooms         2500 non-null   int64  \n",
      " 4   total_bedrooms      2500 non-null   int64  \n",
      " 5   population          2500 non-null   int64  \n",
      " 6   households          2500 non-null   int64  \n",
      " 7   median_income       2500 non-null   float64\n",
      " 8   median_house_value  2500 non-null   int64  \n",
      " 9   ocean_proximity     2500 non-null   object \n",
      "dtypes: float64(3), int64(6), object(1)\n",
      "memory usage: 195.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df_USAhousing.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a peek at the first and last five rows of the data for all columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows     :  2500\n",
      "Columns  :  10\n",
      "\n",
      "Features : \n",
      " ['longitude', 'latitude', 'housing_median_age', 'total_rooms', 'total_bedrooms', 'population', 'households', 'median_income', 'median_house_value', 'ocean_proximity']\n",
      "\n",
      "Missing values :   0\n",
      "\n",
      "Unique values :  \n",
      " longitude              265\n",
      "latitude               275\n",
      "housing_median_age      51\n",
      "total_rooms           1889\n",
      "total_bedrooms         922\n",
      "population            1520\n",
      "households             890\n",
      "median_income         2186\n",
      "median_house_value    1553\n",
      "ocean_proximity          4\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (\"Rows     : \" ,df_USAhousing.shape[0])\n",
    "print (\"Columns  : \" ,df_USAhousing.shape[1])\n",
    "print (\"\\nFeatures : \\n\" ,df_USAhousing.columns.tolist())\n",
    "print (\"\\nMissing values :  \", df_USAhousing.isnull().sum().values.sum())\n",
    "print (\"\\nUnique values :  \\n\",df_USAhousing\n",
    "       .nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QWVdsrmgnO1_"
   },
   "source": [
    "## Explore the Data\n",
    "\n",
    "Let's create some simple plots to check out the data!  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_USAhousing.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a displot showing \"median_house_value\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 296
    },
    "colab_type": "code",
    "id": "SOsTLClWnO2B",
    "outputId": "b8a78674-5ddb-4706-90b4-37d7d83e8092"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_USAhousing, x=\"median_house_value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'median_house_value')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "df_USAhousing['median_house_value'].hist(bins=30)\n",
    "plt.xlabel('median_house_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_USAhousing['median_income']\n",
    "y = df_USAhousing['median_house_value']\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a jointplot showing \"median_income\"  versus \"median_house_value\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data=df_USAhousing, x=\"median_income\", y=\"median_house_value\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='ocean_proximity', ylabel='count'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'ocean_proximity', data=df_USAhousing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes numeric only?\n",
    "#plt.figure(figsize=(20,20))\n",
    "g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
    "g.map(plt.hist, \"households\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# takes numeric only?\n",
    "#plt.figure(figsize=(20,20))\n",
    "g = sns.FacetGrid(df_USAhousing, col=\"ocean_proximity\")\n",
    "g.map(plt.hist, \"median_income\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see below that this is the state of California!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df_USAhousing['latitude']\n",
    "y = df_USAhousing['longitude']\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore and create ML datasets\n",
    "\n",
    "In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders so that they are not surprised, and so that they can protest if the charge is much higher than expected.\n",
    "\n",
    "## Learning objectives\n",
    "* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n",
    "* Visualize your dataset using the Seaborn library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from google.cloud import bigquery\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Extract sample data from BigQuery </h3>\n",
    "\n",
    "The dataset that we will use is <a href=\"https://console.cloud.google.com/bigquery?project=nyc-tlc&p=nyc-tlc&d=yellow&t=trips&page=table\">a BigQuery public dataset</a>. Click on the link, and look at the column names. Switch to the Details tab to verify that the number of records is one billion, and then switch to the Preview tab to look at a few rows.\n",
    "\n",
    "Let's write a SQL query to pick up interesting fields from the dataset. It's a good idea to get the timestamp in a predictable format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing query with job ID: f9c74c84-d32c-4cf7-9aeb-78fe8eb4e2ea\n",
      "Query executing: 0.35s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "ERROR:\n",
      " _blocking_poll() got an unexpected keyword argument 'retry'\n"
     ]
    }
   ],
   "source": [
    "%%bigquery\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, dropoff_longitude,\n",
    "    dropoff_latitude, passenger_count, trip_distance, tolls_amount, \n",
    "    fare_amount, total_amount \n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "LIMIT 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's increase the number of records so that we can do some neat graphs.  There is no guarantee about the order in which records are returned, and so no guarantee about which records get returned if we simply increase the LIMIT. To properly sample the dataset, let's use the HASH of the pickup time and return 1 in 100,000 records -- because there are 1 billion records in the data, we should get back approximately 10,000 records if we do this.\n",
    "\n",
    "We will also store the BigQuery result in a Pandas dataframe named \"trips\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing query with job ID: 49172bb1-513d-431f-b080-6c1177bfa241\n",
      "Query executing: 0.46s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not save output to variable 'trips'.\n",
      "\n",
      "ERROR:\n",
      " _blocking_poll() got an unexpected keyword argument 'retry'\n"
     ]
    }
   ],
   "source": [
    "%%bigquery trips\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, \n",
    "    dropoff_longitude, dropoff_latitude,\n",
    "    passenger_count,\n",
    "    trip_distance,\n",
    "    tolls_amount,\n",
    "    fare_amount,\n",
    "    total_amount\n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "WHERE\n",
    "    ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/3863233666.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "print(len(trips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/846401051.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# We can slice Pandas dataframes as if they were arrays\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "# We can slice Pandas dataframes as if they were arrays\n",
    "trips[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> Exploring data </h3>\n",
    "\n",
    "Let's explore this dataset and clean it up as necessary. We'll use the Python Seaborn package to visualize graphs and Pandas to do the slicing and filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/2797655742.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# TODO 4: Visualize your dataset using the Seaborn library.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Plot the distance of the trip as X and the fare amount as Y.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfit_reg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mci\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "# TODO 4: Visualize your dataset using the Seaborn library.\n",
    "# Plot the distance of the trip as X and the fare amount as Y.\n",
    "ax = sns.regplot(x=\"\", y=\"\", fit_reg=False, ci=None, truncate=True, data=trips)\n",
    "ax.figure.set_size_inches(10, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm ... do you see something wrong with the data that needs addressing?\n",
    "\n",
    "It appears that we have a lot of invalid data that is being coded as zero distance and some fare amounts that are definitely illegitimate. Let's remove them from our analysis. We can do this by modifying the BigQuery query to keep only trips longer than zero miles and fare amounts that are at least the minimum cab fare ($2.50).\n",
    "\n",
    "Note the extra WHERE clauses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing query with job ID: 0851829d-6b90-47da-8a3f-69d5fd7b225a\n",
      "Query executing: 0.23s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Could not save output to variable 'trips'.\n",
      "\n",
      "ERROR:\n",
      " _blocking_poll() got an unexpected keyword argument 'retry'\n"
     ]
    }
   ],
   "source": [
    "%%bigquery trips\n",
    "SELECT\n",
    "    FORMAT_TIMESTAMP(\n",
    "        \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n",
    "    pickup_longitude, pickup_latitude, \n",
    "    dropoff_longitude, dropoff_latitude,\n",
    "    passenger_count,\n",
    "    trip_distance,\n",
    "    tolls_amount,\n",
    "    fare_amount,\n",
    "    total_amount\n",
    "FROM\n",
    "    `nyc-tlc.yellow.trips`\n",
    "WHERE\n",
    "    ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1\n",
    "    # TODO 4a: Filter the data to only include non-zero distance trips and fares above $2.50\n",
    "    AND "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/3863233666.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrips\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "print(len(trips))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/4143888116.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m ax = sns.regplot(\n\u001b[1;32m      2\u001b[0m     \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"trip_distance\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"fare_amount\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     fit_reg=False, ci=None, truncate=True, data=trips)\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "ax = sns.regplot(\n",
    "    x=\"trip_distance\", y=\"fare_amount\",\n",
    "    fit_reg=False, ci=None, truncate=True, data=trips)\n",
    "ax.figure.set_size_inches(10, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's up with the streaks around 45 dollars and 50 dollars?  Those are fixed-amount rides from JFK and La Guardia airports into anywhere in Manhattan, i.e. to be expected. Let's list the data to make sure the values look reasonable.\n",
    "\n",
    "Let's also examine whether the toll amount is captured in the total amount."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/4285639296.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtollrides\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tolls_amount\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mtollrides\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtollrides\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pickup_datetime\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"2012-02-27 09:19:10 UTC\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "tollrides = trips[trips[\"tolls_amount\"] > 0]\n",
    "tollrides[tollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/840128049.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnotollrides\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrips\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"tolls_amount\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mnotollrides\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnotollrides\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"pickup_datetime\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"2012-02-27 09:19:10 UTC\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "notollrides = trips[trips[\"tolls_amount\"] == 0]\n",
    "notollrides[notollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at a few samples above, it should be clear that the total amount reflects fare amount, toll and tip somewhat arbitrarily -- this is because when customers pay cash, the tip is not known.  So, we'll use the sum of fare_amount + tolls_amount as what needs to be predicted.  Tips are discretionary and do not have to be included in our fare estimation tool.\n",
    "\n",
    "Let's also look at the distribution of values within the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'trips' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/tmp/ipykernel_7670/4160825302.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrips\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'trips' is not defined"
     ]
    }
   ],
   "source": [
    "trips.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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