index.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Data Cleaning - Cumulative Lab\n",
    "\n",
    "## Introduction\n",
    "In this lab, we'll make use of everything we've learned about pandas, data cleaning, and exploratory data analysis. In order to complete this lab, you'll have to import, clean, combine, reshape, and visualize data to answer questions provided, as well as your own questions!\n",
    "\n",
    "## Objectives\n",
    "You will be able to:\n",
    "- Practice opening and inspecting the contents of CSVs using pandas dataframes\n",
    "- Practice identifying and handling missing values\n",
    "- Practice identifying and handling invalid values\n",
    "- Practice cleaning text data by removing whitespace and fixing typos\n",
    "- Practice joining multiple dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Your Task: Clean the Superheroes Dataset with Pandas\n",
    "\n",
    "![LEGO superheroes](images/lego_superheroes.jpg)\n",
    "\n",
    "Photo by <a href=\"https://unsplash.com/@yuliamatvienko?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Yulia Matvienko</a> on <a href=\"/s/photos/superhero?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText\">Unsplash</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Understanding\n",
    "In this lab, we'll work with a version of the comprehensive Superheroes Dataset, which can be found on [Kaggle](https://www.kaggle.com/claudiodavi/superhero-set/data) and was originally scraped from [SuperHeroDb](https://www.superherodb.com/). We have modified the structure and contents of the dataset somewhat for the purposes of this lab.  Note that this data was collected in June 2017, so it may not reflect the most up-to-date superhero lore.\n",
    "\n",
    "The data is contained in two separate CSV files:\n",
    "\n",
    "1. `heroes_information.csv`: each record represents a superhero, with attributes of that superhero (e.g. eye color). Height is measured in centimeters, and weight is measured in pounds.\n",
    "2. `super_hero_powers.csv`: each record represents a superpower, then has True/False values representing whether each superhero has that power\n",
    "\n",
    "### Business Understanding\n",
    "\n",
    "The business questions you have been provided are:\n",
    "\n",
    "1. What is the distribution of superheroes by publisher?\n",
    "2. What is the relationship between height and number of superpowers? And does this differ based on gender?\n",
    "3. What are the 5 most common superpowers in Marvel Comics vs. DC Comics?\n",
    "\n",
    "This lab also simulates something you are likely to encounter at some point or another in your career in data science: someone has given you access to a dataset, as well as a few questions, and has told you to \"find something interesting\".\n",
    "\n",
    "So, in addition to completing the basic data cleaning tasks and the aggregation and reshaping tasks needed to answer the provided questions, you will also need to formulate a question of your own and perform any additional cleaning/aggregation/reshaping that is needed to answer it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "\n",
    "#### 1. Load the Data with Pandas\n",
    "\n",
    "Create a dataframes `heroes_df` and `powers_df` that represent the two CSV files. Use pandas methods to inspect the shape and other attributes of these dataframes.\n",
    "\n",
    "#### 2. Perform Data Cleaning Required to Answer First Question\n",
    "\n",
    "The first question is: *What is the distribution of superheroes by publisher?*\n",
    "\n",
    "In order to answer this question, you will need to:\n",
    "\n",
    "* Identify and handle missing values\n",
    "* Identify and handle text data requiring cleaning\n",
    "\n",
    "#### 3. Perform Data Aggregation and Cleaning Required to Answer Second Question\n",
    "\n",
    "The second question is: *What is the relationship between height and number of superpowers? And does this differ based on gender?*\n",
    "\n",
    "In order to answer this question, you will need to:\n",
    "\n",
    "* Join the dataframes together\n",
    "* Identify and handle invalid values\n",
    "\n",
    "#### 4. Perform Data Aggregation Required to Answer Third Question\n",
    "\n",
    "The third question is: *What are the 5 most common superpowers in Marvel Comics vs. DC Comics?*\n",
    "\n",
    "This should not require any additional data cleaning or joining of tables, but it will require some additional aggregation.\n",
    "\n",
    "#### 5. Formulate and Answer Your Own Question\n",
    "\n",
    "This part is fairly open-ended. Think of a question that can be answered with the available data, and perform any cleaning or aggregation required to answer that question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Load the Data with Pandas\n",
    "\n",
    "In the cell below, we:\n",
    "\n",
    "* Import and alias `pandas` as `pd`\n",
    "* Import and alias `numpy` as `np`\n",
    "* Import and alias `seaborn` as `sns`\n",
    "* Import and alias `matplotlib.pyplot` as `plt`\n",
    "* Set Matplotlib visualizations to display inline in the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Superheroes\n",
    "\n",
    "In the cell below, load `heroes_information.csv` as `heroes_df`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0         name Gender Eye color               Race Hair color  \\\n",
       "0           0       A-Bomb   Male    yellow              Human    No Hair   \n",
       "1           1   Abe Sapien   Male      blue      Icthyo Sapien    No Hair   \n",
       "2           2     Abin Sur   Male      blue            Ungaran    No Hair   \n",
       "3           3  Abomination   Male     green  Human / Radiation    No Hair   \n",
       "4           4      Abraxas   Male      blue      Cosmic Entity      Black   \n",
       "\n",
       "   Height          Publisher Skin color Alignment  Weight  \n",
       "0   203.0      Marvel Comics          -      good   441.0  \n",
       "1   191.0  Dark Horse Comics       blue      good    65.0  \n",
       "2   185.0          DC Comics        red      good    90.0  \n",
       "3   203.0      Marvel Comics          -       bad   441.0  \n",
       "4   -99.0      Marvel Comics          -       bad   -99.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "heroes_df = pd.read_csv(\"heroes_information.csv\")\n",
    "heroes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like that CSV came with an index column, resulting in an extra column called `Unnamed: 0`. We don't need that column, so write code to get rid of it below.\n",
    "\n",
    "There are two ways to do this:\n",
    "\n",
    "1. Re-load with `read_csv`, and specify the parameter `index_col=0`\n",
    "2. Drop the column `Unnamed: 0` with `axis=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          name Gender Eye color               Race Hair color  Height  \\\n",
       "0       A-Bomb   Male    yellow              Human    No Hair   203.0   \n",
       "1   Abe Sapien   Male      blue      Icthyo Sapien    No Hair   191.0   \n",
       "2     Abin Sur   Male      blue            Ungaran    No Hair   185.0   \n",
       "3  Abomination   Male     green  Human / Radiation    No Hair   203.0   \n",
       "4      Abraxas   Male      blue      Cosmic Entity      Black   -99.0   \n",
       "\n",
       "           Publisher Skin color Alignment  Weight  \n",
       "0      Marvel Comics          -      good   441.0  \n",
       "1  Dark Horse Comics       blue      good    65.0  \n",
       "2          DC Comics        red      good    90.0  \n",
       "3      Marvel Comics          -       bad   441.0  \n",
       "4      Marvel Comics          -       bad   -99.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "heroes_df = pd.read_csv(\"heroes_information.csv\", index_col=0)\n",
    "heroes_df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code checks that the dataframe was loaded correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# There should be 734 rows\n",
    "assert heroes_df.shape[0] == 734\n",
    "\n",
    "# There should be 10 columns. If this fails, make sure you got rid of\n",
    "# the extra index column\n",
    "assert heroes_df.shape[1] == 10\n",
    "\n",
    "# These should be the columns\n",
    "assert list(heroes_df.columns) == [\n",
    "    \"name\",\n",
    "    \"Gender\",\n",
    "    \"Eye color\",\n",
    "    \"Race\",\n",
    "    \"Hair color\",\n",
    "    \"Height\",\n",
    "    \"Publisher\",\n",
    "    \"Skin color\",\n",
    "    \"Alignment\",\n",
    "    \"Weight\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you want to get familiar with the data.  This step includes:\n",
    "\n",
    "* Understanding the dimensionality of your dataset\n",
    "* Investigating what type of data it contains, and the data types used to store it\n",
    "* Discovering how missing values are encoded, and how many there are\n",
    "* Getting a feel for what information it does and doesn't contain\n",
    "\n",
    "In the cell below, inspect the overall shape of the dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(734, 10)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "heroes_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the info printout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 734 entries, 0 to 733\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   name        734 non-null    object \n",
      " 1   Gender      734 non-null    object \n",
      " 2   Eye color   734 non-null    object \n",
      " 3   Race        734 non-null    object \n",
      " 4   Hair color  734 non-null    object \n",
      " 5   Height      734 non-null    float64\n",
      " 6   Publisher   719 non-null    object \n",
      " 7   Skin color  734 non-null    object \n",
      " 8   Alignment   734 non-null    object \n",
      " 9   Weight      732 non-null    float64\n",
      "dtypes: float64(2), object(8)\n",
      "memory usage: 63.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "heroes_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, interpret that information. Do the data types line up with what we expect? Are there any missing values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThe data types line up with what we expect. The columns with text are categorized as type object, while those with decimal numbers (such as height and weight) are categorized as type float64.\\n\\nThere are evident missing or NaN values in the Publisher and Weight columns, as the total rows are 734 and each of these columns has fewer than 734 non-null values.\\n\\nMoreover, this data source includes placeholders for missing values, indicated by \"-\" characters in the Skin Color column. To utilize this information, we must decide how to handle these \"-\" values.\\n\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate text\n",
    "\"\"\"\n",
    "The data types line up with what we expect. The columns with text are categorized as type object, while those with decimal numbers (such as height and weight) are categorized as type float64.\n",
    "\n",
    "There are evident missing or NaN values in the Publisher and Weight columns, as the total rows are 734 and each of these columns has fewer than 734 non-null values.\n",
    "\n",
    "Moreover, this data source includes placeholders for missing values, indicated by \"-\" characters in the Skin Color column. To utilize this information, we must decide how to handle these \"-\" values.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Superpowers\n",
    "\n",
    "Now, repeat the same process with `super_hero_powers.csv`. Name the dataframe `powers_df`. This time, make sure you use `index_col=0` when opening the CSV because the index contains important information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3-D Man</th>\n",
       "      <th>A-Bomb</th>\n",
       "      <th>Abe Sapien</th>\n",
       "      <th>Abin Sur</th>\n",
       "      <th>Abomination</th>\n",
       "      <th>Abraxas</th>\n",
       "      <th>Absorbing Man</th>\n",
       "      <th>Adam Monroe</th>\n",
       "      <th>Adam Strange</th>\n",
       "      <th>Agent Bob</th>\n",
       "      <th>...</th>\n",
       "      <th>Wonder Man</th>\n",
       "      <th>Wonder Woman</th>\n",
       "      <th>X-23</th>\n",
       "      <th>X-Man</th>\n",
       "      <th>Yellowjacket</th>\n",
       "      <th>Yellowjacket II</th>\n",
       "      <th>Ymir</th>\n",
       "      <th>Yoda</th>\n",
       "      <th>Zatanna</th>\n",
       "      <th>Zoom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agility</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accelerated Healing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lantern Power Ring</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimensional Awareness</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cold Resistance</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 667 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       3-D Man  A-Bomb  Abe Sapien  Abin Sur  Abomination  \\\n",
       "Agility                   True   False        True     False        False   \n",
       "Accelerated Healing      False    True        True     False         True   \n",
       "Lantern Power Ring       False   False       False      True        False   \n",
       "Dimensional Awareness    False   False       False     False        False   \n",
       "Cold Resistance          False   False        True     False        False   \n",
       "\n",
       "                       Abraxas  Absorbing Man  Adam Monroe  Adam Strange  \\\n",
       "Agility                  False          False        False         False   \n",
       "Accelerated Healing      False          False         True         False   \n",
       "Lantern Power Ring       False          False        False         False   \n",
       "Dimensional Awareness     True          False        False         False   \n",
       "Cold Resistance          False           True        False         False   \n",
       "\n",
       "                       Agent Bob  ...  Wonder Man  Wonder Woman   X-23  X-Man  \\\n",
       "Agility                    False  ...       False         False   True  False   \n",
       "Accelerated Healing        False  ...       False          True   True  False   \n",
       "Lantern Power Ring         False  ...       False         False  False  False   \n",
       "Dimensional Awareness      False  ...       False         False  False  False   \n",
       "Cold Resistance            False  ...       False         False  False  False   \n",
       "\n",
       "                       Yellowjacket  Yellowjacket II   Ymir   Yoda  Zatanna  \\\n",
       "Agility                       False            False  False   True    False   \n",
       "Accelerated Healing           False            False  False  False    False   \n",
       "Lantern Power Ring            False            False  False  False    False   \n",
       "Dimensional Awareness         False            False  False  False    False   \n",
       "Cold Resistance               False            False   True  False    False   \n",
       "\n",
       "                        Zoom  \n",
       "Agility                False  \n",
       "Accelerated Healing    False  \n",
       "Lantern Power Ring     False  \n",
       "Dimensional Awareness  False  \n",
       "Cold Resistance        False  \n",
       "\n",
       "[5 rows x 667 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here (create more cells as needed)\n",
    "powers_df = pd.read_csv(\"super_hero_powers.csv\", index_col=0)\n",
    "powers_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will check if it was loaded correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# There should be 167 rows, 667 columns\n",
    "assert powers_df.shape == (167, 667)\n",
    "\n",
    "# The first column should be '3-D Man'\n",
    "assert powers_df.columns[0] == \"3-D Man\"\n",
    "\n",
    "# The last column should be 'Zoom'\n",
    "assert powers_df.columns[-1] == \"Zoom\"\n",
    "\n",
    "# The first index should be 'Agility'\n",
    "assert powers_df.index[0] == \"Agility\"\n",
    "\n",
    "# The last index should be 'Omniscient'\n",
    "assert powers_df.index[-1] == \"Omniscient\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Perform Data Cleaning Required to Answer First Question\n",
    "\n",
    "Recall that the first question is: *What is the distribution of superheroes by publisher?*\n",
    "\n",
    "To answer this question, we will only need to use `heroes_df`, which contains the `Publisher` column.\n",
    "\n",
    "### Identifying and Handling Missing Values\n",
    "\n",
    "As you likely noted above, the `Publisher` column is missing some values. Let's take a look at some samples with and without missing publisher values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Banshee</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>183.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Bantam</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>-</td>\n",
       "      <td>Black</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>DL Hawkins</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>NBC - Heroes</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>Synch</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>-</td>\n",
       "      <td>Black</td>\n",
       "      <td>180.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Agent 13</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>-</td>\n",
       "      <td>Blond</td>\n",
       "      <td>173.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           name  Gender Eye color   Race        Hair color  Height  \\\n",
       "60      Banshee    Male     green  Human  Strawberry Blond   183.0   \n",
       "61       Bantam    Male     brown      -             Black   165.0   \n",
       "219  DL Hawkins    Male         -      -                 -   -99.0   \n",
       "650       Synch    Male     brown      -             Black   180.0   \n",
       "8      Agent 13  Female      blue      -             Blond   173.0   \n",
       "\n",
       "         Publisher Skin color Alignment  Weight  \n",
       "60   Marvel Comics          -      good    77.0  \n",
       "61   Marvel Comics          -      good    54.0  \n",
       "219   NBC - Heroes          -      good   -99.0  \n",
       "650  Marvel Comics          -      good    74.0  \n",
       "8    Marvel Comics          -      good    61.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "has_publisher_sample = heroes_df[heroes_df[\"Publisher\"].notna()].sample(\n",
    "    5, random_state=1\n",
    ")\n",
    "has_publisher_sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Chuck Norris</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>178.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>Godzilla</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Kaiju</td>\n",
       "      <td>-</td>\n",
       "      <td>108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>grey</td>\n",
       "      <td>bad</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>Flash Gordon</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Brundlefly</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>Mutant</td>\n",
       "      <td>-</td>\n",
       "      <td>193.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>Katniss Everdeen</td>\n",
       "      <td>Female</td>\n",
       "      <td>-</td>\n",
       "      <td>Human</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  Gender Eye color    Race Hair color  Height Publisher  \\\n",
       "175      Chuck Norris    Male         -       -          -   178.0       NaN   \n",
       "286          Godzilla       -         -   Kaiju          -   108.0       NaN   \n",
       "263      Flash Gordon    Male         -       -          -   -99.0       NaN   \n",
       "138        Brundlefly    Male         -  Mutant          -   193.0       NaN   \n",
       "381  Katniss Everdeen  Female         -   Human          -   -99.0       NaN   \n",
       "\n",
       "    Skin color Alignment  Weight  \n",
       "175          -      good   -99.0  \n",
       "286       grey       bad     NaN  \n",
       "263          -      good   -99.0  \n",
       "138          -         -   -99.0  \n",
       "381          -      good   -99.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "missing_publisher_sample = heroes_df[heroes_df[\"Publisher\"].isna()].sample(\n",
    "    5, random_state=1\n",
    ")\n",
    "missing_publisher_sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do we want to do about these missing values?\n",
    "\n",
    "Recall that there are two general strategies for dealing with missing values:\n",
    "\n",
    "1. Fill in missing values (either using another value from the column, e.g. the mean or mode, or using some other value like \"Unknown\")\n",
    "2. Drop rows with missing values\n",
    "\n",
    "Write your answer below, and explain how it relates to the information we have:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nOption 2; remove all rows where the publisher is missing. This approach minimizes the risk of altering the significance of the data.\\n\\nIf we were to opt for option 1, we would have to select values to substitute the NaNs. However, using random values for the NaNs is not feasible because we lack information about the distribution and frequency of different publishers. If we were to randomly assign publishers to fill the NaNs, we might unintentionally \\nover-represent or under-represent certain publishers for specific comic book heroes.\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate text\n",
    "\"\"\"\n",
    "Option 2; remove all rows where the publisher is missing. This approach minimizes the risk of altering the significance of the data.\n",
    "\n",
    "If we were to opt for option 1, we would have to select values to substitute the NaNs. However, using random values for the NaNs is not feasible because we lack information about the distribution and frequency of different publishers. If we were to randomly assign publishers to fill the NaNs, we might unintentionally \n",
    "over-represent or under-represent certain publishers for specific comic book heroes.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, implement the strategy to drop rows with missing values using code. (You can also check the solution branch for the answer to the question above if you're really not sure.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "heroes_df.dropna(subset=[\"Publisher\"], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now there should be no missing values in the publisher column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "assert heroes_df[\"Publisher\"].isna().sum() == 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identifying and Handling Text Data Requiring Cleaning\n",
    "\n",
    "The overall field of natural language processing (NLP) is quite broad, and we're not going to get into any advanced text processing, but it's useful to be able to clean up minor issues in text data.\n",
    "\n",
    "Let's take a look at the counts of heroes grouped by publisher:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Publisher\n",
       "Marvel Comics        379\n",
       "DC Comics            212\n",
       "NBC - Heroes          19\n",
       "Dark Horse Comics     18\n",
       "George Lucas          14\n",
       "Image Comics          14\n",
       "Marvel                 9\n",
       "Star Trek              6\n",
       "HarperCollins          6\n",
       "Team Epic TV           5\n",
       "SyFy                   5\n",
       "IDW Publishing         4\n",
       "Icon Comics            4\n",
       "ABC Studios            4\n",
       "Shueisha               4\n",
       "Wildstorm              3\n",
       " DC Comics             3\n",
       "Sony Pictures          2\n",
       "Titan Books            1\n",
       "Microsoft              1\n",
       "Rebellion              1\n",
       "Hanna-Barbera          1\n",
       "J. K. Rowling          1\n",
       "Universal Studios      1\n",
       "South Park             1\n",
       "J. R. R. Tolkien       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "heroes_df[\"Publisher\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two cases where we appear to have data entry issues, and publishers that should be encoded the same have not been. In other words, there are four categories present that really should be counted as two categories (and you do not need specific comic book knowledge to be able to identify them).\n",
    "\n",
    "Identify those two cases below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\"Marvel Comics\" and \"Marvel\" (missing the word \"Comics\")\\n\"DC Comics\" and \" DC Comics\" (with an extra space at the beginning)\\n'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate text\n",
    "\"\"\"\n",
    "\"Marvel Comics\" and \"Marvel\" (missing the word \"Comics\")\n",
    "\"DC Comics\" and \" DC Comics\" (with an extra space at the beginning)\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, write some code to handle these cases. If you're not sure where to start, look at the pandas documentation for [replacing values](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.replace.html) and [stripping off whitespace](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.strip.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "\n",
    "heroes_df[\"Publisher\"] = heroes_df[\"Publisher\"].replace(\"Marvel\", \"Marvel Comics\")\n",
    "heroes_df[\"Publisher\"] = heroes_df[\"Publisher\"].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check your work below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Publisher\n",
       "Marvel Comics        388\n",
       "DC Comics            215\n",
       "NBC - Heroes          19\n",
       "Dark Horse Comics     18\n",
       "George Lucas          14\n",
       "Image Comics          14\n",
       "Star Trek              6\n",
       "HarperCollins          6\n",
       "SyFy                   5\n",
       "Team Epic TV           5\n",
       "Icon Comics            4\n",
       "ABC Studios            4\n",
       "IDW Publishing         4\n",
       "Shueisha               4\n",
       "Wildstorm              3\n",
       "Sony Pictures          2\n",
       "Hanna-Barbera          1\n",
       "South Park             1\n",
       "Universal Studios      1\n",
       "J. K. Rowling          1\n",
       "Titan Books            1\n",
       "Rebellion              1\n",
       "Microsoft              1\n",
       "J. R. R. Tolkien       1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "heroes_df[\"Publisher\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering the Question\n",
    "\n",
    "Now we should be able to answer *What is the distribution of superheroes by publisher?*\n",
    "\n",
    "If your data cleaning was done correctly, this code should work without any further changes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x8fGxnv4ZHR1t1q9fn6PxkpOTzYcffmjq1q1rLBaLKV++fI7GcdY+8NNPP5lKlSqZwMBAM2LECGt73759zWOPPZajMf39/TN9vdavX28KFChgjDHmjz/+yFFBys/Pz3oUbJEiRaxFo99//90EBwfnKN769eub1atX5+ixWXHW/0JjjLnjjjusRwgXKFDAejrD5MmTs73Nri1iXbtERkaaxx9/3KYtp5zxGhQsWNAsW7YsQ/uyZcusR1v+/vvvdh956YzPmHSFCxc2y5cvz9UY6aKjo010dLSxWCwmMjLSejs6OtqULVvWNGnSJEeF5O7du5vp06c7JEYAt5ZTp06ZevXqmXHjxhljrp65ERcXZ2bOnOmw/03InhdeeMH6GZOcnGy2b99uevbsaYYNG2ZWrVqVx9HhZo4ePWoqVapkpk2bZoy5evBFUlKSWbt2rfWH0twewIC8deTIEVOxYkVrTnTp0iVz9uxZs27dOusBG2xjx/r9999NxYoVzYoVK4wxxpw8edLs2rXLvPnmm9YDGdxJvry+ZuV/2cSJExUdHa24uDiNGzdOBQoUkCSdOHFCvXv3zvG49evX1z///KOkpCQVKlTI2v7MM8/keOKRd999V6+88ori4uL0xRdfWCdc2bVrlx577LEcx+qMcZ0Vq6Ne12rVqslischcM3dU+u0ZM2bo/ffft16wOzU1NcfxFihQQKdOnVKJEiW0atUqPf/885IkX19fXbhwIcfj/v3335o3b55mzZqlP//8U23bttXXX3+tmJgYXbhwQa+88oq6du2qI0eOZHvMTZs2adasWfr8888lSY888ojGjh2runXr5ihGZ+0DVapUsZlNO91bb70lT0/PHI3Zpk0bde/eXePHj1etWrVksVj0/fffa9CgQWrbtq2kq7MVly1b1u6xw8LCdOrUKUVFRSkqKkrbt29X1apVdejQIZv9zx4zZ85Uz549dfz4cVWqVEleXl4291epUsXuMZ31v1CS4uPjVblyZUlX3xOJiYmSpJYtW2rYsGHZji8znp6e2rJli7Zs2SLp6vu4f//+OYrTGa9B586d1aNHD7300ks2+9bo0aPVpUsXSdLGjRtVsWJFu8Z1xmdMOm9vb5UuXTpXY0jSl19+qf3798vb21sNGjTQ4sWLbWLNjdKlS2vYsGHavn27KleunOE9kNN9AEDeK1iwoFJTU3XmzBmdOXNGo0aN0vfff68jR47o2LFjmjx5svr27ZvXYd6WzDWT1Rhj9Ndff+nXX3/Vjh07NHHiRP3zzz9KTExUWlqa9uzZo1q1aqlgwYJ5GzSy5OXlJS8vL4WFhen8+fOaMGGCVq5cqYMHD+rSpUtatmyZ7r///rwOE7mQL18+eXl5KTw8XOfPn9fEiRO1YsUKHTx4UJcvX9ZXX32lOnXq5HWYt5UCBQrI29tbhw8f1nfffaePPvpIP/zwgywWi3777TclJSWpR48eeR1m9uVlJRTO8eeff1qva3et33//3XqdL9jPUa/r4cOHs73khjOODmzZsqXx8vIyFStWNBMnTjSnTp3K0Of48ePGYrHcdKy4uDgzatQoU7p0aWOxWMy9995rPvjgA+sRYbeynTt3mnnz5pn58+ebXbt25Wqss2fPmqeeesp4e3tbJ4Px9vY2Tz/9tPXQ+x9//NH8+OOPdo/do0cP6xGc06dPN35+fiYmJsYULFjQdO/ePUfxbtu2zZQsWdJYLBbrktsJbJypbNmy1qPf7rvvPjNmzBhjzNUjDosUKZKXoTndlStXzKhRo0xYWJh1W4WFhZk33njDXLlyxRhz9VftuLg4u8adN29elvcNGjQoVzG//fbbpnfv3rk+xcTDw8MkJCRY/87smpE5de0Rltcvt+IEPgCylv6/5pdffrGePTBw4EBTo0YN4+XlZdq2bWtmzpxpzpw5Y55++mnTrl07jvRxgvTt8N1335nPP//cGHM1Xy5ZsqQJDQ01jz76qPVI/4ULF5oqVaowIcot7tixY6ZBgwamfv36pmDBgqZNmzbm7bffNnv27DH33Xdfjq9djryT/j6Ni4sz8fHx5t9//zX169c3DRs2NIUKFTJt2rQxb731ltm9e7epU6eOeeWVV/I4YveW2WfNqVOnzCOPPGJq1aplPDw8TP/+/c2KFStMUlKSadasmXn99dfzINKcoxiZh0aPHm0+/PDDDO0ffvhhri6O/8ADD5jZs2dnaJ83b56pV69ejsfdtGmTefzxx03t2rXNsWPHjDHGzJ0713z33Xc5HvOjjz7K9JDiTz/9NNPnYI9z586ZX3/9NcMp0TnlrNfVWU6fPm369OljWrdubT2U25ir15kYNWqUXWP98ccfJjU11XTv3t1s3br1hn3T0tKyVUj19PQ0RYsWNQMHDjS//PKLXfHcjLP2q7///tvUr1/fWCwWU6hQIVOwYEFjsVhMw4YNrYWPnDp79qz56aefzO7dux1WkE1NTTWXL1+23l60aJHp16+fmTx5co5npbzzzjtN+/btzfbt282hQ4ccVkCfO3euqVu3rgkPD7eOM3HiRLN06dIcj2nM1Ulc3njjDWPM1ZnF8+XLZ0qXLm28vb3NkCFD7Brr0qVLpmTJkjme2fxmDh48aPr27WsaNWpkYmJiTL9+/Rx2bcLExESbSZJyIygoyHz11VcZ2mNjY01YWFiuxm7btq0JCgoyJUuWNC1btjTt2rWzWbIrNDTUfPnll8aYqxPY5Pb9CeD2c+0kKeXKlTMvv/yySU5ONidOnDBbtmwxX3zxhc2XwU6dOpk+ffq41fW43EH66/n555+bQoUKmQEDBlhP8Tx79qzZu3evTf8XXnjBNGrUyGGfaci99G148OBBs2XLFrN7925z5coV8+eff5oZM2aYKVOmmH///dfav1mzZmbs2LF5FS5yIH0bL1myxNx3331m6tSpxpirl+xK38bXHqTy4IMPWi93Aftd+9nzyy+/mP3791svv5aQkGC+//57s2PHDpvH1K5d27z11lsujTO3KEbmoaioqExnYN2+fbuJjo7O8bgBAQHWD/FrHThwwO6JCtJ9/vnnxs/Pzzz11FPGx8fH+gX53XffNc2aNctxrGXLljXr1q3L0L5hwwZTtmzZHI2ZkJBgmjdvbj3K7Polp5zxuhpztQhTp04dhxdhHOn6I4s6dOhg4uPjczXm+PHjnXYUpDP2K2OuPu8aNWrYFE/37dtnatasaTp27Jjjcd1J/vz5M30f5Ma0adNMSEiIGTVqlPHz87P+f5k1a5apX7++Q9e1fft2M378+EyvpZgdERERDi+eG2PMypUrjbe3t7n77rvN888/b2JjY83dd99tfHx8brlrY61YscIEBQWZjRs3Wtv69u1rIiIizK+//pqrsbt163bDJbuGDx9uPVr3ZktOpaSkmN9++82m4A/Avaxevdr4+fmZ6dOnZzlr9vHjx82QIUNMcHCw036M+q/bvn27CQ4ONrNnzzYXL17MtM+mTZvMkCFDTGBgoPUoVuS9a4v60dHRpnLlyubOO+80MTExGb7nnj171rz00ksmNDQ007PNcGv7+uuvja+vr5k0aVKGyWrSJScnm5dffpltnAvX/uA1fPhwU65cOVOqVClTpEiRDN9fkpOTze+//24efPBBU61aNbfLSSlG5iEfHx/z559/Zmj/448/jI+PT47HDQwMtM4kfK2dO3daJ8Sw17UzEl87K++PP/5oQkNDcxyrj49Ppqc4Hzp0yPj6+uZozE6dOpk6deqY77//3vj7+5tVq1aZefPmmXLlylknssgJZ7yuzi7CnD592nz77bdm3rx5Zs6cOdZl7ty5do1jsVhsipHX7gM55ehTJ6/ljP3KmKv7wPfff5+h/X//+1+OC9LJycnmlVdeMbVr1zalSpUyJUuWtFlyK6t9IP39bK+WLVtaT6FylDvvvNMsWbLEGGO7b/388885mkU83aVLl0y3bt0cOvPxmDFjTNeuXR3+YV+tWrVMj9QcMmRIjiewiY+PN0888YQJDw83np6eDivCGWPMggULTKFChcyOHTtMr169TERERKYzd+elX3/91Xz11VfGYrGY2bNnm6VLl2a62OvcuXOme/fuxtPT03h6elr3r379+lkvAwDg1nPtUTtpaWnm0qVL5sknnzT9+/e3thljrJexMObqzNpdu3Y1pUuXztHlUpBRZp8V7733nmnQoIE5d+6c9fW/9siguLg406NHD1OjRo1cT/AIx9uyZYsJCgqyHi03b948Y7FYzPjx46195s+fbzp37myKFy+e6fcp3FouXbpk/TstLc2cOXPGNG7c2IwcOdKm37Xv0/nz55suXbqwjR1k+PDhJjQ01KxYscIcP37ctGvXzuTPn9/mzNpp06aZpk2bmvr161u32bWfYbc6JrDJQ5GRkdqyZYtKlixp075lyxZFRETkeNz7779fY8aM0YIFC6yTaqSmpmrMmDG67777cjTm/v379cADD2RoDwwM1JkzZ3Ica9GiRbVnzx5FR0fbtP/000/WSUfstW7dOi1btky1atWSh4eHoqKi1LhxYwUGBmrMmDFq0aJFjsZ1xus6ZcoUffDBB2rbtq3efPNNa3vNmjU1aNCgHI2Z7quvvtLjjz+uc+fOKSAgwHpRcOnqRBudO3fO1fi5ZXI4gUp2OGO/kqS0tLQMk1VIVy/SnZaWlqMxn3rqKW3cuFGdO3dWeHi4zXbKrZvtA+mTmNijVatWev755/Xzzz9nOnlH69at7R7z0KFDql69eoZ2Hx8fnTt3zu7x0nl5eWnJkiXZnqgmO/73v/9p7dq1WrVqlSpXrix/f3+b+xcvXpyjcX/99Vd9+umnGdq7d++uSZMm5WjMbt266ejRoxo2bJjD962OHTvq9OnTuu+++1SkSBFt3LjRIRPPpDt58qT2798vi8WismXLqkiRInaPUb58eZUvX17Dhw/XI488kuvJddINHTpUP/30kzZs2KAHH3zQ2h4TE6Phw4frxRdfdMh6ADjOlClT9OOPP2rGjBnKly+fLBaLvLy8dOjQId15552SZP0fmZ7j/f3336pXr57Onz+v1157TVFRUXkW/+3i448/1uDBg7V//37rRG2StHfvXv3999/W/9NpaWny8PCQJP3+++8KDQ3V8OHD5e3trdDQ0DyJHRmZ/z/p0HfffaeWLVuqT58+iouL07Bhw9SzZ08NGDBAknT+/HnVrFlTR48e1fDhw1WqVKk8jhw3MmHCBOXLl0/9+vWTxWKRxWKRj4+P4uLiFBISYtM3/X168eJF1ahRQ4cPH9awYcMcmhP+F+3evVubNm3S3Llz1aRJE3355ZfasGGDHnjgAT3zzDOyWCx68skn1aFDB0VERKhly5by9PTUlStXlC+f+5T43CfS29BTTz2l2NhYXb58WQ0bNpQkrV27VoMHD9bAgQNzPO64ceP0wAMPqFy5ctZZyr777jslJSVp3bp1ORozPDxcBw8ezFDc2bx5s+64444cx9qxY0f1799fAQEB1mLnxo0b9dxzz6ljx445GvPcuXMqWrSoJCk4OFgnT55U2bJlVblyZf3www85jtUZr6uzijCSNHDgQHXv3l2jR4/O9Zfw9A+i69tyy5HFkWs5Y7+SpIYNG+q5557TggULrD8YHD9+XM8//7waNWqUozFXrFih5cuX53jm8Btx5D6QrmfPnpKkkSNHZrgvpzPAlyxZUrt3787wRW/FihWqUKFCzgL9/9q1a6elS5daE+LcKliwoB566CGHjHWtIkWKaPfu3SpTpoxN++7du63/z+y1efNmfffdd6pWrVqu48vq9StatKiqV6+uadOmWdsmTJiQ4/WcO3dO/fr109y5c60Ffk9PT3Xp0kVTpkzJ0X48fPhwSY4pcErS0qVLtWjRIt177702/8MqVKigP/74I0djAnCugIAADR06VF5eXrp48aJ8fX114cIFFShQQH/99Zek/yuAGWMUHx+vCRMmqE+fPurQoUMeR3/76NSpk+677z4VKFBAp06dsv5A3KxZM3355ZdatGiRHn30UXl4eCgtLU3nz5/XW2+9pbZt2+b4YAI4T/pn4JkzZ1S8eHHFx8erdu3aatGihd59911J0sqVK3X48GH17NlTgwcPthb7cWtKTU3V4cOH1bdvX1ksFuv/xTNnzujSpUtKTEyUJF2+fNl6QMKBAwe0YsUKPfPMM3rxxRfZxjlw7Q8w0tXPrHbt2ikmJkYbNmxQz549NXLkSPXt21cPPvignnvuOZ09e1b9+/dXmzZtrGO4UyFSohiZpwYPHqx///1XvXv31qVLlyRJvr6+GjJkiIYOHZrjcStUqKA9e/Zo6tSp+umnn+Tn56cuXbqob9++Cg4OztGYzz77rJ577jl99NFHslgs+uuvv7Rt2zYNGjRIr776ao5jHTVqlI4cOaJGjRpZ3zxpaWnq0qWLRo8enaMxy5Urp/379ys6OlrVqlXTjBkzFB0drffee0/h4eE5jtUZr6szizDHjx9X//79HVKEMsaoW7du8vHxkXT116+ePXvm+qiwa8fMSk6ONHPGfiVJU6dOVZs2bRQdHa3IyEhZLBYdPXpUlStX1vz583M0ZqFChXK8/9yMI/eBdDk9AvRGXnjhBfXp00cXL16UMUbff/+9FixYoDFjxmjmzJm5Grt06dJ6/fXXtXXrVtWoUSPDPtu/f3+7xps1a1au4snK008/rWeeeUZ//vmn6tSpI4vFos2bN2vs2LE5/nEqMjLSYUcg//jjj5m2lypVSklJSdb7c/sDw4ABA7Rx40Z99dVX1gL95s2b1b9/fw0cOFDTp0+3e8zz58+rb9++mjdvnrVYnpsC58mTJzMtEJ87d85pP7AAyJ1u3bpJkrZv365JkyZp1KhRKl26tF544QU1bNhQw4cP12uvvSbp6v+xKVOmaM2aNbk+SwW2LBaLoqKitGfPHtWtW1efffaZHnzwQVWoUEGVKlXS3LlzdeXKFT3++OM6ffq0pkyZom+++YYjzm9R6UdGBgUFadKkSZo/f77atm2rqVOnSrqaM37++efy8fGx/giAW5cxRp6ennrnnXckSVu3btWWLVvUq1cvhYWF6amnntIrr7yiu+66S02aNLE+7v3339fu3bvVpUsXtnEOXFuI3LJli+rWratSpUqpc+fO8vDw0Ny5c9W6dWv16tVLklSsWDGVKFFCn332mfXoVUk2xUx3YTHOPFcS2ZKcnKxff/1Vfn5+KlOmzE2LM3nl5Zdf1sSJE3Xx4kVJV4/eGzRokF5//fVcj/37779bC3yVK1fO1akwH3/8sS5fvqxu3brpxx9/VNOmTXXq1Cl5e3tr9uzZevTRR3Mdr6PMmjVLw4YN0/jx49WjRw/NnDlTf/zxh7UIk5uj+Nq3b6+OHTs65Bf9J598Mlv97CnUeHh4qEOHDvLz83PYmNdz5H51rdWrV+u3336TMUYVKlRQTExMjseaP3++li1bpjlz5ji0aCg5dh+QpCtXrsjX11e7d+9WpUqVHDJmug8++ECjRo1SXFycpKsftCNGjFCPHj1yNe71l8G4lsVi0Z9//pmr8R3FGKNJkyZp/Pjx1qN0IiIi9MILL6h///45KnKtWrVK48ePt/4g4w5CQkL0+eefq379+jbt69evV4cOHXTy5Em7x3z22We1Zs0aTZ06NUOBs3HjxnYXOOvVq6eHH35Y/fr1U0BAgPbs2aOSJUuqb9++OnjwoFauXGl3jABc4/3339eMGTNUoUIFjRgxQqVKldLMmTPVq1cvxcTEKDAwUB4eHlqxYoXWr1+f6dkryL20tDR17NhR69at0yeffKImTZrohx9+0Lhx47R161ZJUlhYmI4ePaoVK1awHW4R6cXH48ePyxijK1euWPOLVq1aae3atfrxxx8VHR2t1NRUjRo1SrNmzdL69etVvnz5vA0edjHG6JlnntHWrVvVo0cP9erVS1euXFFsbKxmzZqll19+WT4+Pjp27JgWLFigTZs2qWrVqnkdtttJf09J0iuvvKJFixbpueeeU9++fWWM0cX/x95Zh0Xxfm383qUbREAsQgywviiKgRKiYoIdGAh2oIjYAipgKzYWgoWJjYmCHSCpqIiKhV1Iw573D96dHyuo7LAI6nyuay/ZZ2bvPc5OPWdOZGejffv2sLOzw8KFC5Gfn4/+/fvDw8ODydApqvGnwTkj/xLi4+PRqFEj8Pl8xMfH/3TdJk2asP6ezMxM3Lt3DwKBACYmJiL1XiormZmZuH//PmrXrl2szsWv+B3btbycMNu2bcOCBQswYsQIidX2kyR8Ph+vX79mnYL6t2BqaoqUlBQQEfT19Yv9TuKWFjh27Bjz97t37yS+D9SpUwehoaESu+HIz8/H7t270blzZ1SrVg3v37+HQCCo1PvFwYMHsX//fjx79oyJahdSllIQQtLT0wEUpmiUBQ0NDWRmZiI/Px+KiorFfv+PHz+WSR8AXrx4AR6Phxo1apRZCwAUFRURHR3N1HATcvfuXbRs2ZJV+QpJOzivXbsGOzs7ODo6IigoCGPGjMHdu3dx/fp1REZGonnz5mLbyMHB8fvYvHkzdu3ahZo1a2LRokXQ09NDdHQ0Nm7ciC9fvqB69eoYO3ZssfMQB3t+NFkeNmwYjhw5ggMHDqBz58549eoVXr16hdOnT8PIyAjm5uY/fajI8fsQ/oaHDx+Gt7c3Pnz4ABUVFbRp0wbbtm3Dw4cP4eTkhIcPH6JmzZrQ1NREUlISTp48yTmT/1BycnIwefJk3LlzBwMHDsTEiRMhLS2N9evXY+fOneDz+dDV1cWCBQvQuHHjijb3j2bBggVYu3Ytjhw5AgMDA5H+IbNmzcKqVavg4uKCW7duIS8vD9HR0ZCSkvqjHZEA54z87fTu3RtBQUFQVVVF7969f7quOOmpRR07fD6f8ZJ/D9uabkUp6+Rz6tSpWLhwIZSUlH5Zx60stcckQXlu1/J2wvwsVJvtfiCMLP1VNGNpkJKSQlpamsT+v+W1X61ZswajR4+GvLw8k7bwI8RN+QXApIX9CGG9u9JS2hB9tvvA9u3bceDAAezatUti6eWKiopISkoq1+YAubm5ePLkCerUqVOmeipr1qzBnDlzMHz4cGzZsgUjRoxASkoKbt++jQkTJsDX11eCVpeN4ODgny4fPnw4K12BQAAfHx+sWLEC3759A1DoOHV3d8ecOXPKlCbSoUMHaGpqYseOHUyqT1ZWFoYPH46PHz/i/PnzYmuWh4MzISEBy5cvR3R0NAQCAZo1a4YZM2ZwN+McHJUI4SQtKSkJBQUF4PP5TAmcgIAA7N69G7Vq1cKCBQtgZGSE3NxcyMrKoqCggKt5JkGEv8P169dx584d6OjooFu3bsy9pNAhefDgQZHUT47Kx4ULF9CtWzcsX74ctWrVwocPHzB9+nRYWFjg8OHDAAqd/V++fEG1atXQvn17rvHTH4LwOH3x4gWUlJQgEAigqamJnJwcTJw4EbGxsRg0aBDGjh0LRUVFfP78Gerq6sjKypLIvPBf5v379+jduzdGjhwp0lxU2IwmKysLvr6+iI2NhY6ODgICAiAjI/NXXKs4Z+RvZsSIEVizZg1UVFR+mfoqTnpqamoqateuDR6Ph9TU1J+uy+aiIMnJp7W1NQ4fPgx1dXVYW1v/cD0ej8eqMUzfvn1hZmZWrL7MsmXLcOvWLRw4cKDUWuW9XX+HE0aS6OrqIiMjA/369YOLiwvatGnDWkvSkZHltV8ZGBggKioKmpqaf0zKb3liamqKR48eIS8vD3p6esVqMLKJDLS2tsbkyZPh4OAgISv/R2ZmJiZNmsQ45h4+fAhDQ0O4urqievXqYtehEnZnHjRoEFRUVBAXFwdDQ0N4enri48ePTJ2k0tCsWTOEh4dDQ0MDpqamP32yKYmIS0kxa9YsbNu2DfPnz0fbtm1BRLh69Sq8vb0xatSoMjlkExMTYWdnh+zsbDRt2hQ8Hg+xsbGQl5fHmTNn0LBhQ7E1y8PBycHB8WcQGhqKMWPGQE5ODlWrVsWwYcOYB5YBAQHYs2cP9PT04OXlxXR//dMjTSojJ0+eRK9evdCsWTPcunULAwYMgJubG1q2bAmg0CEZFhaGHTt2oGvXrhVsLcePmDZtGl6+fImQkBBmLCYmBra2thgwYIBIMzuOP4/Q0FDMmDEDAoEArVq1grOzMzp06MA4JOPi4jBw4ECMGTOGuf/nzpfi8/02e/LkCRo1aoSdO3cWC1bLyclhSvhlZGQw2/1P65r9IzhnJEepKM/Jp6TR0tLChQsXikWoJCQkwNbWFm/evKkgy4pTnk6Y8qCgoAAnT55EUFAQTp48CQMDA4wYMQLDhw9HtWrVxNKKjIxE27Zt/4oT6Z+E8EkmWyQdyQkABw4cwMyZM+Hm5lZik5mylJaYPHkyrl69Cn9/f9jZ2SE+Ph6GhoY4duwYvLy8ftiY5UcUfYCgra2Nc+fOoWnTpkhOTkarVq3w4cOHUmvNnz8fHh4eUFRUlNh2/fr1K1RVVZm/f4ZwPXGpXr06AgICiqX5Hz16FOPHj8fLly9Z6QrJysrCrl27ROqyOjo6sn7yLgkH5+/YrhwcHJKDiPDx40d06dIFEydORPXq1XH58mWsW7cOkydPZpovbt68GevWrYO5uTk2btzI3ZNIGOE0c+TIkWjZsiXGjBmD6OhoDBw4EI0bN4aHhwdat24NAHBwcEBMTAySkpIkXkObgx1Fo4uNjY3Rv39/kYd4wsistWvXIigoCGFhYdDW1v7j69j9izx+/Bg2NjaYPn06MjIycPPmTSQnJ8PHxwc9evRgUrYvXLgAV1dXTJw4saJN/mt48+YNHBwc0K1bN7i7u0NBQYE5fo4dO4abN2+K+Fr+pmOLu+L+pbx8+RJXr17F27dvi3W/ZZNKGhwcjK1bt4pMPps2bYoaNWpg/PjxlcoZ+e3bN8jKyhYbl5GR+eUk8ldIeruOHz8e7u7uePHihUScMOWdUiwlJYWePXuiZ8+eePv2LXbt2oWgoCDMmzcPdnZ2cHFxQY8ePUoVKWtpaSn29/8tVKlSBQ8fPkTVqlWhoaHx0wtKWer6LVmyBPr6+kzTpn79+uHQoUPQ1dVFWFgYq7qPbJyNv0JoX9F9suiNbFlKSxw5cgT79u1jijwLMTExQUpKith61apVw4cPH6Cnpwc9PT3cuHEDTZs2xZMnT8TuXF10W0pqu2poaDDlD9TV1Uvct8q6XT9+/FhiIfoGDRpIpA6lgoICRo0aVWYdIY0aNUJycrKIg3PgwIFiOTh/x3bl4OAoO98fhw0bNoSDgwNUVVXRtGlTKCkpwc/PDwDg6emJ0aNHQ0ZGBjY2NpwjUoIIf4fXr19DVlYWOjo6TBRk8+bNsXfvXgwaNAhLly7FjBkz0KpVKxw5cgSvXr3iHJGVCB6Ph+PHj2PUqFE4f/48evfuDU9PT5w6dQpdunRhUkTV1dXx9etXSEtLM9fHv8VZ8jdT1KklEAjQqVMnjB8/HgBw69YtrF27FrNmzQJQ2KDI398fM2bMQLdu3SrM5r+FJUuW4OnTp9i4cSN0dHTQuHFjrF+/Ho0aNULXrl0hKyuLrKwsbNu2DdLS0iK/1d90bHFX3Qrkw4cP8PT0xMWLF0t0brGd1G3fvh1jx46FrKwsNDU1RXZYHo/HyglVXpPP7OxsrF279ofbgE1qYqNGjbBv3z7mqbeQvXv3MrWC2CDJ7ers7Ax/f3+JO2FWrVoFR0dHyMvLY9WqVT9cj+1+UBRtbW20bdsWDx48wMOHD5GQkAAnJyeoq6tj+/btxZpF/E4kuV/9qv5kUUpbi3LVqlVMcxJ/f/9S64vLpk2bsGvXLgCFHcDPnz+P06dPY//+/fDw8MDZs2dZa0dHRyMpKQk8Hg8mJiZlKk7+5MkT1p/9Fe/evSuxFEBGRgari7mNjQ2OHz+OZs2awcXFBW5ubjh48CCioqJ+WQf4d3DhwgWmlufFixfL5TuaNm2KdevWFXvgsW7dOlYO7mPHjqFLly6QkZERacBUEmwbb5XVwVl0u164cOGvuhHk4PhbEN47nThxAps2bYKSkhLu3bvHHK9aWlpwdnYGUFi6JzMzE4sXL/5l2SQO8eHxeDh48CBmzZqFr1+/Ij09HYaGhsy9gtAhOXToUMyZMweLFy9GixYtRJo2cFQcResH7tu3D/Pnz2caetatWxeBgYEAgC5dukAgECAhIQHVqlXjHPp/EMLf+Ny5cwgLC4NAIMDbt2+Z5S1btoSrqyvWrFkDT09P5Obmok+fPli9enUFWv13kJ+fD2VlZeY6tXz5cmzevBm9evWCm5sbgoODoa2tjYSEBKSnp+POnTt/bbQxl6ZdgXTp0gUpKSlwcXGBjo5OsZ2LbXOBWrVqYezYsZg1a1aZGgkUxdzcHObm5sUmn5MmTcLt27dx48YNVrqDBw/GuXPn0Ldv3xK3AZtooWPHjqFPnz4YPHgwbGxsAADh4eEICQnBgQMHWKdES3K7Cpu3ZGVl/XS9ylhL8s2bN9i5cye2b9+Ox48fw8HBAS4uLrC1tUVWVhbmzp2LgwcP/rLGZnkiyf3q+/qT0dHRKCgoQP369QEU1iCUkpJC8+bNWdU4LU8UFBTw8OFD1KpVC5MnT0Z2djY2bdqEhw8fwtzcHJ8+fRJb8+3btxg4cCAiIiKgrq4OIsKXL19gbW2NvXv3QktLqxz+J+yxtLRE3759MWnSJKioqCA+Ph4GBgaYOHEiHj16hNOnT4ulJxAIIBAImBvu/fv348qVKzAyMmIeVpSWX0XFFkUSEYeSIjIyEt26dUPt2rXRunVr8Hg8XLt2Dc+fP0dYWBjatWsnlt73jcJ+BNuow+DgYFStWpV5kj99+nRs3rwZJiYmCAkJEfs8K2xyURLv379H1apVxbaRg4NDMkRGRqJ79+7o2bMnsrKycPz4cXh4eDDRkEBhMMC6deuwfft2pib03zbBq2hSUlJgZ2eHkSNHQktLC2vWrIGGhgbc3d3RvXt3Zr1bt25hwoQJOHz4MGrWrFmBFnN8z82bN7F+/Xo8ffoUmzdvZoJSLl68iBUrVuDu3bvQ0tKChoYGbt68iYiICPz3338VazTHLynq0AoLC0Pv3r3RsmVLfPjwAUlJSTh+/LhI5GNUVBQWLlyId+/e4ezZs1BSUuLOl2IiEAjA5/NFtn1mZib27duHMWPGYPz48UxwyqpVq3D//n28ffsWDRo0wMKFCyEtLf3X1Ij8Hs4ZWYGoqKjgypUrrCJJfoampiZu3bqFOnXqSExT0pNPIWpqaggLC0Pbtm0lZitQWCjbz88PsbGxUFBQQJMmTeDl5VWm1GBJbldJN2/5XfTo0QNnzpxBvXr1mI5f33dUfvXqFWrWrFksGvF3Ul771cqVKxEREYHg4GBoaGgAAD59+oQRI0agXbt2cHd3Z6UrEAjw6NGjEqM427dvz9re6tWr4+DBg2jTpg3q168PHx8f9OvXDw8ePECLFi1YlS0YMGAAUlJSsHPnTqY78b179zB8+HAYGRmJFDUvLTt27Pjp8qKd5cTl2rVrsLOzg6OjI4KCgjBmzBjcvXsX169fR2RkJJo3b15qrZs3b+LYsWPIy8uDra1tmbt+Fu12/eHDB/j4+KBz585M/azr16/jzJkzmDdvHtzc3EqlGR8fX+rvL0stzlevXmH9+vUidR3Hjx9fKaNa6tevj40bN8LGxgbXr19Hhw4d4O/vjxMnTkBaWhqhoaFi6Tk4OCA0NLSY4/TNmzfo0KEDEhMTJWk+BwdHKXny5AkuX76Md+/ewd3dHV++fMGBAwcwfvx4zJw5EwsWLGDWFdb31dTUrChz/1piY2Oxc+dO5OfnM1FUsbGxmDp1KuTl5TF+/HgRh2TRBg0clYf9+/dj9uzZePHiBQ4dOiTioHr48CEePnyI48ePw9DQEA4ODsxDeo4/g/fv3+PgwYPg8XjMvfGqVatw8OBB7NmzR6SR1J07d1CtWrVKeY/3J3Hr1i2mXAVQ6JAMCQnBuHHjMHHiRJEMO6EDE/h7mtWUCHFUGGZmZnT9+nWJ63p4eNCiRYskrvvy5UuaPXs29e7dm3r16kVz5syhly9flknT2NiY4uLiJGRh+SLJ7crj8ejt27cS0SqKm5tbqV9scHZ2pmvXrv10HYFAQE+fPi215smTJ+n06dPFxk+fPk1hYWFi20hUfvtV9erVKTExsdh4QkIC6erqstK8fv06GRgYEJ/PJx6PJ/Li8/llsnfChAmkp6dHtra2pKmpSenp6UREtHfvXjI1NWWlqaqqSrdu3So2fvPmTVJTU2Olqa6uLvJSUlIiHo9HcnJypKGhwUqzKPHx8TRs2DBq2LAhGRsbk6OjI8XHx4ulERoaSlJSUqSkpERqamrE5/Np1apVZbZNSO/evWnt2rXFxteuXUv29val1hHuN9/vS5Let/4kFBQUKDU1lYiIpk+fTkOHDiUiosTERKpatarYei1btiQnJyeRsVevXlGDBg2oT58+ZTeYg4NDbF6/fk1SUlIkKytLCxcuZMazs7Npy5YtJC0tTd7e3hVo4b/B169fadCgQaSpqUmdO3cWWRYdHU3W1tbUo0cPOnToEDMuEAh+t5kcpeTkyZPUqFEj6tatG924caOizeGQEElJScTj8cjIyIj279/PjD969IhGjRpFampqdOrUqQq08O/jypUrxOPxaPny5SLj3759oxUrVhCPxyMfH58Ksq7i+EtdrH8GGzZswMyZM+Hp6YlGjRpBRkZGZDnbjpyLFi1C9+7dcfr0aTRu3LiYbmnr2gnJy8tDp06dsGnTJok3qlmxYgVmzJiBgICASpmSXBRJb9d69er9Msxd3NTM0nYGFje8/ubNm/j48SO2bdvGjO3YsQNeXl7IyMiAg4MD1q5dCzk5OfB4PLF+y5kzZ2Lx4sXFxokIM2fORJcuXcSyFSi//err16948+ZNse67b9++RXp6OivNsWPHwszMDCdPnoSurq5EUx9WrVoFfX19PH/+HEuXLoWysjIAIC0tjSlQLS4CgaDYvg8UNohiGw1bUrp4cnIyxo0bBw8PD1aaRWncuLFIFCIb/Pz84OTkhICAAEhLS8PHxwc+Pj6YMmVKme0DgDNnzmDJkiXFxjt37oyZM2eWWqc8628W5fPnz9i2bZtI3VBnZ2eoqamJrfWrZltFYVPrVllZGR8+fEDt2rVx9uxZJspUXl7+l6UySiIsLAzt27eHm5sbVq1ahZcvX8LGxgZNmzbF3r17xdbj4OAoOzo6Ojhw4ABcXFyQkJCAjIwMKCkpQU5ODsOGDQOfz8fIkSMhKyvLNGTgkBz0/+mHKioq8PDwAJ/Px9mzZxEUFAQnJycAQLNmzbBixQq4uLhg79696NSpE5SVlbmUz0qA8PeLj49HWloa3rx5g4EDB6Jr164oKCjAggULsHbtWvD5fLRo0ULkMxx/Hjo6OnB3d8fq1avx6tUrZrxOnTqYMWMGpKWl0bVrV5w5cwYdO3asQEv/HurUqQMvLy/4+fmBz+cz96JKSkqwtbWFuro65s2bBxUVlTL3dfiT4JyRFYi6ujq+fPnC1DUUQmXsyOnn54czZ84w4fLfN1oRFxkZGSQmJpbLBcfMzAzZ2dkwNDSEoqJiMScHmzppfD7/p7ZWlu06f/58VhP3n1FeDSu8vb1hZWXFOAYTEhLg4uICJycnGBsbY9myZahevTq8vb3F1k5OTi6xsVCDBg3w6NEjVvaWx34FAL169cKIESOwYsUKtGrVCgBw48YNeHh4sG5ekpycjIMHD8LIyIjV53+GjIwMpk2bVmy8LA40GxsbTJ48GSEhIUy6xsuXL+Hm5oYOHTqw1v2eunXrYvHixRgyZAju379fJq2CggIcPnyYcZwZGxvD3t5erJSHBw8eYPfu3cxnPDw84O3tLbEagZqamjh8+HAx5+uRI0fESiP8HQ91oqKi0LlzZygoKKBly5YgIqxcuRK+vr44e/YsmjVrJpbez5ptFYVt462OHTti5MiRMDU1xcOHD5lUs7t370JfX19sPU1NTZw5cwYWFhYACsuCNGvWDLt375ZYnWYODo6fU5IjpFevXhAIBBg0aBBq1qwJPz8/yMnJQVZWFo6OjpCRkYGZmVkFWfx3IvwdsrOzoaCgAIFAAFNTU8ycORMCgYDpBDtkyBAAgKmpKbZv3w51dXXmASlHxcPj8XDo0CFMnToVWlpayMnJwZw5c7B9+3b06NEDeXl5WLx4MdatW4exY8cyJbs4/kw0NDQwZ84c5Ofnw93dHXp6ekxPhTp16mDKlCmQk5ND7dq1K9bQP5yiqdbVqlXDuHHjICUlBW9vbxAR0yRVTU0Nffv2RZ8+fSQ6l/oT4JyRFYijoyNkZWWxZ8+eEptssGXlypUIDAxknkRKgmHDhmHbtm0lRrCVhUGDBuHly5fw8/OT2DY4fPiwyPu8vDzExMQgODgY8+fPZ60r6e06cODAP6ZmZGxsLBYuXMi837t3L8zNzbFlyxYAhc19vLy8WDkj1dTU8Pjx42JOgUePHkFJSYmVveWxXwFAQEAApk2bhiFDhiAvLw8AIC0tDRcXFyxbtoyVprm5OR49eiQxZ2R5dyZet24d7O3toa+vj1q1aoHH4+HZs2do3Lgx07lbUkhJSYk8sWVDYmIi7O3t8fr1a5GmQ1paWjh27BgaN25cKp1v375BXV2deS8nJwcFBQV8/fpVIs7I+fPnw8XFBREREUzNyBs3buD06dPYunUrK83q1avDysoKVlZWsLS0lFg9Jzc3N/Ts2RNbtmxhnLP5+fkYOXIkpkyZgkuXLomlV97RnOvXr8fcuXPx/PlzHDp0iHHuRkdHY9CgQaw0a9asiXPnzsHCwgIdO3bEzp07uYkZB8dvQugAu3TpEm7cuIGXL1+iZ8+eaNSoEfr06YNdu3Yxzq9FixZBVlYWcnJyGDp0aAVb/nch/B1OnTqFDRs2ICMjAyoqKvDz80OjRo0wZ84c+Pr6IiAgAHw+H4MHDwYAidfK5yg7N2/exKhRo7BixQqMGDECz549g76+PhITE2Fra8s8cJ8xYwbk5eVhamoKeXn5CraaozQIj9M7d+7g6dOn+PTpE7p27QptbW2sWLECBQUF6Nu3Lw4dOgR7e3sAhdl7S5cuLTETiuPnXLx4Ea9evYKjo2OxpjXa2toYPXo0AGDevHl48eIFOnTogLVr10JBQQGdOnUCj8f7u2tEfgfXwKYCUVRURExMjMQL/larVg2XL19G3bp1JaY5adIk7NixA0ZGRjAzMyvmJBI3RVmIoqIirl+//ltuTPbs2YN9+/bh6NGjrD4vye0q7KYtaWekONF54jRtkJeXR3JyMmrVqgUAsLCwgJ2dHebOnQsAePr0KRo3bswqVXn06NG4ceMGDh8+zDQHevToEfr06YMWLVqwcsSU936VkZGBlJQUEBGMjIzEdpoWbTKSkpKCuXPnwsPDo8T0f3GbjJR3Z2Ih586dE2leYmtry1rre6cpESEtLQ3r1q1DrVq1cOrUKdbarVq1gra2drGmQ05OTnj79i2uX79eKh0+n4/g4GCRaOZBgwbB398fOjo6zBgbB6+QmzdvYs2aNUhKSmK2q6urK8zNzVnphYSEIDIyEhEREXj48CF0dHRgaWnJOCeFDYjERUFBATExMUxnTSH37t2DmZkZMjMzWelWZn7U+TwzMxNycnKQkpJixipT53MOjr+V0NBQDB06FB07dsS9e/cgIyOD+vXrY8WKFTAwMMCBAwfg5OSEoUOHYs2aNZCVla1ok/9KTpw4gT59+jAZF9HR0bh58yaCgoLQp08fxMbGYuXKlbhz5w48PT3Rv3//ijWYo0R2796NEydOICQkBMnJyejYsSM6d+6MTZs2iawnfIhrYGBQQZZysOHgwYMYNWoU6tWrh/j4eNSrVw8DBw7E1KlTIS0tDTc3N2zduhVBQUHcMVoGPn/+jBEjRuD58+eYPn06sy2/j+T/+PEjjh49Cg8PD+jo6KBKlSq4cOECZGRk/rnyB5wzsgJp3749PD09yzSJL4lFixYhLS1NrDpcv8La2vqHy3g8Hi5cuMBKt1mzZtiwYQOT8lqepKSkoEmTJsjIyGD1eUlu1/Lqpj1ixIhSr7t9+/ZSr6unp4edO3eiffv2yM3Nhbq6Oo4fP86EkickJMDS0pLVJPzLly+ws7NDVFQUatasCQB48eIF2rVrh9DQUJFotNLyO/crNghLCfzo9CtcVlaHYXmwY8cODBgwoFjny9zcXOzdu5dV5+vvnaY8Hg9aWlqwsbHBihUroKury9peBQUFREVFFavzmZiYiBYtWpS6ZmBp0m8r4+8l5M2bN7h48SJOnDiBffv2QSAQsLZVR0cHO3fuLNZN/MyZMxg2bBjevHkjlp4wTaU0sH3w9X2NS2NjY7i4uJS6VIY4NUeHDx/OykYODo7S8fTpU3Tq1AlTp07F2LFjARRem/bs2QNVVVWsX78eWlpaCAkJwZQpU5CQkPDHZKL8KRQUFCAnJwc9evRAy5YtsWjRImbZuHHjsGvXLkRFRaF+/fqIiorC5s2bMXv2bFalMTgky8OHD3Hv3j0mLRcoLD2TmJiIkJAQNG3aFHZ2dti4cSP4fD527tyJu3fvSjw7juP3kJCQgI4dO8LPzw/9+/eHtLQ0PDw8EBsbi549e8Ld3R3fvn2Du7s7jhw5gidPnnAlFMpAdHQ0Vq1ahdTUVEyYMAEDBw4EAGbOV9TRmJmZiU+fPkFXVxd8Pv+fiogUwjkjK5ADBw7A29tbYhFRQnr16oULFy5AU1MTDRs2LKYrTkRceXP27FnMnz8fvr6+JW4Dtk18vicrKwuzZs3CqVOn8ODBA1Yaf9J2lTRjxoxBQkIClixZgiNHjiA4OBivXr1iIg12794Nf39/3L59m5U+EeHcuXOIi4uDgoICmjRpgvbt27O2t7z2K2tr658+rSqtUz41NbXU3ympGoDZ2dkSSan5UVTvhw8foK2tXemccf/99x9WrlxZrDbvhQsXMHnyZCQkJFSQZaI8e/bsp8vZ1u359u0brly5wkRIxsTEwMTEBJaWlqWu1fg9rq6uOHz4MJYvX442bdqAx+PhypUr8PDwQJ8+feDv7y+W3s8edhWF7YOvkmpcRkVFISsri1WNSw4Ojt9D0XpbRYmLi4OdnR2OHj2Kli1bMusGBQVh+fLlCAoKYsbT09OhoqLyW+3+2xD+Dnl5ecjLy4OioiKAwutLq1at4OrqitGjRyMvL4+537K0tEStWrWYEha5ublcdGolwcvLCwsXLsT+/fvRt29fAMC1a9fg5uaGBw8eoH///ti8eTPzu0+dOhUvX77E1q1buWOpEvOj8+WxY8cwdepUXLlyBdWqVQNQeF6cPn06bt68iYiICKiqquLr16/IysoSyfbhYEdMTAyWLFmCly9fFnNI8ng8vH37FnPnzsXAgQOZ+cmPfr+/nX/L9VrJGDBgAADA2dmZGZNERJS6ujrrZhq/4tGjR0hJSUH79u2hoKBQ5lBiOzs7AChWrLUs2+D7dDoiQnp6OhQVFbFz507Wtpbndi1P3r17hwcPHoDH46FevXrQ0tISW8PHxwe9e/eGpaUllJWVERwcLHJTGRgYWCxKShx4PB46depUJo2ilMd+BRQ6toqSl5eH2NhYJCYmihUN9bs6xxcUFMDPzw8BAQF48+YNHj58CENDQ8ybNw/6+vpwcXERW/NHx/yLFy9YN2RasGABpk2bxkxwhGRlZWHZsmXw9PQUS+/r16/M335+fnB1dYW3t7dI06EFCxaU2L26otDX15d44y1zc3PEx8ejUaNGsLKywuzZs9GuXTtW0cZFWb58OXg8HoYNG4b8/HwAhc2Sxo0bxypyorwabwmRRI3LovvUr5DUQzQOjn8Z4cQsNTUVJ06cQGZmJnr27In69etDXl4eqqqqTE1h4brOzs6YN28eTp06xTgjOedJ2RBu2+TkZKxcuRLPnz/HgAEDMHToUCgrK6NmzZoIDQ3F6NGjISMjwzgk69Wrh48fPzLXNc4RWXmYP38+MjMz4ejoiIKCAgwYMAB16tSBoaEh3r9/D1NTUwDA69evsX79euzcuRORkZHcsVSJER6nr169YgJDOnbsCEVFRSbiTpgJlJeXBxUVFSxevBhVq1bFmTNn0K9fP6iqqnL3LxLC1NQUM2bMwJIlS7B+/XoQEQYNGgQej4e0tDT07dsXnz59woYNG5jP/IuOSICLjKxQfhUd9bscFqXhw4cP6N+/Py5evAgej4fk5GQYGhrCxcUF6urqWLFiBSvdyMjIny63tLQUW/P7dDo+nw8tLS2Ym5szNeP+BTIyMphanwKBAEBhVNuwYcOwdu3aYo6f0vDlyxcoKyuL1EcDCmtfKCsrl/pmc82aNRg9ejTk5eV/mfbOpntueexXP8Pb2xvfvn3D8uXLxf5scHAwqlatynT4nT59OjZv3gwTExOEhISU6TywYMECBAcHY8GCBRg1ahQSExNhaGiI/fv3Y9WqVaWulwgUXlh5PB7i4uLQsGFDkTSCgoICPHnyBHZ2dti/f7/Ydko62lKYBi/k+9SIou8rSyRnXFycyHth4y1hl2o2D0KqVKkCHo8HW1tbppEN2zqRJZGZmSlSO5XNOeVHSPLBlyRqXH6/T5VEZS2twMHxpyGcWCckJKB3795o1aoVdHV1sWDBAibC39raGunp6Th06BBznczNzYWdnR0GDx6MkSNHVuR/4a9A+DvEx8ejW7du6NOnD9q0aYPu3bsz5/v9+/dj6dKl6NChg8gDvuHDh4OImG7a/1INtMqK8PcU/rtw4UIsXboUO3bsQK9evZCSkoI5c+YgKioK3759g56eHt69e4dDhw4xDkqOyofw90xMTISjoyP09fVRtWpVrF+/HvLy8nj37h0aNGiAPn36YPPmzcznXr9+DTs7O6xatarUGSoc4lE0QnLSpEno0aMH7Ozs8P79e8TGxkJGRgYFBQXF5tX/Epwz8i9GEhFxQoYNG4a3b99i69atMDY2RlxcHAwNDXH27Fm4ubnh7t27ErS8fHj+/Dm8vLwQGBhYJh1JbtfyZMyYMTh//jzWrVuHtm3bAgCuXLkCV1dXdOzYERs3bqww2wwMDBAVFQVNTc2fFsHm8Xh4/Pjxb7SMHY8ePULLli1Z1cysX78+Nm7cCBsbG1y/fh0dOnSAv78/Tpw4AWlp6TKl/xsZGWHTpk3o0KEDVFRUmOP2/v37aN26NT59+lRqLWEn+vnz58Pd3V2knoysrCz09fXRp08fVtEPfD4fb968KXYsXbhwAQMGDMC7d+/E0vuVM7ooknZMS5qTJ09i2bJliIiIYPX5+Ph4REREIDIyEpcvXwafz4elpSWsra2ZWmuVifJ48CWJGpd/0z7FwfEn8PDhQ7Rt2xajR4/G/PnzRaKapaWl8eXLF7Rs2RLKyspwc3ND9erVcfbsWWzevBm3bt2CkZFRBf8P/g6ePn0KS0tL9O/fH8uWLWPGhQ6Qb9++YdWqVTh8+DBUVVXRqVMnPHjwAKGhobhx40axes0cv59Pnz4xwRhF0+WXL1+O6dOnQ15eHtu2bcOgQYPw/v17vHz5EpGRkWjYsCHq1avHNK/kqHwIH4ImJSXBwsIC48aNw/Tp05kIR6Gj6+zZs+jTpw/69++PKVOmQFlZGYGBgQgMDMSNGze437gciYmJwdKlS/Hs2TM8ffoUampqiIuLg4yMzD9ZI7IYxFGhPHr0iCZOnEgdOnQgW1tbmjRpEj169KhMmt++faMRI0aQlJQU8Xg84vF4JC0tTc7OzpSRkcFKU0dHh2JjY4mISFlZmVJSUoiI6PHjx6SkpFQmez99+kTLly8nFxcXGjlyJK1cuZI+f/5cJs2SiI2NJT6fz/rzkt6uubm55OTkxGxLSaOpqUkXL14sNn7hwgWqWrVquXxnZeJ37VdERDt27CBdXV1Wn1VQUKDU1FQiIpo+fToNHTqUiIgSExPL/DvJy8vT06dPiUj0uL179y7r4zYoKIiys7PLZJcQdXV10tDQID6fz/wtfKmqqhKfz6fx48ez1s/LyyNvb2969uyZROytCB4+fEiKiooS0YqKiiInJyeSlpZmfS68cOECLV++nK5cuUJERAEBAVSrVi2qWrUqjRw5kjIzM8tk49ChQ6lz5870/PlzkX32zJkzZGJiwkpz0qRJVLNmTdq7dy89e/aMnj9/TiEhIVSzZk2aPHlymezl4OCQPDk5OeTi4kKDBw8WOacIBAIiIsrPzycioq9fv1KXLl3I2NiY9PT0yNTUlO7cuVMhNv+tLF++nKytrenDhw/FlhUUFBARUXp6Oh09epR69OhBFhYW1Lt3b4qPj//dpnKUwLt378jc3Jzmz58vMr5o0SKqUqUKhYeH04wZM0hGRoZCQkIqyEqOspCenk49evSgkSNHMudGov+dL4WcO3eOdHV1qWbNmmRoaEj6+voUHR39u839KxCe+0pLTEwM2drakpWVFeXm5hJR4RyFg+gfd8VWLGfOnEHPnj3x33//oW3btiAiXLt2DQ0bNsTx48fRsWNHVrpTp05FZGQkjh8/Xiwizt3dnVVEXEZGRokpeO/fvy/WVVccSmosIExLrGyNBSS9XWVkZHD48GHMmzevPMxFZmZmiUWItbW1S5WWWBFQCZ3G2FBe+9X3qbJEhLS0NERFRbH+HZWVlfHhwwfUrl2biTQGAHl5+VJ3ev4RDRs2xOXLl4uleh84cIB1yo2NjQ3evXvHdD6/desW9uzZAxMTE4wePVosLX9/fxARnJ2dMX/+fJGak8Joy9atW7OyEwCkpaWxfPnyP6K78fc1CYX7lre3N+rWrctKMyYmBhEREYiIiMDly5eRnp6Opk2bYvLkyaxScrZs2YJx48ZBX18fc+bMgZeXF3x9fTF06FDw+Xzs2rULmpqaZeq4efbsWZw5c4bZv4TUrVtXrMZPRZF0jUugeHduExMTODs7s66bysHB8T94PB5u3LiBQYMGQUFBQWS8KCoqKggLC0NaWhoyMzOhoaGBKlWq/G5z/2ouXboEVVXVErer8PdQVlZGz5490bNnT+Tl5QFAscaBHBVDdnY2WrZsif3790NRURHTpk3D8uXLsXTpUuzduxc2NjawsbFh6ijn5uZi2LBhFW02hxhkZmbi7t27GDx4sEi6r/D4FEYx29ra4t69e0hISIBAIEDdunVRvXr1ijL7j6Voo5nz588jNTUVVapUYSKJqYSyQv/99x8CAgJgYGDwz3bN/iEV5wfl+O+//2jGjBnFxmfMmEGmpqasdcsjIq5r1640d+5cIiqMsHr8+DEVFBRQv379qE+fPqxttbCwICcnJ5GnA3l5eTR8+HBq164da92SKGtkZHlsVycnJ1qxYgVrm36GjY0N9evXj7KyspixzMxM6tevH3Xo0KFcvpMtW7dupYYNG5KsrCzJyspSw4YNacuWLaz1ymu/Gj58ODk5OTEvZ2dnmjFjBp05c4a15uDBg6lZs2bk4uJCioqK9P79eyIiOnr0KDVs2JC1LhHRsWPHSE1NjRYvXkyKioq0bNkyGjlyJMnKytLZs2dZaVpYWNCOHTuIiCgtLY1UVFSodevWpKmpWezJe2mJiIgotyeE9vb2tH37donr5uTk0PPnzyk1NVXkxRYej0d8Pl/kxePxqHbt2nTt2jVWmlJSUmRmZkbu7u50/Phx+vLlC2v7iIgaNmxIa9asISKiU6dOkbS0NAUFBTHL9+/fT3Xq1CnTdygrK9PDhw+Zv4WRkbdu3aIqVaqIrZefn08RERH04cMHysjIoPj4eIqLi2OdJUBEdPv2bapSpQrVqFGDevXqRQ4ODlSzZk3S1NTkogw4OMqIQCCgp0+fkra2Nh04cICICs+3JbFy5Up68eLF7zTvn6N3797UsWPHYlFWRXFxcWHuCzgqH6mpqTRz5kwyNjYmW1tb0tLSogsXLhRbb+zYsaSjo0Nfv36tACs52HL58mWSk5Nj7pd+dD998ODB32nWX4+HhwcZGBhQy5YtqXPnzlSjRg26evXqLz8nblTl3w7njKxA5OTkmElXUR48eEBycnKsdRUUFOjevXvFxhMTE1mn+929e5e0tLTIzs6OZGVlqW/fvmRsbEw6OjplSiuXl5enpKSkEr9PQUGBtW5JlNUZWR7b1cfHh9TV1alPnz7k5+dHq1evFnmVhYSEBKpRowZpamqSjY0NdejQgTQ1NalGjRqUmJhYJm1JMnfuXFJSUqKZM2fS0aNH6ejRozRz5kxSVlamOXPmsNL8nftVWfn06RNNmDCBevbsSadOnWLGPT09ycfHp8z6p0+fpvbt25OSkhIpKChQ27Zty+Q8VVdXp/v37xMR0erVq6lNmzZEVJhGa2BgwErz5MmTdPr06RJtDwsLY20rUWEacbVq1cjd3Z327NnD7GPCl7g8fPiQLCwsSnQcluX8EhERIfK6dOkSJSUllclJW1bn4/coKCgwaf9ERDIyMiLnxNTUVJKVlS3Td5THgy85OTl6/Phxmewqyu98iMbB8a/SokULsrGxYd5/P4G7du0aWVlZiZyTOCSPq6sraWpqMqWaiETTP1+/fk2DBw+miIiIijCPo5QIHZI6Ojrk7OzMjBdN6yUievPmze82jaOMvH79mjQ0NGjmzJnM2PcPD/bt20eWlpYSvy/8VwkKCiIdHR26fv06ERH5+/sTj8ejffv2VbBlfx5cfGgFoqWlhdjY2GIpeLGxscW6yopD69at4eXlhR07djBdB7OysjB//nzWKY8mJiaIj4/Hxo0bISUlhYyMDPTu3RsTJkyArq4ua1tVVVXx7NmzYl1Onz9/DhUVFbG0ftVt9vPnz+KaJ0J5bNetW7dCXV0d0dHRiI6OFlnG4/FYdZIW0qhRIyQnJ2PXrl24f/8+iAgDBw6Eo6OjSNpTRbNx40Zs2bIFgwYNYsZ69uyJJk2aYNKkSfDx8RFbU5L7VVEMDQ1x+/ZtaGpqiox//vwZzZo1Y9VsR11dHevWrSs2LmwYU1Y6d+6Mzp07S0QLKOzyLCzNcP78efTs2RMA0KBBA6SlpbHSnDlzZonpskSEmTNnokuXLqztHTduHABg5cqVxZax6Xzs5OQEaWlpnDhxArq6uhLrEMrj8dCmTZtiaRv5+fm4dOkS2rdvL7amsIB5dHQ0k05sbGzMukxBdna2yLlDTk5OpEyHnJwckwbNlmXLlsHKygpRUVHIzc3F9OnTcffuXXz8+BFXr15lpdm4cWM8fvz4p82yxCEqKgpbtmwR+a2kpaUxffp0mJmZSeQ7ODj+ZQQCAXr27InVq1fDy8sLc+fOLZb2e/r0aSgrK0NdXb1ijPzLof9PNZwxYwZOnDiBsWPHYu/evahVqxb4fD6zfMOGDUhOTmZdToTj91C7dm3mfujIkSNYtmwZPDw8ICUlJdLNtyzzT46KQUFBAW3btsXhw4fRunVr9OzZs9i9qbCBJZsmkxzFuXfvHoYMGYJWrVrhyJEjmDt3LjZt2oT+/fvj27dv+Pr1K5cCX0o4Z2QFMmrUKIwePRqPHz9GmzZtwOPxcOXKFSxZsgTu7u6sdVevXg07OzvUrFkTTZs2BY/HQ2xsLOTl5XHmzBnWutWqVZOYg0TIgAED4OLiguXLl4tsAw8PDxHnVGn4Va0uNTW1MtVBKY/t+uTJE9b2lAYFBQWMGjWqXL+jrBQUFJQ4gW/evDlrx4Yk96uiPH36tETnVU5ODl6+fFlqnfj4eDRq1Ah8Ph/x8fE/XbdJkyZi21meNGzYEAEBAejWrRvOnTuHhQsXAgBevXpVzElbWpKTk2FiYlJsvEGDBnj06FGZ7BUIBGX6/PfExsYiOjq6mKO7rFhbWyMtLa3YRODLly+wtrYW22kKAG/fvsXAgQMREREBdXV1EBGjt3fv3mLdy38Fj8dDeno65OXlmYmo8KYLKF73kg3l8eDL19cX06ZNw8KFC9G8eXMoKSmJLBc6bUtLeT3s4OD4VylagwsA+Hw+Jk6ciMuXL2Pr1q349OkTfHx8oKCggKdPn2L9+vXYuXMnIiIiuDqtEkZ4bhc6M6pVq4YVK1Zg/PjxsLe3h6urK2xtbXH37l2cOnUKwcHBuHTpEjfx/gMo6pDctm0bpKSkMHXqVJE6gxx/HqqqqvDx8YGVlRU8PT3x9etXDBkyBADw5s0bLF++HDt37sSFCxeYYBqO0vP99QkAvn37hurVq+PEiRMYOnQoli1bhlGjRkEgEODw4cN4//49xo0bx23v0lCBUZn/PAKBgFauXEk1atRgujPXqFGD/P39f1qbpTRkZmbS5s2baerUqeTm5kZbtmxh1eU0Li6uVC+25OTkkKurK8nKyjLpjnJycjRlyhSJdeyVJJLaruVJVFQUWVlZlRiK//nzZ7KyshJJt6loJk6cSG5ubsXG3d3dWXdSlvR+JUzp5fF4tGPHDpE039DQUJowYQLVq1ev1Ho8Ho9JhRGm9wrPAUXflyXtt6j2j15suHjxIqmrqxOfz6cRI0Yw47NmzaJevXqx0tTR0aHw8PBi4+fOnSMtLS1WmuWFmZkZXb58WeK6PB6P3r59W2z8wYMHpKKiwkqzf//+1Lx5c5FU6rt375KZmRkNHDiQlY0lpaZLKlW9vPj+2GJrb3BwMGVnZ3PduTk4JEBwcLBIbeiiadjC1NH3799T//79SUtLi7S0tMjIyIhatmxJxsbGFBMT87tN/usQbvOcnJyf3htlZ2fTpUuXyNzcnOTk5IjH45GRkRFZWVlxXbP/QFJTU2nOnDmkra3N1IHm+HMRHse3b98mAwMDqlq1KrVt25Y6duxItra2VLt2bbpz504FW/nnExUVxfy9YMECqlq1KikrK9PGjRuZ8Y8fP1KnTp3Iy8urAiz8M+ER/X/7Wo4KJT09HQAqXVQFn88Hj8cr1uW46G7DJtXxezIzM5GSkgIigpGRUYmdu/9WXrx4gWPHjuHZs2fIzc0VWVZSaumvGDx4MIyNjX/Y3dnPzw/37t3Drl27WNkraSZNmoQdO3agVq1aaNWqFQDgxo0beP78OYYNGyaSmiXu9pDUfiV8Ilb0WBAiIyMDfX19rFixAt27dy+VXmpqKmrXrg0ej/fLDsHfd8IWh6NHj4q8z8vLQ0xMDIKDgzF//ny4uLiw0i0oKMDXr1+hoaHBjD19+hSKioqsUnxGjx6NGzdu4PDhw6hTpw4A4NGjR+jTpw9atGiBrVu3srJTSEZGBiIjI0s8xkpTCqFoxF9UVBTmzp0LPz8/NG7cuFjqoLhRdsLyEkePHoWdnZ1I2nNBQQHi4+NRv359nD59WixdoDAa/Pz582jRooXI+K1bt9CpUyexS1dERkaWaj1LS0uxdL/n06dPIp2qjY2NMWLECNZdcn9ld2ntlZKSQlpaGtTV1eHh4YGAgIASu3MX/Q05ODiK8+7dOzg7O+Pdu3eYNGkSHB0dAYhGoAhTRzMzMxEdHY3Lly8jIyMDZmZmMDc35yLxyohwWycnJ2PFihX4/PkzrK2tMWbMmJ9+Ljo6Gp8+fULdunWhpqbGpcn/oTx58gQ7d+6Eo6Mjc9/F8eciPJ4fP36M48eP4/Lly5CSkkKbNm1gb28PfX39ijbxj6Po9ejatWvo1asXFi1aBGdnZwBA9+7dcenSJZw8eRK1a9dGfn4+JkyYgA8fPuD69etct+xSwjkjK4CsrCycO3cO1tbWxZyPX79+RUREBDp37iz2hCY6OhrTpk3D0aNHi02Iv3z5AgcHB/j7+6Np06al1izqKCEiNGrUCGFhYcUcJOI6TAoKCnD37l3UrVu3WP3CrKwsJCcnM2msFU15bFch4eHh6NmzJwwMDPDgwQM0atQIT58+BRGhWbNmuHDhgtiaderUweHDh3+Y3puQkAB7e3tW9Q3LA2tr61Ktx+Pxfrk9ynu/MjAwwO3bt1G1alVWn68s7NmzB/v27SvmrKwovnz5Ajs7O0RFRaFmzZoACp307dq1Q2hoaJkmOzExMejatSsyMzORkZGBKlWq4P3794zjtDTHgfChjBD6/zS2ogjHxH0wM2LECABAcHAw+vfvL7LfysrKQl9fH6NGjWK1z6moqODy5cv477//RMZjYmJgaWkpkbRqSRMZGQl7e3uoqqoy5Ruio6Px+fNnHDt2rMyOzrLA5/Px+vVrxuH+Lz9E4+AoK3FxcVi+fDmePHmCsWPHMmmFRSeAJaXHcZQd4XaNj49Hly5d0K9fP1hYWKB79+4/TCssWleQo3KTn58PaWlpZGRkFCtLUtJ6HJWfku47vx/nzpeSo+i23LFjB65fv45du3ZBRUUFXl5eGDNmDDIyMtC9e3c8efIEnz9/RoMGDSAlJYWIiAjIyMhw58xSwjkjK4DVq1fj2LFjCA8PL3G5ra0tevXqhQkTJoil+zsi4lRUVJgiuGUhKCgI69atw82bN4sdqAUFBTA3N8eUKVOYm9OKpDy3a8uWLWFnZ4cFCxYw21ZbWxuOjo6ws7NjaruIg7y8PJKSkn7YrOHJkycwMTFBVlaW2NqVnT9lvzp27Fip1xU2iJEkKSkpaNKkCTIyMkq1vqmpaakbtdy5c4eVTUSEc+fOIS4uDgoKCmjSpAmrpi3fY2VlhXr16mHjxo1QV1dHXFwcZGRkMGTIEEyePPmXja+A0kcEAuyjAufPn49p06b9dOIgLvb29vj8+TNCQkKYKKKXL1/C0dERGhoaOHz4sMS+S1I0atQIbdq0YWpGAoXH7vjx43H16lUkJiaKrbl9+3YoKyujX79+IuMHDhxAZmYmhg8fXiodPp+PN2/eiF1rk4ODo2Ti4+OxZMkSpKam/tAhCYhOuH80KecQj6dPn8LKygr9+/fH0qVLmXFu+/7ZCB0gT58+RZs2bXDo0CHWTTY5KgfCY/LatWsIDw9HdnY2GjVq9MMa+EXPn9zxXDZmzZqF7du3Y8GCBfj27RtOnDiB58+fY+rUqcwc/eLFi/j69St0dHTQsmVL8Pl8ztEvDr8vI5xDSIsWLejYsWM/XH78+HFq0aKF2LqGhoY/rd8YHx9PBgYGYusWRVlZmVJSUsqkQURkYWFBISEhP1y+b98+ateuXZm/RxKU53ZVVlamR48eERGRuro6JSYmEhFRbGws6enpsdKsWbMmnTp16ofLw8LCqGbNmqy0fzfCuoqlpbz2qxs3blBYWJjIWHBwMOnr65OWlhaNGjVKrFqURWvY/exVHvX3MjMzafLkyWLVuPT29mZeM2fOJFVVVWrVqhW5ubmRm5sbtW7dmlRVVWnmzJkSt7esqKmp0f3795m/hfUTb9y4QfXr1xdbLzU1tcSavgKBgFJTU8tmbBEiIiLo5MmT9PHjR9Yaz549I1NTU5KRkSFDQ0OqU6cOycjIULNmzejZs2cSs1WSyMvLM79XUe7fv0/y8vKsNOvVq0cXLlwoNh4RESF2rdeuXbtSr169fvri4OD4OUXrQ965c4cGDx5Mbdq0oZ07d5a4DofkWb16NVlZWZXqPqusdew5fi/Pnj0jHR0dcnZ25n67v4RDhw5RlSpVqFevXjRq1Cji8Xjk7u5OOTk5FW3aX0XR4+XJkyfUoEEDOnjwIDOWmJhIY8eOJX19fdq6dWuJGty1Szw4l20FkJyc/NOU3iZNmiA5OVls3ZcvX/605qSysjLS0tLE1i0PHjx4wNQHLIkWLVogKSnpN1r0Y8pzuyopKSEnJwcAUL16daSkpKBhw4YAgPfv37PStLW1ha+vL+zs7IotIyL4+fnB1taWlbYkUVRURGpqKhNlZGdnh+3btzMdc9+8eYPq1auLlfZaXvuVt7c3rKys0KVLFwCFqe4uLi5wcnKCsbExli1bhurVq8Pb27tUepLu8PwjNDQ0iqUXp6enQ1FRUaxIXi8vL+bvkSNHwtXVlemiXXSd58+fl1pzzZo1GD16NOTl5bFmzZqfrluauo4/QkZGhtkGOjo6ePbsGYyNjaGmpoZnz56JrWdgYFBi1+uPHz/CwMBA7DTtZcuW4du3b5g/fz6Awt+oS5cuOHv2LABAW1sb4eHhzHlBHGrVqoU7d+7g3LlzuH//PogIJiYmleL4/xHNmjVDUlIS6tevLzKelJRULN28tKSmppYYKa6npyf2PqCiolKsBAQHB0fpoP+P0vny5Qt4PB4UFBRgamqKuXPnYuHChQgICAAADBkyBHw+n0s7LEciIiIgJydXYp1n4e+UlZUFBQUFLrKqEkPfRb7l5+djw4YNGDBgAPz9/bnf7i8gJSUF7u7uWLBgASZMmICXL19iz549KCgogKysLLPe9/sCh3gUvd68evUKampq+PDhg0h99YYNG2LChAk4f/48Zs+eDQBM/X3h9ueuWeLBOSMrgPz8fLx79w61a9cucfm7d++YovjioKWlhQcPHvwwPff+/fsSqXUniRNdRkbGT+uVpaenIzMzk7X+zp07ERAQgCdPnuD69evQ09ODv78/DAwMYG9vL5ZWeW7XVq1a4erVqzAxMUG3bt3g7u6OhIQEhIaG/tSp9jPmzp2L5s2bw9zcHO7u7qhfvz54PB6SkpKwYsUKPHz4ENu3b2elLUmys7NFmsFcvXq1WOo4iVlForz2q9jYWBHn2969e2Fubo4tW7YAKHT6eHl5ldoZ+bvw9/cXec/n86GlpQVzc3OR5jPicODAAURFRRUbHzJkCMzMzBAYGFgqnVWrVsHR0RHy8vJYtWrVD9fj8XhlckaampoiKioK9erVg7W1NTw9PfH+/Xvs3LkTjRs3FlvvRzd73759+2GtrZ8REhKCGTNmMO8PHjyIS5cu4fLlyzA2NsawYcMwf/587N+/X2xtIR07dkTHjh2Z93fu3IGnpydOnDjBWlOSxMfHM3+7urpi8uTJePTokUhDq/Xr12Px4sWs9LW1tREfH1+sgHtcXBw0NTXF0lqzZg2rJk0cHP86wnPniRMn4Ofnh8zMTGRmZmLOnDkYNGgQ5s2bBx8fHwQEBIDP52Pw4MHcpK4ckZWVRXZ2NgQCAYhIpLSN8Brn6emJhg0bwsnJqYKs5Pie9+/fIy0tDR8+fICVlVWx+xFpaWm4uroyD/Y5/nw+f/6MGjVqYMKECXj69CksLCwwdOhQ5t45Pj4eTZo04RyRZUR4vZk5cyaeP38OT09PmJmZIT4+Hu/evWOCZxo1aoQWLVrg1atX2LRpE6pXr44uXbpw258tFRGO+a9jbm5Oixcv/uHyRYsWkbm5udi6Tk5OZGFhUeIygUBAFhYW5OTkJJbmf//9R6ampsxLSkqKGjZsKDJmamoqtq1NmzaljRs3/nD5+vXrqWnTpmLrEhFt2LCBqlatSj4+PqSgoMCklW/fvp2srKzE1iuP7SokJSWFSQHPyMigcePGUePGjalXr1709OlTVppERLdv36aGDRsyqb58Pp94PB41bNiQbt26xVpXkvB4PJH0oO9LALx+/VrsNOXy2q/k5ORE0lrbtm1LCxcuZN4/efKElJWVxdYVcv78eerWrRuTStutWzc6d+4ca73yREdHhwIDA4uNBwYGkra2dgVY9HNu377NpOi+ffuWunTpQioqKmRqakqxsbGl1hGmpPP5fBozZgzz3s3NjVxdXcnc3JzatGkjtn3q6upM6jhR4flmyJAhzPvr16+zKqtw9uxZmjZtGs2aNYs5rpKSksje3p74fD517txZbM3yQnieKq+yBR4eHqSnp0cXLlyg/Px8ys/Pp/DwcNLT0yN3d/dS6/D5fLFLR3BwcPyPU6dOkZKSEi1btoxSUlLI2dmZ5OXl6eLFi0REFBcXR0OHDqWGDRvSvn37KtbYvwhh2mDR9MGZM2eSkpISc/35PrUwLS2NBg0aROfPn/99hnL8lLt375K1tTX17t2bJk2aJFZ5II4/l0uXLpGJiQmFh4eTvr4+jR49mvLz84mIKCoqivr06UMPHz6sYCv/XIqmZt+8eZMaN25MN2/eJCKiLVu2kJqaGi1btozS0tKIiCg9PZ369u1LGzZsoHbt2tG0adMqxO6/BS4ysgJwdnbG1KlT0bBhQ3Tv3l1k2fHjx+Hj44OVK1eKrVseEXEODg4i78WNKvwRgwcPxty5c9GmTZtiXZ/j4uLg6emJ6dOns9Jeu3YttmzZAgcHB5FIGjMzM0ybNk1svfKMNCzaCEhRUREbNmxgpfM9ZmZmSExMRGxsLJKTk0FEqFevHus0xz+F8tqvdHR08OTJE9SqVQu5ubm4c+cOk1YLFEZcysjIsLJ53bp1cHNzQ9++fTF58mQAhZFgXbt2xcqVKzFx4kRWukI+f/6MW7du4e3bt8XSw4cNGya23pQpUzBu3DhER0eLRK4FBgbC09NTbL2bN2/i2LFjyM/PR4cOHdCpUyexNX6GsCMzUBjlHBYWxkonJiYGQGF0T0JCgkhqjKysLJo2bcrq/JKXlwc5OTnm/fXr15n9ACgs3yBuyYbg4GCMGDECVapUwcePH7F161asXLkS48ePR58+fRAXF4dGjRqJbauQjIwMLF68GOHh4SXuV6XpUF6UJ0+esLalNPj4+CA1NRUdOnRgCooLBAIMGzYMfn5+pdYhrt8fBwcriAgCgQDBwcGYOHEipk2bhrS0NFy+fBnDhw+HlZUVgMIyRVOmTEFAQABatmxZsUb/BQjTDoURO+np6VBTUwMATJo0CaGhoXBwcMDFixeZJmdCAgIC8OjRIzRo0OC3281RnLt378LCwgLjx4/HkCFDYGxsXNEmcZQD9P8R5ImJiZCSkoKRkRH09fWhra0NBwcHdO/eHZs2bWLW37dvHz59+sQ624njf5HgK1asYBo/tWjRAkBhaarPnz9j2bJluHjxIrS1tfHgwQNkZWUxmWK3b9/mSoqUhQp1hf7DODo6Eo/HI2NjY3JwcKBevXpRgwYNiM/n08CBA1nr/gkRcUREubm5ZGVlRdLS0mRnZ0dTpkwhNzc3srOzI2lpabK0tKTc3FxW2vLy8kxUYdFou4cPH7JugFBe29XAwIDev39fbPzTp09lajaUm5tLBgYGdPfuXdYa5Q2fz6e3b98y71VUVOjx48fMezaRkeW1X40ePZpat25Nly5doqlTp5KmpqZI0ehdu3aRmZmZ2LpERNWrV6e1a9cWG1+3bh3p6uqy0hRy7NgxUlFRIT6fT2pqaqSurs68NDQ0WOvu27eP2rRpQxoaGqShoUFt2rRhFcUSGhpKUlJSpKSkRGpqasTn82nVqlWs7SoNOTk5lJ6ezvrzTk5O9OXLF4nZ07RpU9q+fTsRFTbH4fF4Isft1atXqUaNGmJrLlq0iIgKfysej0fNmjVjmmWVlYEDB5Kuri5Nnz6dVq1aRf7+/iKvysqDBw9o//79dPz4cVaR5xEREZSXl1cOlnFw/P3k5uZS69atKSIigr5+/UrVq1en0aNHM8t37dpFycnJRERcxJcEEEY6PnnyhBYuXEgWFhakp6dHgwcPpr179xIR0dGjR0lXV5cMDAxox44dlJCQQCdOnKBx48aRqqqqWNkDHOXH+/fvydzcnCZNmiQyzjWn+bsQ/p6HDx+m6tWr06pVq5hsjICAANLU1KSxY8fS5cuXKTo6mqZOnUrq6uoUHx9fkWb/NYwbN465X/5+bn748GGaPn06de3alSZOnMhco/r27UsTJ07kmtaUAc4ZWYHs27eP7O3tycTEhIyNjcne3l5iaSkxMTG0f/9+2rdvH8XExEhEU9Lk5ubSkiVLqGnTpqSoqEgKCgrUtGlTWrJkSZm6gxkbG9ORI0eISNQZuXr1amrWrFmZbJb0dv0+VVnI69evSVZWtkza1atXF0n/rGzweDzGKaahoUE8Ho/U1NSY9+rq6qzSMstjv3r79i1ZWFgQj8cjFRUVCg0NFVluY2NDs2fPZqWtrKzMTMCK8vDhQ1JSUmKlKaRu3bo0efJkysjIKJNOeWFmZkYuLi6Mg2fhwoWkqakpMf3AwECaOHEi7dq1i4gK09JkZWWJz+eTra1tiQ8CfjcBAQGkpKREzs7OZGJiUizVe+HChdS9e3exNJWVlRnHfkFBAUlLS1NERITEbFZTU6MrV65ITO97duzYQW3atCFdXV3Gabhq1SrmvM7BwfHnUNSh1atXL+rRowfp6enR+PHjmWtyRkYG2dvb08qVK0kgEHBOljIinBjHx8dT3bp1adCgQTR69Gjy8fEhAwMDqlatGi1YsICIiC5evEg2NjYkJSVFfD6f6tWrR9bW1pyDoxIRFxdHDRs2pKtXr5bK6cEdP38uJ0+eJCUlJdq4cSO9e/dOZNnmzZupbdu2JCsrS//99x81b96ce2DAkh8dI15eXsTj8Wjt2rU/nTulpaXR7NmzqUqVKpU68OdPgEfE5R1x/F1s374d8+bNw4oVK+Di4oKtW7ciJSUFixYtwtatWzFw4MCKNhHHjh0DUJgGHxwczKTNAEBBQQHCw8Nx7tw5PHjwgPV3LF68GPfv38fWrVuZ1MTKRHBwcKnWGz58eDlbUnq+fPkCZWVlkULvQGEnZWVlZZHU3dLi6OiI//77Dx4eHiLjy5cvR3R0NEJCQljbq6SkhISEBJFyAJUJVVVVprkMAOTk5EBJSQmvX78uc7MtX19f+Pr6ok2bNoiJiUH//v1x5MgRTJkyBXw+H2vWrEH37t2xceNGsXRtbGx+uvzChQti27pt2zacOHEC1apVg5eXF6pVq8YsGz9+PDp27IhevXqVWo/P5+P169dMoxUVFRXExcVJbD8wMDBAWFhYuaSJbdy4EZ6enpgyZQp8fX2RmJgIQ0NDBAUFITg4GBcvXiyVztSpU7Fw4UIoKSlh6tSpP12XTVkUDg6OX/PhwwfUr18fU6ZMwdy5c3HgwAHMmjULSkpKiIuLY9abM2cO9u3bh7Nnz1ba69WfgjBdMC4ujknrnTVrFtTV1QEADx8+hI+PD06dOoXZs2fDzc0NAHD79m1kZGTAwMAA6urqIvelHBXLrl274OLigrdv3/7wd8nKysKlS5fQuXPn32wdh6TIzc3F4MGDUbNmTfj7+yMrKwtpaWkICQmBvr4+evbsCXl5eSQmJqJKlSpQVVXl0rNZUDSl+vnz58jJyYGWlhZzbE2ZMgUbN27Exo0bMWjQICgoKIh87vPnz5g+fTrCw8Nx6NChv74EWnlT+TwUHBxlZMSIEcjPz8f06dORmZmJwYMHo0aNGli9enWlcEQC/6vFyePxijnbZGRkoK+vjxUrVpTpO27evInw8HCcPXsWjRs3hpKSksjy0NDQMumXlcrkZCwtP7oJrFKlilg6a9asYf42NjaGr68vIiIi0Lp1awCFNRivXr0Kd3d39sYC6Ny5M6Kioso8udPQ0Ch1l7iPHz+WWvfbt2/MBAkA5OTkoKCggK9fv5bZGRkUFIRt27Zh0KBBiIqKgrm5Ofbt24e+ffsCKOyGN3bsWLF1mzZtKvI+Ly8PsbGxSExMZL1Pu7i4wMXFpcRlbOvInjlzhtlfBQIBwsPDkZiYKLJOz549WWkvXLgQnp6eCA4OhqKiIiuNHyGpmr8xMTHIy8tj/ubg4Pj9KCoqol+/frh//z6ICJ06dUJMTAyOHz8OKysrNGvWDM+fP0d4eDjCw8M5R6QE4PP5ePToEVq1aoVp06Zh4cKFKCgoAADk5+ejXr168PLywrt37xAYGIjOnTvDxMSEqZHGUXmg/68fqKysDB6Ph7t376JNmzbMeFH27NmDa9euoVOnTlxX3z8I4W+ZlJQEdXV1ZGRkQEpKCvHx8diyZQuSkpJw9+5dVK9eHVeuXMGqVatgampa0Wb/sRAR44icO3cuwsLCcP/+fbRu3RpmZmZYsmQJ/P39QUQYP348eDweBgwYAEVFReZz6urq8Pb2hqenJ2rWrFmR/52/Ai4ykuOv5v379xAIBEyEUGXDwMAAt2/fLrPjpSRGjBjx0+Vsm+5wlB0DA4NSrcfj8cRuBiKMugWAd+/eYcGCBRgxYgQaN25crMlOaZ1RpY1iBcRzMvP5/GKRwYMGDYK/vz90dHTEtrMocnJyePToEWrVqsW8j4+PR/369QEAL1++hIGBAXJzc8XWLglvb298+/YNy5cvl4heWShNEW0ej8dMUMXF1NQUKSkpICLo6+sX26/u3LnDShcAFBQUcP/+fejp6YlEdCYnJ6NJkybIyspirc3BwfH7uXDhAuzs7HDkyBF07doVX79+xcWLFxESEoKMjAwYGRlhzJgxXKMUCSEQCDB37lxs3boVc+fOhaurK4DCrBspKSnG+XH58mVYWVnh0KFDxZpVclQs+fn5IhlNGRkZqF+/PszMzHDkyBEA//s9hUyaNAlqampYuHAh54z8wzh8+DAcHR0RFxeH0NBQLF26FPn5+ejUqRMcHBzg6OgINzc3pKSkiNzjc7DHz88PK1aswLZt28Dn83Hjxg0cPHgQFhYWCAwMBABMmzYNK1euxPHjx9GtWzfmsyU9DOBgD+eM5BCbFy9eoHr16lzXKA6OSkhpj8uyOKMkRXk6zX6VqvzmzRtUr15dYtvg0aNHaNmypViRoX8qRTvJl4SXlxdrbRMTEyxatAj29vYiv9maNWsQFBQklqPT2dn5l+vweDxs27aNtb0cHByivHz5EkpKSiJR76NHj0Zqaip27Ngh8qCJo3x49eoVli5dihs3bsDBwQEzZ84EUOio5PF44PF4yMzMhL6+Pry9vTF+/PgKtphDyIMHD+Dv74/Hjx+jS5cu6NGjB+rUqYNNmzZh0qRJ6N27NwIDA5mshIyMDPj5+WHXrl04f/486tatW8H/Aw5xyMzMxNKlS6GiosJkQ92+fRvZ2dlo164d43R2dXXFmzdvEBwcDDk5Oc4ZVga+fPmCfv36oVevXhg3bhwzFhoaisWLF8PNzY3JnFq3bh3Gjh1bKcud/S1wW5ZDbExMTBAbG1tp02lMTU1LPEnzeDzIy8vDyMgITk5OsLa2rgDrCrl58yY+fvyILl26MGM7duyAl5cXMjIy4ODggLVr10JOTq5M35Ofn4+IiAikpKRg8ODBUFFRwatXr6CqqgplZeWy/jc4KiECgUDiml+/foWqqirz988QrlcaysPWoty7dw+vX78GUPgk8/79+/j27RuAwqhpSXL9+nXIy8tLVLOyUhZn46/w8PDAhAkTkJ2dDSLCrVu3EBISAj8/P7GdhkFBQdDT04OpqSl+x3NXW1tbPH78WOxoZg6OPx1hpMidO3dgYWGBnj17on379oyTq1evXnBzc8Pz58+ho6OD3NxcpsYyF2UieapXr46ZM2fC19cXR44cAY/Hw4wZM8Dn8xnnRkxMDKpXr45WrVpVtLkc/09cXBxsbGzQpUsXqKmpYcWKFZCSkmKckB8/foSPjw9atmyJDh06oKCgAKmpqbh16xZOnz7NOSL/MKKiotC5c2cYGhrC19eXGS9aMuHx48dMzeyrV6/+M/eZ5YmcnByeP3+Op0+fMmNqamro168fDh06hNjYWGZ84sSJAIpHK3NIDm6r/kUkJyfD09MTmzZtKuYQ+PLlC8aNGwcfH58yOxHLY1KXm5uLJ0+eoE6dOmU+2O3s7LBx40Y0btwYLVu2BBEhKioK8fHxcHJywr1792Bra4vQ0FDY29uLpX358mVs2rQJKSkpOHjwIGrUqIGdO3fCwMAAFhYWpdbx9vaGlZUV44xMSEiAi4sLnJycYGxsjGXLlqF69erw9vYWy76ipKamws7ODs+ePUNOTg46duwIFRUVLF26FNnZ2QgICGCtzSE5fhW9JUwXEAdJO7s1NDSQlpYGbW1tqKurlzhxFE4oKzrasigdOnQQOV91794dQOGDCbYT4N69e4u8JyKkpaUhKioK8+bNK5vBHD+s+bt27Vq0a9dOLK2xY8di7969ePz4MZydnTFkyBCx67uKg4ODAz58+FBu+hwclRUej4dDhw7h9u3bWLp0KVJTU5mGNUOHDsXw4cPRqFEjeHp6IiwsTKTZG+eILB+qVauGOXPmwNfXF4cPHwYAzJgxg0ntPXToEHR0dKCvr1+BVnIIiYuLQ9u2bTF58mTGMTVmzBjcvHkTTk5OUFNTw6xZs9C2bVv4+PjgypUrkJWVhYWFBVasWME0AuT4c9DS0oKFhQWOHz+O9PR0AKLp91FRUfDw8MC7d+8QGRmJRo0aVaS5fyRFm9UI4fF4aNu2LR48eIDHjx8zfhFlZWXUr18fSUlJxZyPnCOy/ODStH8z309kf4a4DUZGjx4NdXV1LF26tMTlM2bMwNevX8XuHvs9kuzMmpmZiUmTJjE16R4+fAhDQ0O4uroyT3bFZdSoUahdu3Yxx4CPjw9SU1OxZcsWeHl54eTJk4iKiiq17qFDhzB06FA4Ojpi586duHfvHgwNDbFhwwacOHECYWFhpdbS1dXF8ePHYWZmBqCwk2RkZCSuXLkCADhw4AC8vLxw7969Umt+j4ODA1RUVLBt2zZoamoyv1lkZCRGjhyJ5ORk1tqVnYKCAgQFBSE8PBxv374tFoEnbsfj6OhoTJs2DUePHi3R0e/g4AB/f/9izU1Kw/ddkvPy8pCYmIjPnz/DxsaGVaMhOzs7WFtbY8aMGQAKnd3NmjUTcXaPGTOm1M7uyMhI1KhRA0ZGRoiMjPzpupaWlmLbWx6kpqaWaj09PT2xdL+vxcrn86GlpQUbGxt06tRJLK0/lYKCAqxatQr79+/Hs2fPitXdlFSqurDmb0FBAfz8/LB161axa0bm5OQgNDQUgYGBuHbtGrp16wYXFxeuyD8HhwQQPtR58eIF2rRpgzlz5mDMmDHIz8/H69evMXPmTCQnJ+PLly9o3749Dhw4gEOHDsHGxqaiTf9neP36NXx9fXH79m306tULM2bMgI+PD1auXIlLly5xDo5KwOvXr1GzZk2MHj0aGzZsYBwho0aNQkxMDD59+gRtbW2MGjWKeYCdmZkJOTk58Pl87lr2B/Ps2TOMGzcOt2/fxuXLl1G/fn0RB1pkZCQMDAxQu3btCrb0z6PodoyPj0d6ejpq1KgBfX19REdHo1OnThg4cCAmTJgAExMTZGRkoGvXrmjSpAnWrl1bwdb/O3DOyN/Mr5qKFEXcBiMNGjTAzp07f9gRLzo6GoMHD8aDBw/E0v2eRYsWYdy4cSL1gNgyefJkXL16Ff7+/rCzs0N8fDwMDQ1x7NgxeHl5seqCqqamhujoaBgZGYmMP3r0CM2bN8eXL19w//59tGjRgnkSVRpMTU3h5uaGYcOGiThkY2NjYWdnx6SDlgZ5eXkkJyczzTUsLCxgZ2eHuXPnAgCePn2Kxo0bi2Xf91StWhVXr15F/fr1Rex9+vQpTExMkJmZyVpbkhARDh48iIsXL5boOGTjjJs4cSKCgoLQrVs36OrqFrtRW7VqlVh6gwcPhrGx8Q8j3/z8/HDv3j3s2rVLbFtLQiAQYPz48TA0NMT06dPF/nx5OLv5fD5q1KgBa2tr5vWnRVSEhISgZ8+exTrLVxTl+XCqPPH09MTWrVsxdepUzJs3D3PmzMHTp09x5MgReHp6Mg0TxOHz58+YMGECzp49CxkZGcycORMTJ07E/PnzsXz5cpiYmGDq1KkYNGgQa7tTU1MRFBSEHTt2IC8vD/fu3ZNouYrQ0FB4e3sjPj5eYpocHJWd8+fPMx1f/f39IS8vz0T35OXl4fnz59i4cSN2796NnJwcxMbGMvc+HL8HoUMyLi4OOTk5iI+Px9WrV9GsWbOKNu2fRujMT05OhpOTE969e4eLFy+iRo0aWLx4MRYsWICVK1ciOzsb169fx/HjxxEaGgo7O7uKNp1DTIp2zX7//j3y8/Nhbm4ORUVFvH37Fo6OjkhMTMSlS5dQt25dLi24jBTNfpozZw72798PPp+PzMxM9OzZE4sXL2aijmvUqAEejwc+n4/09HTcuXMHMjIyXAmR3wS3l/9myrODcWpq6k+7RletWhXPnz8v8/fMmjWrzBpCjhw5gn379qFVq1YiB7yJiQlSUlJYacrLy+PatWvFnJHXrl1jam0IBAKx6zE+ePAA7du3LzauqqqKz58/i6Wlo6ODJ0+eoFatWsjNzcWdO3dEmkKkp6cX61ArLsKIou958eIFVFRUyqQtSSZPnozNmzfD2toaOjo6Ejnx7927F/v370fXrl0lYGFh2vPPonR79OiBrVu3SuS7gELHn5ubG6ysrFg5Iz99+iTSJCAyMlLk5rVFixZinwsiIyMRGRmJiIgITJw4EdnZ2ahduzZsbGwY52SNGjXEtvV3MmbMGJibm0skqjs6OhpJSUng8XgwMTGBqamp2BpFu4j/SezevRtbtmxBt27dMH/+fAwaNAh16tRBkyZNcOPGDVbOyNmzZ+PSpUsYPnw4Tp8+DTc3N5w+fRrZ2dkICwuTSMStsHEDEbGuV7plyxbGYTp58mSYm5vjwoULcHd3x4MHDzB06NAy28nB8Sdx4sQJrFmzBnXr1sW3b98gLy8PKSkpCAQCyMjIwNDQEMuWLcOQIUOgra0NXV3dijb5n0OYsj179mxcvnwZ169fx3///VfRZv3zZGZmQklJCXXq1MGOHTvg7OwMKysr9OnTB9u3bxdxPLZt2xZnz55FUlIS54z8wxA6tQ4dOoTx48dDS0sL9+7dQ4cOHTBs2DAMHToUu3fvxpAhQ2BjY4Nz586hQYMGFW32H41wLunv74/AwEDs3bsXlpaWGDNmDHbv3o1BgwbB1tYWYWFhTCm32rVrw9XVFdLS0pwz+HdCHBVKXl4enTt3jgICAujr169ERPTy5UtKT08XW0tHR4fCw8N/uPz8+fOko6PD2tbyQEFBgVJSUoiISFlZmfk7NjaWVFVVWWkuXLiQFBQUyNXVlXbu3Em7du0iV1dXUlRUJB8fHyIiWrlyJdna2oqla2hoSOfOnStma3BwMBkbG4ulNXr0aGrdujVdunSJpk6dSpqampSTk8Ms37VrF5mZmYml+T39+/enUaNGMfY+fvyY0tPTycbGhpycnMqkLUk0NDTo5MmTEtXU1dWlBw8eSExPTk6OHj9+/MPljx8/Jnl5eYl9HxHRyZMnqWrVqqw+W7t2bYqMjCQiopycHFJQUKDz588zy+Pj40lDQ4O1bbm5uRQZGUnz588na2trUlBQID6fT/Xq1WOt+Tsoetyy5c2bN2RtbU08Ho80NDRIXV2deDwe2djY0Nu3byVkaeVGUVGRUlNTiYioWrVqFB0dTUREKSkprM/btWvXZs6vKSkpxOPxaPLkyWW2NTs7m/bs2UO2trYkLy9Pffv2pZMnT1JBQYHYWsuWLSMZGRlq3rw5KSoqkqKiIvn6+pKmpiZ5e3vTu3fvymwvB8efhkAgoAULFhCPx6PAwMBiy9kcaxzlw9u3b+n169cVbQYHFd5LVK1alfbu3UtEhcfJo0ePqEuXLsTj8Wj37t1ERMzc4MuXL2RmZkZbt26tMJs52HP79m1SV1enzZs3U1paGiUmJlLv3r3JysqK9uzZQ0REz58/p1atWlH9+vUpNze3gi3+sxFed/r160crVqwgIqIjR46Qmpoabdy4kYiIsrKyKD8/v9hnSxrjKD84Z2QF8vTpU2rQoAEpKiqSlJQUM0mePHkyjRkzRmy9fv36kYODww+X9+zZk/r27cva3vKgffv2tGbNGiL6n8OMiGjChAnUuXNn1rq7du2iVq1akYaGBmloaFCrVq2YCzsRUWZmJmVlZYmluWTJEjIxMaEbN26QiooKXb58mXbt2kVaWlq0du1asbTevn1LFhYWxOPxSEVFhUJDQ0WW29jY0OzZs8XS/J6XL19SvXr1yNjYmKSlpalVq1akqalJ9evXpzdv3pRJW5Lo6+tTUlKSRDWXL19O48ePJ4FAIBG9mjVr0qlTp364PCwsjGrWrMlK283NTeQ1ZcoUGjBgACkrK9OECRNYaf4OZzdR4XF09uxZcnd3J1VVVeLz+ax0hg8fzjhPyxNJOCP79+9PzZs3p3v37jFjd+/eJTMzMxo4cGBZTfwtDBs2jKytrVl/vl69enTjxg0iIrKwsKBFixYREdHevXtJS0uLlaa0tDS9fPmSea+goEAJCQmsbSQiGjduHGloaFDTpk3J39+f3r9/Xya9Bg0a0LZt24iI6OLFi8Tj8ahDhw706dOnMulycPwpCK+pAoGgmJPRzc2N5OTk6MCBAxVhGgfHH8W7d+/I2dmZZGVl6fDhw0RU6EC5f/8+2drakr6+Pj179oxZf/bs2aSnp0dPnz6tIIs5SoPwvCg8V+bl5RERUUBAADVv3pxycnKYZcnJydSjRw/q2rUrM/bixQvmYS9H2cjOziYzMzO6desWXblyhZSVlSkgIICICp38a9asocuXL1ewlRycM7ICsbe3pyFDhlBOTo7IJDkiIoKMjIzE1rtz5w7JyclRnz596ObNm/T582f6/Pkz3bhxg3r37k1ycnJMBEtl4erVq6SiokJjx44leXl5mjx5Mtna2pKSkhJFRUWJrZeXl0fe3t4iF3BJMnv2bFJQUCAej0c8Ho/k5eVp7ty5rPU+f/5c4hOYDx8+iDiP2JKZmUnbtm2jCRMm0Lhx42jLli2UmZlZZl1JEhQURAMHDpSoXQ4ODqSmpkYGBgbUvXt36tWrl8hLXJycnMjCwqLEZQKBgCwsLFhHm1pZWYm8bGxsaMCAAbRp0ybmJkZcysvZnZWVReHh4TR37lyysLAgOTk5atCgAY0ZM4Z2795NL168YGWv8PxkZGREvr6+rHV+xeXLl8V+CPE9qqqqdOvWrWLjN2/eJDU1tTJpHzhwgPr160fm5uZkamoq8pIks2bNKlN09IwZM8jX15eICm2WlpYmIyMjkpWVpRkzZrDS5PP5IpGlRR9OsYXH45Genh45ODgUOwewOR8oKCiITBJkZWUZpywHx9+OcLJ87tw5cnZ2pu7du9OiRYsoIyODWWfy5MkkJydHBw8erCgzOTj+GN68eUOurq7E4/FEHJKPHj2i9u3bk56eHn369IkWL15M8vLylW4Ox1Eyqamp5OHhIfKgcsuWLWRsbEwfP34kov9F392+fZt4PB7dvHmzIkz9a7h06RKtWLGCVqxYIRJw4+joSHp6eqSoqEg7duxgxt+9e0dWVla0fv36ijCXowhcA5sKpDwajJw4cQLOzs748OGDyLimpia2bt2Knj17Ssp8iZGQkIDly5cjOjoaAoEAzZo1w4wZM9C4cWNWesrKykhMTCy35hqZmZm4d+8eBAIBTExMJNoA4V8kMzMTvXv3xtWrV6Gvr1+sVuadO3fE1vxVoyhxa7empKSgefPmqF+/Ptzd3VG/fn2mEPWKFSvw8OFDREVFFatTWtF8+fIFysrKkJKSEhn/+PEjlJWVISsrW2otS0tL3L59G3Xq1EH79u1haWkJS0tLkdqUZeHDhw/YtWsXgoKCkJiYCFtbW7i4uMDe3r7M9VMliYqKCi5fvlys3lZMTAwsLS3x9etXVrpr1qzBnDlzMHz4cGzZsgUjRoxASkoKbt++jQkTJsDX11cC1pcPN2/exNWrV2FkZMT6GsPn89GlSxemlu/x48dhY2NTrNmQOI18nJycSlWDtrTnAz6fj9evXzO1mYtetzk4/gWOHDkCJycn2Nvbo169evDx8cHw4cPh7u6OunXrAgDc3d2xatUqHD58GPb29hVsMQdH5SEjIwMFBQVQVVVlxtLS0uDn54f169cjNDQUDg4OEAgEePz4MUaPHo2IiAjIysri6tWraN68eQVaz1FaVq9ejYCAAHTs2BE+Pj5QVVXFjRs30KZNG6xduxYTJkxg1n3w4AF69+6NvXv3sp73/uvs3LkTPj4+sLOzg4mJCcaMGcMsi46Oxrhx45CZmYmEhAQAhXOgIUOG4OvXr7h06VKxORLH74VzRlYgVapUwZUrV2BiYiIyqbly5Qr69OmDN2/esNLNysrC6dOn8ejRIxAR6tWrh06dOkFRUVFsreTkZHh6emLTpk0iF0+g0NExbtw4+Pj4VKrJmIODAxwcHODk5FTRplQ4Dx48wNq1a5lGGw0aNMDEiRMrVWHk/v374+LFi+jbt2+JDWy8vLwqyDJRoqKi4OTkhHv37jE2EhFMTEywffv2H3ax/xECgQArVqzAkSNHkJeXB1tbW3h6ejJNliobMjIy0NXVhYODA6ysrNC+fXtUrVq1XL4rJiYGgYGB2Lp1K5SVlTFkyBCMHz+emeyKy8GDB7F//348e/YMubm5IsvEdXbb29vj8+fPCAkJQfXq1QEAL1++hKOjIzQ0NHD48GFWNjZo0ABeXl4YNGiQyPXA09MTHz9+xLp160qtFRERASsrq5+uM378eGzYsIGVrZcuXUKbNm2KFffOz8/HtWvXSmz09St+9QBBSHk2gfsVfD4fPj4+zAOoGTNmwMPDo9hxwKaBDwdHZYKKdBEV/p2QkAAHBwdMmzaNmdzVrl0bnz59Qo8ePbB8+XLmgdzs2bMxbNiwSnWvwcFRkSQnJ6N///5QVlbGqFGjUK1aNXTq1AkAkJOTA3d3d2zYsAEHDx5E7969QUS4f/8+li9fjsmTJ6NJkyYV/D/gKC35+flYvnw5jh49ClNTU/j5+UFdXR1Lly7F3LlzsXz5cvTp0weqqqpYvHgxQkJCcP36dYk93P+X2LlzJ8aMGYNNmzahV69ezP3ZqlWrULduXXTv3h0hISFYtGgR3r59izp16iAvLw8CgQDXr1+HjIwMCgoKOIdkBcI5IyuQAQMGQE1NDZs3b4aKigri4+OhpaUFe3t71K5du0InXUJGjx7NnEBLYsaMGfj69Ss2btzISv/OnTuQkZFhngYdPXoU27dvh4mJCby9vcWK3BKyadMmeHt7w9HREc2bNy8WWcM2cqdXr14lRtnweDzIy8vDyMgIgwcPRv369VnpS5qDBw9i0KBBMDMzQ+vWrQEAN27cwO3bt7Fnzx7069evgi0sRElJCWfOnIGFhYXEtd+9e4cHDx6Ax+OhXr160NLSKrNmbGwskpOTGUc/246UixYtwty5c9GhQwcoKCjgzJkzGDZsGDZv3lxmG8uDjIwMXL58GREREbh48SJiY2NRr149WFpawsrKCpaWlhLZvmlpadixYwcCAwPx8uVL9OnTB2lpabh48SKWLl0KNzc3sfQkHXH4/Plz2NvbIzExEbVq1QKPx8OzZ8/QuHFjHD16FDVr1hRLT4iioiKSkpKgp6cHbW1tnDt3Dk2bNkVycjJatWpVLNr9Z6ipqeHixYto1qxZicsnTJiAXbt24cuXL6xslZKSQlpaGhMhKOTDhw/Q1tZGQUEBK93Kjr6+/i8jLXk8Hh4/fvybLOLgkDwCgQB8Ph/v37+HtLQ01NXVUVBQgEuXLiEyMhLe3t548eIF2rVrh169emHgwIGwtLSEo6Mj3N3dYWxsXNH/BQ6OSoVAIMC8efOwaNEiyMvLo06dOsjMzESVKlXQsmVLjBgxAjweD8eOHcPChQtx6tQpdO7cGQKBAPn5+azmQhy/h6IPbgAwjq2CggJs3rwZa9asEYmQXLFiBWbPno0aNWpAUVERHz58QFhYGExNTSvwf/FnkpSUhAEDBmDixIkYPXo0M96/f38cPHgQtra28PDwQMeOHfH69WsEBweDx+OhWrVqcHR0hJSUFNc1uzJQIcnhHERUPg1GCgoKaNu2bdStWzdq2LAhNWrUiHr06EHBwcGsmnnUr1+/xPpoQqKiosrUQdfMzIypLZSSkkJycnI0aNAgMjIyYt1JVVjPsaQX2wYbRIVNNtTU1EhPT4969+5NvXr1In19fVJXV6f+/ftT/fr1SU5Ojq5cucL6OySJgYEBzZs3r9i4p6cnGRgYVIBFJVO/fn2Ki4uTqOa3b99oxIgRJCUlxfz20tLS5OzsLFLfqqzk5ORQeno6q8/Wq1dPpFbJqVOnSE5OTmJNd8qbr1+/UlhYGHl4eFCLFi1IVlaWGjZsyEorNzeXDh48SN26dWO6FW/cuJG+fv3KrBMSEkLq6upia9evX5/pVFi0Nu+8efNYNwgiIjp79iytWbOGVq9ezXSBLgsGBgZMPSgzMzOmyPaZM2fE7nw+depU0tbWLrGj/IQJE0hZWZkuXbrE2lYej1di5/AHDx6QiooKa10ODo7KQXJyMhkYGNCYMWOYY/3Dhw8UHx9P+fn51KdPHxo+fDhlZWVRQUEBmZmZEY/HI0dHR64LLAdHCaSlpdGUKVOoR48e5ObmRo8ePaL58+eTra0taWtrU4MGDahr166kra1NPB6PIiIiKtpkjlLy4cMH+vLlCxGJNvfy9fUlWVlZatKkCY0fP54+f/5MRIU9Hg4ePEh79+7lGhKVgTNnzjBNUIXbfPz48WRkZEQnT54kW1tb6tKlCx09erTEz3NdsysHnDOygpFkgxGBQEDdunUjHo9H//33Hw0cOJAGDBhATZo0IR6PR/b29mJrysvL//RE+fTpU1JQUGBlL1FhM4hHjx4REdHixYupU6dORER05coV1t2Jy4sZM2bQuHHjRDpIFhQU0MSJE2nWrFkkEAho9OjR1LZt2wq08n8oKChQcnJysfGHDx+W6TeTNCdOnKDOnTvTkydPJKY5evRoMjQ0pLCwMPry5Qt9+fKFTp48SXXq1KGxY8ey0gwMDKSJEyfSrl27iKiwCYisrCzx+XyytbUVu1OvnJycSDMMgUBAsrKy5da8RdIUFBTQjRs3aNGiRdSpUydSVFRk7ezX1NQkDQ0NGj9+PMXExJS4zsePH0lfX19sbQUFBeYcpqWlRbGxsURUeBxUqVKFlb3lgYuLC3l7exMR0caNG0lBQYFsbW1JXV2dnJ2dxdYbMWIE1a5dW2R/mjRpEikpKbGe5AgbvvD5fOratatIE5iePXuSvr4+de7cmZU2BwdH5aCgoIDmzZvHdIt3dXWl169fM8szMjKodevWTGf5goICmjJlCp0+fZru379fUWZzcFR6Xr58SRMmTKAWLVrQhg0bmPEbN25QaGgo2dnZMXO2u3fvVqClHKVBIBDQp0+fSFNTk2bPnk0fPnxglvn6+lLVqlUpLCyMli1bRubm5jRu3DiRpjYcZcPHx4c0NTVFxl69ekXPnz8nIqJ79+5R27ZtqU2bNlyH8koM54ysQCQZoUVU6CxRUVGhCxcuFFsWHh5OKioqFBwcLJamjo4OhYeH/3D5+fPnSUdHR2xbhaioqNDDhw+JiMjW1pb8/f2JqLATmby8PGvd8qBq1aolRho9ePCAORnGx8eXuaOupOjSpQsFBgYWGw8MDGScvpUBdXV1xqmnrKxMGhoaIi82aGpq0sWLF4uNX7hwgapWrSq2no+PDykoKFCHDh2oSpUqNHbsWKpWrRotXryYli5dSjVr1hTbyVlSdFnRyL3KRkFBAd28eZOWLFlCdnZ2pKKiQnw+n2rVqkXDhg2j7du3s37Cu2PHjjJ3uf4Rkoo4vHHjBoWFhYmMBQcHk76+PmlpadGoUaMoOzubtZ0FBQUi3dP37dtHkyZNotWrV1NOTg4rvV69elGDBg3o3bt3NGXKFFJUVCzx+lBanJycyMnJiXg8Hg0YMIB57+TkRKNHjyY/Pz969+4da30ODo7KQUxMDKmrq1OHDh2oc+fONGXKFObYfvPmDenq6tKECRPo1q1bNGvWLKpVqxY3yebgKAWvXr2iiRMnUosWLcjX11dkWW5uLuXk5LDOjuOoGJYuXUoyMjLM77l06VKqUqUKc8+Yl5dHixYtIgsLCxo6dKhI1g8He/bu3UuKiop09uzZYsuEgUNLliyhrl270rdv3363eRylhEuSr0C0tbXh4OCAoUOHomPHjuDz+WXSCwkJwezZs2FtbV1smY2NDWbOnIndu3dj2LBhpdZs37491q5dCxsbmxKXr1mzBu3atWNts5mZGXx8fGBra4vIyEim9uSTJ0/KVMg3MjISy5cvZxq3GBsbw8PDo0y25ufn4/79+6hXr57I+P3795kaafLy8qXq3vo76NmzJ2bMmIHo6Gi0atUKQGHNyAMHDmD+/Pk4duyYyLoVhb+/v8Q1MzMzS9x/tLW1WXWpDwoKwrZt2zBo0CBERUXB3Nwc+/btQ9++fQEAjRo1wtixY8XWnTdvnkhjqdzcXPj6+kJNTY0ZW7lypdi65YG6ujoyMjKgq6sLKysrrFy5EtbW1qhTp06ZtYcOHSoBC0vGxsYGx48fR7NmzeDi4gI3NzccPHgQUVFR6N27d6l1vL29YWVlhS5dugAAEhIS4OLiAicnJxgbG2PZsmWoXr06vL29WdnJ5/NFrgH9+/dH//79WWkJ9fbu3Ytu3brBxMQEGRkZOHbsWInXh9IirGOsr6+PadOmFavHy8HB8edB3zWrEQgE+O+//zBp0iRkZmZCUVERYWFh8PX1xcyZM6Gjo4NNmzahT58+OHXqFHJycnD8+HGoq6tX7H+Eg+MPQFdXF3PmzIGvry+OHTsGPp+PmTNnAiisOSwjI1OsHjNH5UR47vTw8ICCggJcXV0RHh6OxMRE7N+/Hx06dGBqEk6bNg2ZmZm4ceMGMjIyoKKiUtHm//G0aNEC0tLS2LRpE+rXr4/atWszy/h8PtLT03H58mXUr1+fu1+txHANbCqQ0NBQhISE4OTJk1BVVcWAAQMwZMgQsbvyCqlWrRpOnz79w4YaMTEx6NKlC16/fl1qzZiYGLRu3Rrdu3fH9OnTmeYs9+/fx9KlS3Hy5Elcu3bth40SfkV8fDwcHR3x7NkzTJ06lemcPGnSJHz48AF79uwRW3PXrl0YMWIEevfujbZt24KIcO3aNRw+fBhBQUEYPHgwK1tdXV0Zh2+LFi3A4/Fw69Yt+Pn5YfDgwVi9ejW2bt2KoKAgXLlyhdV3SJLSOrd5PN5f13CiQ4cO0NTUxI4dO5ju1FlZWRg+fDg+fvyI8+fPi6UnJyeHR48eoVatWsz7+Ph45nh4+fIlDAwMinVq/hlWVlalaoZx4cIFsWwtLzZt2gRra+tiznhJkJGRgcWLFyM8PBxv376FQCAQWV6WhiACgQACgYApUL1//35cuXIFRkZGGDt2bKkLw+vq6uL48eMwMzMDAMyZMweRkZHMsX7gwAF4eXnh3r17rOw8ffo0lJWVmUZO69evx5YtW2BiYoL169dDQ0Oj1Fpr1qxh/k5PT8fChQvRuXNndOjQQWQ9ruszB8e/jbBZzcePH5Gfnw9tbW1mgr1t2zZs2bIF58+fx7Zt27Br1y5YWFgwDsknT57g69ev0NHRQbVq1Sr6v8LB8Ufx+vVr+Pr6IiYmBh06dMD8+fMr2iQOFhR9mLNlyxaMGTMGQ4cOxaZNm5j5R9GmNp8/f4ampmZFmvxXERISghEjRqBv376YNm0a4wNJTU3FqFGj8PbtW0RFRUFaWrpYsyGOygHnjKwEpKen4+DBgwgJCcHFixdhYGCAIUOGwNPTUywdWVlZpKamQldXt8Tlr169goGBAXJycsTSPXHiBJydnYt1c9XU1MTWrVtZR9UVFBTgypUraNy4MapUqSKyLDs7G1JSUpCRkRFb19jYGKNHjy7WdXflypXYsmULkpKSWNu7ePFirFu3Dm/evAEA6OjoYNKkSZgxYwakpKTw7Nkz8Pl81h11/3WysrKQl5cnMqaqqiq2TmJiIuzs7JCdnY2mTZuCx+MhNjYW8vLyOHPmDBo2bCiWHp/Px+vXr5mn1SoqKoiLi4OhoSEA4M2bN6hevfpf59T9XQwaNAiRkZEYOnQodHV1i90sTJ48uYIs+x/y8vJITk5mHNIWFhaws7PD3LlzAQBPnz5F48aNkZ6ezkq/cePGWLJkCbp27YqEhASYmZnB3d0dFy5cgLGxMROVWBoMDAx+uY64XZ+bNWuG8PBwaGhowNTU9Kc3dHfu3Cm1LgcHR8WSnJyMLl26QF5eHosWLUK9evWYB202NjZo0aIFlixZAh8fHxw/fhzt2rWDu7v7D+81OTg4Ssfr168xa9YsvHjxAnv37uWcVH8oRZ1cAQEBGD9+PBYuXIiJEycymU5ChySHZCkoKMD27dsxfvx46OjooFGjRsjPz2fuxS9fvgwZGRlu+1diOGdkJePevXtwdHREfHy82I4NKSkpvH79GlpaWiUuL4vDJCsrC6dPn8ajR49ARKhXrx46deokkmLKBnl5eSQlJZVq8lxa5OTkcPfuXRgZGYmMP3r0CI0aNUJ2dnaZv+Pr168A2DnKfgd5eXno1KkTNm3aVC6RbJIkIyMDM2bMwP79+4s5vAGwdvBlZWVh165duH//PogIJiYmcHR0hIKCgthafD4fFy5cYJzmbdq0wf79+xmn8/v379GxY0fOGckSdXV1nDx5Em3bti0X/c+fP+PWrVslRl2WtmyFnp4edu7cifbt2yM3Nxfq6uo4fvw4E22YkJAAS0tLfPz4kZWNysrKSExMhL6+Pry9vZGYmIiDBw/izp076Nq1q1gR7eXB/Pnz4eHhAUVFxV9GcAgj3Dk4OCo3AoEA8+bNw+rVq6GqqgpdXV3UqVMHVatWxeLFi3HgwAFcvXoVAQEBkJWVha+vL3bs2IG+ffti4cKFZS4vxMHxr1M0uIHjz6WoQ3LdunVwdXWFn58fxowZI1ZmCwc7YmNjsXXrVjx8+BC1a9dGs2bNMGbMGEhJSTGp8hyVE+6XqQRkZ2fj2LFj2LNnD06fPg1tbW1MmzZNbB0igpOTE+Tk5EpcLm5EZFEUFBTQq1cv1p//EY0bN8bjx48l6oysVasWwsPDizkjw8PDmaimslJZnZBCZGRkkJiY+EeEo0+fPh0XL17Ehg0bMGzYMKxfvx4vX77Epk2bsHjxYta6CgoKGDVqlMTs7NChA4o+u+nevTuAwggzLvS/bGhoaBSLjpYUx48fh6OjI1Ojp+jvxOPxSu2MtLOzw8yZM7FkyRIcOXIEioqKIjVo4+Pjy1Q/U1ZWlqlnev78ecauKlWqMA8/KhIvLy84Oztj9erVnLPxB9ja2uLx48dlKivAwfE74fP5mDhxIjIyMpCamooqVapg0KBBmDVrFoYOHYrMzEyEh4ejXbt2GDFiBObMmQM5OTn07duXc0RycEgAzgn553H//n1oa2uL3LcWnQtMnDgRUlJSmDBhAmRlZeHm5sbNEcqZ//77D+vWrSs2XlBQwDkiKzncr1OBnD17Frt378aRI0cgJSWFvn374syZM7C0tGSlN3z48F+uI07zGiEXL17EnTt30KpVK7Rt2xabNm2Cr68vsrKy4ODggDVr1rCKNgMAX19fTJs2DQsXLkTz5s2LFZhl4/Rzd3eHq6srYmNj0aZNG/B4PFy5cgVBQUFYvXo1KzuFHDx4EPv378ezZ8+K1QesbKmJw4YNw7Zt28rk0PsdHD9+HDt27ICVlRWcnZ3Rrl07GBkZQU9PD7t374ajo6PYmosWLYKOjg6cnZ1FxgMDA/Hu3TvMmDFDLL0nT56IbQNH6Vm4cCE8PT0RHBxc5mjr73F3d4ezszP8/PzKpO3j44PevXvD0tISysrKCA4OFqk3GRgYiE6dOrHWb9u2LaZOnYq2bdvi1q1b2LdvHwDg4cOHlabsQ3BwMBYvXswVXv8BDg4OJUZ3c3BUZnR1dTF9+nT4+fkhJiYGjx49wu3btxEWFoYLFy4gPDxc5Jhn87Ccg4OD408kPT0dKioqTH3d2NhY9OjRA5cvXy72EL2oQ3LcuHGQkZFh5qEc5U9JgSFcanblh0vTrkAUFRXRvXt3DB48GN26dWNVH7G82bJlC8aNGwd9fX28ePECXl5e8PX1xdChQ8Hn87Fr1y6MGzeOtcOr6JP1oicQ4QmFbdrr4cOHsWLFCqY+pLCbtr29PSs9oLApxJw5czB8+HBs2bIFI0aMQEpKCm7fvo0JEybA19eXtXZ5MGnSJOzYsQNGRkYwMzMr5uitLF2alZWVcffuXejp6aFmzZoIDQ1Fy5Yt8eTJEzRu3Bjfvn0TW1NfXx979uxBmzZtRMZv3ryJgQMHlotzMTY29ofNozh+jqmpKVJSUkBE0NfXL3YuLIujX0lJCQkJCUx9z7Ly5csXKCsrF7vB+fjxI5SVlUvdEOd7nj17hgkTJuDZs2dwdXWFi4sLAMDNzQ0FBQUiTWkqiu9rp3JwcPw9pKWlwc/PD9evX8eQIUMwZcoUAIUNxCR1/uTg4OD4U9i5cycOHjyIdevWMZl19+/fx9ChQ3Ht2jVIS0uX6GgUOi45ODh+DRcZWUHk5+dj8eLF6NevX6UuAr569WqsWrUKkyZNwunTp9GjRw9s3bqVicK0srLCrFmzWDsjL168KElzGXr16iXxtPINGzZg8+bNGDRoEIKDgzF9+nQYGhrC09OTdZ248iQxMZHpcv7w4UORZZXpKZ2hoSGePn0KPT09mJiYYP/+/WjZsiWOHz8OdXV1VpqvX78u8bjS0tJCWlpaGS3+H1++fMHu3buxdetWxMXFcTUjWeLg4FBu2p07d0ZUVJTEJtPCYuTfU5Y08/z8fFy8eBGbN28utt+uWrWKtW55UJnOHZWN0NBQeHt7Iz4+vqJN4eAQG11dXcyZMwe+vr4ICQlBZmYmZs+eDUNDQ674PwcHxz/H+/fv8fbtW8ydOxcLFy5E7dq18fr1a+Tm5oLP5//wfohzRHJwlB4uMrICUVRURFJSEvT09CralB/yaJjAMAAAH7NJREFUvY2ysrKIi4uDsbExgMJonrp165apHmV58PnzZxw8eBCPHz/GtGnTUKVKFdy5cwc6OjqoUaMGK82i20JbWxvnzp1D06ZNkZycjFatWnHpeSxZtWoVpKSk4OrqiosXL6Jbt24oKChAfn4+Vq5cyaqTct26deHl5YUhQ4aIjO/cuRNeXl5lrul24cIFBAYGIjQ0FHp6eujTpw/69OkDU1PTUmvUqFEDNjY2sLa2hrW1tUTrpnL8j23btmHBggUYMWIEGjduXCzqsmfPnhVkmSh/wvWAz+dDTU3tlw7JyvhwRlJs2bIFZ8+ehYyMDCZPngxzc3NcuHAB7u7uePDgAYYOHYpNmzZVtJkcHKx5/fo1fH19ERMTgw4dOvyyYRUHBwfH30pAQAB2794NPT09LF26FA8ePMDo0aMRFxdXYukfroY8B4d4cJGRFYi5uTliYmIq9eQzOztbpB6knJycSIMcOTk55Ofnl+k7Pn/+jG3btiEpKQk8Hg8mJiZwdnb+YQTSr4iPj4etrS3U1NTw9OlTjBw5ElWqVMHhw4eRmpqKHTt2sNKtVq0aPnz4AD09Pejp6eHGjRto2rQpnjx5As6nzx43Nzfmb2tra9y/fx9RUVGoU6cOmjZtykpz5MiRmDJlCvLy8mBjYwOgsIHR9OnT4e7uzkrzxYsXCAoKQmBgIDIyMtC/f3/k5eXh0KFDMDExEVtv7NixiIyMxKRJk5CdnY1atWqJOCcrS53A30l0dLTIeUAc5+6PEDYxWrBgQbFlZSkFIWn+hOsBUNhVm+25+U9n+fLlmD17Npo0aYKkpCQcPXoUc+bMwcqVKzFp0iRMmDABVatWrWgzOTjKRLVq1TBnzhzMmjUL165dw4cPH6CpqVnRZnFwcHD8NvLy8iAjI4Nhw4ZBVlYWgYGBmDdvHlq1aoXGjRsjMjKSyYghInz+/Bl169YtUyNDDo5/ES4ysgI5cOAAZs6cCTc3txKbtzRp0qSCLPsfUlJSePjwIbS0tEBEqFWrFq5cuQJ9fX0AwJs3b9CgQQPWE/qoqCh07twZCgoKaNmyJYgIUVFRyMrKwtmzZ5k0Y3GwtbVFs2bNsHTpUqioqCAuLg6Ghoa4du0aBg8ejKdPn7KydeTIkahVqxa8vLwQEBDANJuIiopC7969sW3bNla65cnt27dx4MCBEhvuhIaGVpBVPyY7Oxvy8vJl1iEizJw5E2vWrGH+3/Ly8pgxYwY8PT3F1uvatSuuXLmC7t27w9HREXZ2dpCSkoKMjAzi4uJYOSOF5OXl4fr164iIiEBkZCSuX7+OnJwcGBoawsbG5p+Isnr79i0GDhyIiIgIqKurg4jw5csXWFtbY+/evdDS0qpoE8ud33k9YNv1+V+vGSmsPezs7IyIiAjY2NjAxsYGBw8eZF1SgoOjsvLmzRsAXLdfDg6Of5OgoCBs2bIFJ06cwJ49e3DgwAEkJycjLS0NrVu3RnJyMmRkZJh5y/nz57ksJw4OMeGckRVISTUlinbiqgwRO9/XxPg+/Lystgo7J2/ZsgXS0oWBuvn5+Rg5ciQeP36MS5cuia2ppqaGO3fuoE6dOiLOyNTUVNSvXx/Z2dmsbBUIBBAIBIyd+/fvx5UrV2BkZISxY8eyblxRXuzduxfDhg1Dp06dcO7cOXTq1AnJycl4/fo1evXqhe3bt1e0iQCAgoIC+Pn5ISAgAG/evMHDhw9haGiIefPmQV9fn2nkwYZv374hKSkJCgoKqFu3rkhUrzhIS0vD1dUV48aNQ926dZlxSTgjv+fTp09YsWIF1q5di2/fvlWK80B5M2DAAKSkpGDnzp1MCYh79+5h+PDhMDIyQkhISAVbWP78zuvBunXr8OHDB3h5eYn1OSkpKaSlpf2zzkhFRUXcv38ftWvXBlCYGXDp0iWYm5tXsGUcHBwcHBwcZUV4z5WRkYE+ffrA1tYW06ZNAwBs2rQJ+/btg5ycHDZs2AA9PT18+fIFqqqq+PTpE5cZwcHBAi5NuwIpj46+kqa8GswIiYqKEnFEAoWOn+nTp8PMzIyVpry8PL5+/Vps/MGDB2WKsOLz+SIOg/79+6N///6s9cobPz8/rFq1ChMmTICKigpWr14NAwMDjBkzplI1TfL19UVwcDCWLl3KpNQCQOPGjbFq1aoyOSOVlZXRokWLMtt4+fJlBAYGwszMDA0aNMDQoUMxYMCAMusChdGgV69eRUREBCIiInD79m3o6+tjwIABsLS0lMh3VHZOnz6N8+fPM45IADAxMcH69evRqVOnMuuHh4cjPDwcb9++hUAgEFkWGBhYZn1J8DuvBxMnTmT1uX/92eX3kduysrL/RNQuBwcHBwfHvwCPx0NERAT8/f2hqqqKwYMHM92xx4wZg4KCAuzZswcLFy7E7NmzYWRkBABcKQsODpZwzsgKpLLXBgNQ7s4QVVVVPHv2DA0aNBAZf/78OVRUVFhp2tvbY8GCBdi/fz+AwgvLs2fPMHPmTPTp06dM9mZnZyM+Pr5Ep0ZlaYQhJCUlBd26dQNQGMGTkZEBHo8HNzc32NjYVJqi9Dt27MDmzZvRoUMHjB07lhlv0qQJ7t+/L5ZW7969S7WeuCnqrVu3RuvWrbF69Wrs3bsXgYGBmDp1KgQCAc6dO4datWqJvb96eXnh4sWLuH37NgwNDWFpaYmJEyfC0tIS1apVE0vrT0cgEBRrLAMURp5+f5yJy/z587FgwQKYmZlBV1e30hYWl+T1oDTHgbS0NKpVq4aOHTuiR48epdIt62/xN7B161YoKysDKIziDwoKKhYN4erqWhGmcXBwcHBwcJQBgUCAJ0+e4ObNmyAiaGhogM/nIycnB3Jychg/fjxkZGSwZs0aLFu2DOvXr4e0tHSlvbfk4KjscGnalYB79+6VWNOvsjm3ygNXV1ccPnwYy5cvR5s2bcDj8XDlyhV4eHigT58+8Pf3F1vz69ev6Nq1K+7evYv09HRUr14dr1+/RuvWrREWFlasFltpOX36NIYNG4b3798XW1ZZ0uqLUqtWLYSFhaFx48Zo2rQpZs6ciUGDBuH69euws7PDly9fKtpEAICCggLu378PPT09kbT6e/fuoWXLlvj27VuptUaMGFGq9SSRov7gwQNs27YNO3fuxOfPn9GxY0ccO3as1J/n8/moXbs2Zs6ciX79+v3TT1Xt7e3x+fNnhISEoHr16gCAly9fwtHRERoaGjh8+DBrbV1dXSxduhRDhw6VlLnlxs6dOxEQEIAnT57g+vXr0NPTg7+/PwwMDGBvb19qndIcBwKBAG/fvkVkZCSmTZtWYoMfDlH09fV/OeHg8Xhi1+Lk4ODg4ODgqDiKliH79OkTjh8/jkmTJsHe3p5pfJqbm8uU5Nq+fTtsbGz+iMAiDo7KDOeMrEAeP36MXr16ISEhgakNBoA5GVYG59b3NSNLgsfjse6onZubCw8PDwQEBDAaMjIyGDduHBYvXsy6xh8AXLhwAXfu3IFAIECzZs1ga2vLWgsAjIyM0LlzZ3h6ev4RBd0HDx4MMzMzTJ06Fb6+vli9ejXs7e1x7tw5NGvWrNI0sDEzM8OUKVMwZMgQEWfk/Pnzcf78eVy+fLmiTfwpBQUFOH78OAIDA8VyRp4+fZpJzY6JiUG9evVgZWUFS0tLWFpa/lPpn8+fP4e9vT0SExNRq1YtJpq5cePGOHr0aJk6i2tqauLWrVuVvsPhxo0b4enpiSlTpsDX1xeJif/X3p0HRX3ecRz/LBCMgigRA6JRQSz1wCPVHqNpgSjiQQImaoyIV9N6RafmEEyM4kHUOJrEIzMJKhBFUyYqoWoYRw6VtkGrEW8NatRa21QiNXiz2z9SaIiYKLL7rPJ+zWQiv2fntx8YWPS73+f5HlBgYKBSUlKUmppqtyMzNm3apHHjxun06dN2uT8AAIAzqihCfvPNN/Lw8NCNGzfk7u6uixcvauPGjXrllVcUExOj999/X5IqOyQB1A6KkQZFRUXJ1dVVH3zwgQIDA1VYWKgLFy7opZde0sKFC/XEE0+YjqjMzMzbrv35z3/WkiVLZLPZdOXKlXt6nsuXL6u4uFg2m01BQUFq0KDBPd3PHry8vLR3716nL2pUKCkp0dWrV+Xv7y+r1aqFCxdWDtyZPn26vL29jeYbPXq03nnnHeXl5Wn48OFKSEjQrFmzlJiYqKNHjyotLU1/+tOf1Lt3b6M5HeHSpUvasWOH8vPzlZubq3379ikoKEhhYWFaunSp6XgOs3XrVh05ckQ2m03t27e/5zcQJGnq1Kny9PTU9OnTayGh/bRv315JSUmKjo6uUpQ/cOCAQkNDq+3Irg0XL17U6NGjnebNCQAAAHurKERmZ2dr2bJlKisrU5MmTfTuu+/Kz89PpaWl2rBhg+Lj4xUTE6P33nvPdGTggUMx0iAfHx/l5OSoU6dOatSokQoLCxUcHKycnBy99NJL2rt3r+mI1Tpy5IgSEhKUlZWlYcOGafbs2ZXTRU26cuWKtm3bpgEDBkiSEhISdO3atcp1V1dXzZ49u8oAgrsxevRo9ejR454GqjhCdcN7quPl5WXnJD/su5N5s7OzlZSUpL/97W+VnaxvvPFGrQwvuZ+Ul5ersLBQn3zyiZYvX15npmnb0+TJk5WWlqZOnTqpU6dOt5xNuWjRIkPJqrrdcQXHjx9Xp06d7vkNHwAAAPxfZmamYmNj9eKLL8rf318ZGRn6+9//ri1btqht27YqLS1VZmamxowZowkTJtTo+DAAt8cAG4PKy8srD8L38fHRuXPnFBwcrFatWuno0aOG093q3LlzmjFjhlJTU9WnTx99/vnn6tixY43uZY9BIxWddBXFyKVLl6pDhw6qX7++pG+LqP7+/vrDH/5w94H/d79BgwZpx44dCgkJuaWo4SxDCxo3bnxHBymbLnJ9932QPn36qE+fPgbTmGG1WrV7927l5uYqLy9PBQUFKisrU4sWLRQTE6OwsDDTEe3qs88+U0lJifr27Vt5LS0tTTNmzFBZWZmio6O1ZMmSe9oSU1RUpC5dukiSDhw4UGXNmQ4cDwgI0Oeff37L+UNbtmxR+/btDaUCAAB48Bw9elSJiYmaN2+eJkyYoDNnzuitt97SpUuX1LNnT23fvl3BwcGKiopSamqqunXrZjoy8MChGGlQx44dVVRUpMDAQP3iF7/QggUL5O7urvfff1+BgYGm41UqLS1VUlKSlixZoi5dumjbtm33vIW8UaNGVT5OT09XVFRUjSdoS9KaNWtuKTSmp6dXfi1Xr16tZcuW1bgYmZ6eruzsbNWvX195eXlVChkWi8VpipHfPVvOZrOpX79+Sk5OVvPmzQ2mqp4zFYMcrV+/fiooKKgcshQaGqrFixcrLCzMqX7+7WnmzJkKDQ2tLEbu379fY8aM0ciRI9WuXTu99dZb8vf318yZM2v8HPY6a7G2vfLKK5owYYKuXr0qm82mwsJCrV27Vm+++aaSk5NNxwMAALgvWa1Wubi4VP5f+vaIpLCwMI0dO1Znz55VeHi4IiIiNGXKFEVHR+vpp5/Whg0b1K5dOw0dOrRO/5sFsBe2aRuUnZ2tsrIyDRw4UCdOnNCAAQN05MgRNWnSRB999JHCw8NNR9SCBQs0f/58+fn5KSkp6a4mut6N725LrCk/Pz9t27ZNHTp0kCQ1bdpUu3btUuvWrSVJx44dU/fu3Ws8RdrPz0+TJk1SfHx85S+y+0FtfG3twcXFRY0aNfrRX+4lJSUOSuRYQ4cOVVhYmMLCwtS2bVvTcYxo1qyZsrKyKt9tfu2115Sfn6+dO3dKkjIyMjRjxgwdOnTIZEyH+eCDDzRnzhydOXNGktS8eXPNnDnT6Y+GAAAAcDYVxceK8yFLS0urNMQcPXpUwcHBGjNmjC5duqTVq1fL3d1dMTExyszMVGBgoA4dOqSHHnqIYiRgB3RGGvTdbakVL3YlJSXy9vZ2mhe8+Ph41a9fX0FBQUpNTVVqamq1j3OG4QelpaVyc/v/t/RXX31VZd1qtVY5Q/JuXb9+XUOGDLmvCpHOLjEx8ZYu2bpi7dq1piMY9/XXX1eZTJ+fn6/IyMjKj7t3715ZmLsXu3btUkZGhk6fPq3r169XWXOG164KL7zwgl544QX9+9//ltVq1aOPPmo6EgAAwH2nohB56tQprV69WtnZ2Tpz5ox69Oihfv36adiwYQoODtY333yjY8eOafDgwXJ3d5f0bQNKVlaWHn/88cprAGofxUgn88gjj5iOUEVcXJzTFEZ/TIsWLXTgwAEFBwdXu15UVKQWLVrU+P4jRozQRx99pGnTptX4Hqjqueeeq9MFF6vVqpSUFK1fv16nTp2SxWJRQECAnn32WQ0fPvy++dmrKV9fX508eVKPPfaYrl+/rj179igxMbFy/dKlS7eczXq31q1bp7i4OEVERGjr1q2KiIjQ8ePHdf78ecXExNzrp1BrEhMTFRsbqzZt2sjHx8d0HNRAr169dOLECZ04ccJ0FAAA6qyKQuT+/fv1zDPPqFu3bmrfvr0iIyO1YsUK5eXl6dChQ5o7d648PT3VsGFDLV++XB07dtSGDRu0adMmJSQkqFmzZqY/FeCBRjHSgNGjR9/R41auXGnnJD8uJSXFdIQ71q9fP73xxhvq37//LROzr1y5osTERPXv37/G9y8vL9eCBQuUnZ3t1FN5q+OMRS1nzORINptNUVFR2rJlizp37qyQkBDZbDYdPnxYI0eO1Pr167Vx40bTMe0qMjJS8fHxmj9/vjZu3KgGDRpUOY+2qKhIbdq0uafnSEpK0uLFizVhwgQ1bNhQ77zzjgICAvT73//eqf6S+fHHH2vWrFnq3r27YmNjNWTIEDVt2tR0LNyF6OhoXbhwwXQMAADqrIpC5L59+9SzZ0+NHz9eCQkJaty4sSRp0KBBmjNnjlasWCFPT08lJCQoKSlJEydO1IgRI9SwYUNlZmaqZcuWZj8RoA7gzEgDXFxc1KpVK3Xt2lU/9OXfsGGDA1M51ieffFLl46FDh+rtt9+usmVTkp566qk7vuc///lPdenSRe7u7po4caJ+8pOfyGKx6MiRI1q6dKlu3rypvXv33vIcd+qHJhtbLBbl5OTU6L617fuTyrOyshQeHi4PD48q101vT3VxcdH58+frbGfkqlWrNHnyZGVmZt7yvZWTk6Po6GgtXbpUcXFxhhLa31dffaWBAweqoKBAnp6eSk1NrdKt+OSTT+qXv/yl5s6dW+Pn8PDw0MGDB9W6dWv5+PgoNzdXISEhOnz4sMLDw/WPf/yjNj6VWnHw4EGtWbNG69at09mzZ9WrVy/FxsYqOjpaDRo0MB0PAADA6X3xxRcKCQnRyy+/rNmzZ6u8vFyurq66efOm3NzcVFxcrIkTJ+rs2bPasGGDgoKCdP36dZ06dUo+Pj5Ot1MReFBRjDRg/PjxWrdunVq2bKnRo0crNja2zr3o3cm5ixaLReXl5Xd135MnT2rcuHHaunVrZaHXYrGod+/eWr58udMNcbGHUaNG3dHjVq1aZeck+CEREREKDw9XfHx8tetJSUnKz89Xdna2g5M5XmlpqTw9PeXq6lrleklJiTw9Pe/pvJ7HHntMmzdvVkhIiDp37qz4+HgNHTpUf/nLXxQZGVnjgVb2VlBQoPT0dGVkZOjq1av6z3/+YzpSnff9N3qq4+bmJj8/P/Xu3VtRUVEOSAUAACpYrVa9/vrrSk5O1uuvv65JkyZJUmVBsmKYzY4dOxQaGqqMjIw7+v0OoPaxTduA5cuXa/HixVq/fr1WrlyphIQE9e/fX2PGjFFERESd2L5qtVrtct+AgAB9+umnKikp0RdffCFJCgoKqlPFXoqM94eioiItWLDgtut9+/bVu+++68BE5txuiFFt/Nw+8cQT2rp1q0JCQjR48GBNnjxZOTk52rp1q5588sl7vr+9eHh4qH79+nJ3d9elS5dMx4Fu/336XVarVcePH1dycrJefvllzZo1ywHJAACA9G3Dy8SJE3X58mWlp6fr8uXLio+Pl6urq6xWa+W/s3/2s5+pSZMmOn/+vOHEQN1FZ6QT+PLLL5WSkqK0tDTduHFDhw4dkqenp+lY+J87fbfM9LZn3F/c3d315Zdf3vbcwnPnzikgIOCeJsDj2+7Kq1evyt/fX1arVQsXLtTOnTsVFBSk6dOny9vb23TESidPnlR6errWrFmjY8eO6de//rWef/55DRo0qM5Onb9fbdq0SePGjdPp06dNRwEAoM45f/685s6dq127dikmJkZTp06V9P8OyYKCAk2YMEErV67U448/bjgtUDfRGekELBaLLBaLbDab3ToGUXMUAWAP5eXlcnO7/Utwxdk2qJmKbc1ubm7y9PSs/Hjs2LEaO3asyWjV+tWvfqXCwkKFhIRo1KhRev7559W8eXPTsVBDPXr0ULdu3UzHAACgTvLz89Nrr72muXPnVs5hmDp1auWRQB9//LF8fX3VunVrgymBuo3OSEOuXbtWuU17586dGjBggEaNGqXIyMg7Ok8RwP3NxcVFffv2Vb169apdv3btmj799NO7PjcV33JxcbmjIy+c5es7bdo0DRs2TB06dDAdBQAA4IFQXYfknDlztGjRIm3fvl0dO3Y0HRGosyhGGvDdATajRo1SbGysmjRpYjoWAAdi0JB95efnV/7ZZrOpX79+Sk5OvqXb8De/+Y2jowEAAMBBKgqS+/bt07Vr11RUVKSCggK2ZwOGUYw0wMXFRS1btlTXrl1/sHOnrp9BWDHtDADuVcOGDbVv3z4FBgaajlJpypQpmj17tjw8PDRlypQffOyiRYsclAoAAODBcv78eU2bNk07duxQRkaGunTpYjoSUOdxZqQBcXFxFNn+580331RCQsIt18vLyxUbG6u1a9caSAUA9rd3717duHGj8s+3w+8LAACAmvPz89P8+fNltVrl6+trOg4A0RkJw3x9fTV79mz97ne/q7xWXl6u5557TgcOHNDhw4cNpgPshyntjuWMnZEAAAAAUBfRGQmjNm/erF69eqlx48YaPHiwbty4oSFDhujIkSPKzc01HQ+wG6a0Ox4dhgAAAABgHp2RMC4vL09PP/200tLStGLFChUXFysnJ4cWegA19v3O06ysLIWHh8vDw6PKdWfpPC0rK9O8efO0bds2/etf/5LVaq2yfuLECUPJAAAAAKB20RkJ40JDQ/Xhhx/qmWeeUbt27ZSfny8fHx/TsQDcx77feRobG2soyZ357W9/q/z8fA0fPlzNmjWjixMAAADAA4vOSDjc7c7K++tf/6qgoKAqhUhn6VoCAHtq3LixNm3apB49epiOAgAAAAB2RWckHO52Z+X16dPHwUkAwDl4e3vrkUceMR0DAAAAAOyOzkgYY7PZdPr0aTVt2lQNGjQwHQcAjFm9erUyMzOVmprK6yEAAACABxrFSBhjtVr18MMP6+DBg2rbtq3pOABgTNeuXVVcXCybzabWrVvroYceqrK+Z88eQ8kAAAAAoHaxTRvGuLi4qG3btrpw4QLFSAB1WnR0tOkIAAAAAOAQdEbCqE2bNmnevHl677331LFjR9NxAAAAAAAAYEcUI2GUt7e3Ll++rJs3b8rd3V3169evsl5SUmIoGQAAAAAAAGob27Rh1Ntvv206AgAY4+LiIovFcst1Ly8vBQcH69VXX9XAgQMNJAMAAAAA+6AzEgAAQzIzM6u9fvHiRRUWFmrVqlVKTU3VoEGDHJwMAAAAAOyDYiScxpUrV3Tjxo0q17y8vAylAQDzli1bprS0NH322WemowAAAABArXAxHQB1W1lZmSZOnKhHH31Unp6e8vb2rvIfANRlEREROnbsmOkYAAAAAFBrKEbCqFdffVU5OTlavny56tWrp+TkZCUmJsrf319paWmm4wGAUVeuXNHDDz9sOgYAAAAA1Bq2acOoli1bKi0tTaGhofLy8tKePXsUFBSkDz/8UGvXrtXmzZtNRwQAY1588UUVFxfzWggAAADggcE0bRhVUlKigIAASd+eD1lSUiJJ6tmzp8aNG2cyGgDY3ZQpU6q9Xlpaqt27d6u4uFg7duxwcCoAAAAAsB+KkTAqMDBQp06dUqtWrdS+fXv98Y9/1M9//nNlZWWpcePGpuMBgF3t3bu32uteXl6KjIzU+PHj1apVKwenAgAAAAD7YZs2jFq8eLFcXV01adIk5ebmqn///iovL9fNmze1aNEiTZ482XREAAAAAAAA1BKKkXAqp0+f1u7du9WmTRt17tzZdBwAAAAAAADUIoqRAAAAAAAAAByCMyNhjNVqVUpKitavX69Tp07JYrEoICBAzz77rIYPHy6LxWI6IgAAAAAAAGoRnZEwwmazKSoqSps3b1bnzp3105/+VDabTYcPH9b+/fv11FNPaePGjaZjAgAAAAAAoBbRGQkjUlJStH37dm3btk1hYWFV1nJychQdHa20tDTFxcUZSggAAAAAAIDaRmckjIiIiFB4eLji4+OrXU9KSlJ+fr6ys7MdnAwAAAAAAAD24mI6AOqmoqIiRUZG3na9b9++2rdvnwMTAQAAAAAAwN4oRsKIkpIS+fr63nbd19dXX3/9tQMTAQAAAAAAwN4oRsKI8vJyubnd/shSV1dX3bx504GJAAAAAAAAYG8MsIERNptNI0eOVL169apdv3btmoMTAQAAAAAAwN4oRsKIESNG/OhjmKQNAAAAAADwYGGaNgAAAAAAAACH4MxIAAAAAAAAAA5BMRIAAAAAAACAQ1CMBAAAAAAAAOAQFCMBAAAAAAAAOATFSAAAAAAAAAAOQTESAAAAAAAAgENQjAQAAAAAAADgEBQjAQAAAAAAADjEfwHRCmE8eppyogAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Set up plots\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 5))\n",
    "\n",
    "# Create variables for easier reuse\n",
    "value_counts = heroes_df[\"Publisher\"].value_counts()\n",
    "top_5_counts = value_counts.iloc[:5]\n",
    "\n",
    "# Plot data\n",
    "ax1.bar(value_counts.index, value_counts.values)\n",
    "ax2.bar(top_5_counts.index, top_5_counts.values)\n",
    "\n",
    "# Customize appearance\n",
    "ax1.tick_params(axis=\"x\", labelrotation=90)\n",
    "ax2.tick_params(axis=\"x\", labelrotation=45)\n",
    "ax1.set_ylabel(\"Count of Superheroes\")\n",
    "ax2.set_ylabel(\"Count of Superheroes\")\n",
    "ax1.set_title(\"Distribution of Superheroes by Publisher\")\n",
    "ax2.set_title(\"Top 5 Publishers by Count of Superheroes\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Perform Data Aggregation and Cleaning Required to Answer Second Question\n",
    "\n",
    "Recall that the second question is: *What is the relationship between height and number of superpowers? And does this differ based on gender?*\n",
    "\n",
    "Unlike the previous question, we won't be able to answer this with just `heroes_df`, since information about height is contained in `heroes_df`, while information about superpowers is contained in `powers_df`.\n",
    "\n",
    "### Joining the Dataframes Together\n",
    "\n",
    "First, identify the shared key between `heroes_df` and `powers_df`. (Shared key meaning, the values you want to join on.) Let's look at them again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Frost Giant</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>304.8</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>white</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>Yoda</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>Yoda's species</td>\n",
       "      <td>White</td>\n",
       "      <td>66.0</td>\n",
       "      <td>George Lucas</td>\n",
       "      <td>green</td>\n",
       "      <td>good</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>732</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Black</td>\n",
       "      <td>170.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>733</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>-</td>\n",
       "      <td>Brown</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>81.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>719 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  Gender Eye color               Race        Hair color  \\\n",
       "0             A-Bomb    Male    yellow              Human           No Hair   \n",
       "1         Abe Sapien    Male      blue      Icthyo Sapien           No Hair   \n",
       "2           Abin Sur    Male      blue            Ungaran           No Hair   \n",
       "3        Abomination    Male     green  Human / Radiation           No Hair   \n",
       "4            Abraxas    Male      blue      Cosmic Entity             Black   \n",
       "..               ...     ...       ...                ...               ...   \n",
       "729  Yellowjacket II  Female      blue              Human  Strawberry Blond   \n",
       "730             Ymir    Male     white        Frost Giant           No Hair   \n",
       "731             Yoda    Male     brown     Yoda's species             White   \n",
       "732          Zatanna  Female      blue              Human             Black   \n",
       "733             Zoom    Male       red                  -             Brown   \n",
       "\n",
       "     Height          Publisher Skin color Alignment  Weight  \n",
       "0     203.0      Marvel Comics          -      good   441.0  \n",
       "1     191.0  Dark Horse Comics       blue      good    65.0  \n",
       "2     185.0          DC Comics        red      good    90.0  \n",
       "3     203.0      Marvel Comics          -       bad   441.0  \n",
       "4     -99.0      Marvel Comics          -       bad   -99.0  \n",
       "..      ...                ...        ...       ...     ...  \n",
       "729   165.0      Marvel Comics          -      good    52.0  \n",
       "730   304.8      Marvel Comics      white      good   -99.0  \n",
       "731    66.0       George Lucas      green      good    17.0  \n",
       "732   170.0          DC Comics          -      good    57.0  \n",
       "733   185.0          DC Comics          -       bad    81.0  \n",
       "\n",
       "[719 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "heroes_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "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>3-D Man</th>\n",
       "      <th>A-Bomb</th>\n",
       "      <th>Abe Sapien</th>\n",
       "      <th>Abin Sur</th>\n",
       "      <th>Abomination</th>\n",
       "      <th>Abraxas</th>\n",
       "      <th>Absorbing Man</th>\n",
       "      <th>Adam Monroe</th>\n",
       "      <th>Adam Strange</th>\n",
       "      <th>Agent Bob</th>\n",
       "      <th>...</th>\n",
       "      <th>Wonder Man</th>\n",
       "      <th>Wonder Woman</th>\n",
       "      <th>X-23</th>\n",
       "      <th>X-Man</th>\n",
       "      <th>Yellowjacket</th>\n",
       "      <th>Yellowjacket II</th>\n",
       "      <th>Ymir</th>\n",
       "      <th>Yoda</th>\n",
       "      <th>Zatanna</th>\n",
       "      <th>Zoom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Agility</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Accelerated Healing</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lantern Power Ring</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dimensional Awareness</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cold Resistance</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phoenix Force</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omnipresent</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omniscient</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 667 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       3-D Man  A-Bomb  Abe Sapien  Abin Sur  Abomination  \\\n",
       "Agility                   True   False        True     False        False   \n",
       "Accelerated Healing      False    True        True     False         True   \n",
       "Lantern Power Ring       False   False       False      True        False   \n",
       "Dimensional Awareness    False   False       False     False        False   \n",
       "Cold Resistance          False   False        True     False        False   \n",
       "...                        ...     ...         ...       ...          ...   \n",
       "Phoenix Force            False   False       False     False        False   \n",
       "Molecular Dissipation    False   False       False     False        False   \n",
       "Vision - Cryo            False   False       False     False        False   \n",
       "Omnipresent              False   False       False     False        False   \n",
       "Omniscient               False   False       False     False        False   \n",
       "\n",
       "                       Abraxas  Absorbing Man  Adam Monroe  Adam Strange  \\\n",
       "Agility                  False          False        False         False   \n",
       "Accelerated Healing      False          False         True         False   \n",
       "Lantern Power Ring       False          False        False         False   \n",
       "Dimensional Awareness     True          False        False         False   \n",
       "Cold Resistance          False           True        False         False   \n",
       "...                        ...            ...          ...           ...   \n",
       "Phoenix Force            False          False        False         False   \n",
       "Molecular Dissipation    False          False        False         False   \n",
       "Vision - Cryo            False          False        False         False   \n",
       "Omnipresent              False          False        False         False   \n",
       "Omniscient               False          False        False         False   \n",
       "\n",
       "                       Agent Bob  ...  Wonder Man  Wonder Woman   X-23  X-Man  \\\n",
       "Agility                    False  ...       False         False   True  False   \n",
       "Accelerated Healing        False  ...       False          True   True  False   \n",
       "Lantern Power Ring         False  ...       False         False  False  False   \n",
       "Dimensional Awareness      False  ...       False         False  False  False   \n",
       "Cold Resistance            False  ...       False         False  False  False   \n",
       "...                          ...  ...         ...           ...    ...    ...   \n",
       "Phoenix Force              False  ...       False         False  False  False   \n",
       "Molecular Dissipation      False  ...       False         False  False  False   \n",
       "Vision - Cryo              False  ...       False         False  False  False   \n",
       "Omnipresent                False  ...       False         False  False  False   \n",
       "Omniscient                 False  ...       False         False  False  False   \n",
       "\n",
       "                       Yellowjacket  Yellowjacket II   Ymir   Yoda  Zatanna  \\\n",
       "Agility                       False            False  False   True    False   \n",
       "Accelerated Healing           False            False  False  False    False   \n",
       "Lantern Power Ring            False            False  False  False    False   \n",
       "Dimensional Awareness         False            False  False  False    False   \n",
       "Cold Resistance               False            False   True  False    False   \n",
       "...                             ...              ...    ...    ...      ...   \n",
       "Phoenix Force                 False            False  False  False    False   \n",
       "Molecular Dissipation         False            False  False  False    False   \n",
       "Vision - Cryo                 False            False  False  False    False   \n",
       "Omnipresent                   False            False  False  False    False   \n",
       "Omniscient                    False            False  False  False    False   \n",
       "\n",
       "                        Zoom  \n",
       "Agility                False  \n",
       "Accelerated Healing    False  \n",
       "Lantern Power Ring     False  \n",
       "Dimensional Awareness  False  \n",
       "Cold Resistance        False  \n",
       "...                      ...  \n",
       "Phoenix Force          False  \n",
       "Molecular Dissipation  False  \n",
       "Vision - Cryo          False  \n",
       "Omnipresent            False  \n",
       "Omniscient             False  \n",
       "\n",
       "[167 rows x 667 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "powers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, identify the shared key, and your strategy for joining the data (e.g. what will one record represent after you join, will you do a left/right/inner/outer join):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"\\nThe common identifier between these datasets is the superhero's name. In heroes_df, this is denoted by the name column, while in powers_df, it is represented by the column names themselves.\\n\\nGiven that one dataset has the superhero names as values within a column and the other has them as column names, we will need to transpose one of them. Transposing powers_df is the logical choice because it will maintain records that represent superheroes rather than superpowers. This aligns with the query about heights, which pertain to superhero attributes rather than superpower attributes.\\n\\nAlthough powers_df has fewer columns than heroes_df has rows, it includes names not found in heroes_df (specifically '3-D Man'). Therefore, an inner join appears to be the appropriate method to retain only records present in both datasets.\\n\""
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replace None with appropriate text\n",
    "\"\"\"\n",
    "The common identifier between these datasets is the superhero's name. In heroes_df, this is denoted by the name column, while in powers_df, it is represented by the column names themselves.\n",
    "\n",
    "Given that one dataset has the superhero names as values within a column and the other has them as column names, we will need to transpose one of them. Transposing powers_df is the logical choice because it will maintain records that represent superheroes rather than superpowers. This aligns with the query about heights, which pertain to superhero attributes rather than superpower attributes.\n",
    "\n",
    "Although powers_df has fewer columns than heroes_df has rows, it includes names not found in heroes_df (specifically '3-D Man'). Therefore, an inner join appears to be the appropriate method to retain only records present in both datasets.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the cell below, create a new dataframe called `heroes_and_powers_df` that contains the joined data. You can look at the above answer in the solution branch if you're not sure where to start.\n",
    "\n",
    "***Hint:*** Note that the `.join` method requires that the two dataframes share an index ([documentation here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html)) whereas the `.merge` method can join using any columns ([documentation here](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html)). It is up to you which one you want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Web Creation</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Frost Giant</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>304.8</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>white</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Yoda</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>Yoda's species</td>\n",
       "      <td>White</td>\n",
       "      <td>66.0</td>\n",
       "      <td>George Lucas</td>\n",
       "      <td>green</td>\n",
       "      <td>good</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Black</td>\n",
       "      <td>170.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>-</td>\n",
       "      <td>Brown</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>647 rows × 177 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  Gender Eye color               Race        Hair color  \\\n",
       "0             A-Bomb    Male    yellow              Human           No Hair   \n",
       "1         Abe Sapien    Male      blue      Icthyo Sapien           No Hair   \n",
       "2           Abin Sur    Male      blue            Ungaran           No Hair   \n",
       "3        Abomination    Male     green  Human / Radiation           No Hair   \n",
       "4            Abraxas    Male      blue      Cosmic Entity             Black   \n",
       "..               ...     ...       ...                ...               ...   \n",
       "642  Yellowjacket II  Female      blue              Human  Strawberry Blond   \n",
       "643             Ymir    Male     white        Frost Giant           No Hair   \n",
       "644             Yoda    Male     brown     Yoda's species             White   \n",
       "645          Zatanna  Female      blue              Human             Black   \n",
       "646             Zoom    Male       red                  -             Brown   \n",
       "\n",
       "     Height          Publisher Skin color Alignment  Weight  ...  \\\n",
       "0     203.0      Marvel Comics          -      good   441.0  ...   \n",
       "1     191.0  Dark Horse Comics       blue      good    65.0  ...   \n",
       "2     185.0          DC Comics        red      good    90.0  ...   \n",
       "3     203.0      Marvel Comics          -       bad   441.0  ...   \n",
       "4     -99.0      Marvel Comics          -       bad   -99.0  ...   \n",
       "..      ...                ...        ...       ...     ...  ...   \n",
       "642   165.0      Marvel Comics          -      good    52.0  ...   \n",
       "643   304.8      Marvel Comics      white      good   -99.0  ...   \n",
       "644    66.0       George Lucas      green      good    17.0  ...   \n",
       "645   170.0          DC Comics          -      good    57.0  ...   \n",
       "646   185.0          DC Comics          -       bad    81.0  ...   \n",
       "\n",
       "     Web Creation  Reality Warping  Odin Force  Symbiote Costume  Speed Force  \\\n",
       "0           False            False       False             False        False   \n",
       "1           False            False       False             False        False   \n",
       "2           False            False       False             False        False   \n",
       "3           False            False       False             False        False   \n",
       "4           False            False       False             False        False   \n",
       "..            ...              ...         ...               ...          ...   \n",
       "642         False            False       False             False        False   \n",
       "643         False            False       False             False        False   \n",
       "644         False            False       False             False        False   \n",
       "645         False            False       False             False        False   \n",
       "646         False            False       False             False        False   \n",
       "\n",
       "     Phoenix Force  Molecular Dissipation  Vision - Cryo  Omnipresent  \\\n",
       "0            False                  False          False        False   \n",
       "1            False                  False          False        False   \n",
       "2            False                  False          False        False   \n",
       "3            False                  False          False        False   \n",
       "4            False                  False          False        False   \n",
       "..             ...                    ...            ...          ...   \n",
       "642          False                  False          False        False   \n",
       "643          False                  False          False        False   \n",
       "644          False                  False          False        False   \n",
       "645          False                  False          False        False   \n",
       "646          False                  False          False        False   \n",
       "\n",
       "     Omniscient  \n",
       "0         False  \n",
       "1         False  \n",
       "2         False  \n",
       "3         False  \n",
       "4         False  \n",
       "..          ...  \n",
       "642       False  \n",
       "643       False  \n",
       "644       False  \n",
       "645       False  \n",
       "646       False  \n",
       "\n",
       "[647 rows x 177 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here (create more cells as needed)\n",
    "#get a transpose version\n",
    "powers_df_transposed = powers_df.T\n",
    "powers_df_transposed\n",
    "\n",
    "# reset index to first column called \"name\"\n",
    "powers_df_transposed.index.name = \"name\"\n",
    "powers_df_transposed.reset_index(inplace=True)\n",
    "powers_df_transposed\n",
    "\n",
    "#merge dataframes\n",
    "heroes_and_powers_df = heroes_df.merge(powers_df_transposed, on=\"name\", how=\"inner\")\n",
    "heroes_and_powers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the code below to check your work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Confirms you have created a DataFrame with the specified name\n",
    "assert type(heroes_and_powers_df) == pd.DataFrame\n",
    "\n",
    "# Confirms you have the right number of rows\n",
    "assert heroes_and_powers_df.shape[0] == 647\n",
    "\n",
    "# Confirms you have the necessary columns\n",
    "# (If you modified the value of powers_df along the way, you might need to\n",
    "# modify this test. We are checking that all of the powers are present as\n",
    "# columns.)\n",
    "assert [power in heroes_and_powers_df.columns for power in powers_df.index]\n",
    "# (If you modified the value of heroes_df along the way, you might need to\n",
    "# modify this as well. We are checking that all of the attribute columns from\n",
    "# heroes_df are present as columns in the joined df)\n",
    "assert [attribute in heroes_and_powers_df.columns for attribute in heroes_df.columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created a joined dataframe, we can aggregate the number of superpowers by superhero. This code is written for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
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       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
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       "      <th>Omniscient</th>\n",
       "      <th>Power Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
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       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Cosmic Entity</td>\n",
       "      <td>Black</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>14</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Frost Giant</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>304.8</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>white</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Yoda</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>Yoda's species</td>\n",
       "      <td>White</td>\n",
       "      <td>66.0</td>\n",
       "      <td>George Lucas</td>\n",
       "      <td>green</td>\n",
       "      <td>good</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Black</td>\n",
       "      <td>170.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>-</td>\n",
       "      <td>Brown</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>647 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  Gender Eye color               Race        Hair color  \\\n",
       "0             A-Bomb    Male    yellow              Human           No Hair   \n",
       "1         Abe Sapien    Male      blue      Icthyo Sapien           No Hair   \n",
       "2           Abin Sur    Male      blue            Ungaran           No Hair   \n",
       "3        Abomination    Male     green  Human / Radiation           No Hair   \n",
       "4            Abraxas    Male      blue      Cosmic Entity             Black   \n",
       "..               ...     ...       ...                ...               ...   \n",
       "642  Yellowjacket II  Female      blue              Human  Strawberry Blond   \n",
       "643             Ymir    Male     white        Frost Giant           No Hair   \n",
       "644             Yoda    Male     brown     Yoda's species             White   \n",
       "645          Zatanna  Female      blue              Human             Black   \n",
       "646             Zoom    Male       red                  -             Brown   \n",
       "\n",
       "     Height          Publisher Skin color Alignment  Weight  ...  \\\n",
       "0     203.0      Marvel Comics          -      good   441.0  ...   \n",
       "1     191.0  Dark Horse Comics       blue      good    65.0  ...   \n",
       "2     185.0          DC Comics        red      good    90.0  ...   \n",
       "3     203.0      Marvel Comics          -       bad   441.0  ...   \n",
       "4     -99.0      Marvel Comics          -       bad   -99.0  ...   \n",
       "..      ...                ...        ...       ...     ...  ...   \n",
       "642   165.0      Marvel Comics          -      good    52.0  ...   \n",
       "643   304.8      Marvel Comics      white      good   -99.0  ...   \n",
       "644    66.0       George Lucas      green      good    17.0  ...   \n",
       "645   170.0          DC Comics          -      good    57.0  ...   \n",
       "646   185.0          DC Comics          -       bad    81.0  ...   \n",
       "\n",
       "     Reality Warping  Odin Force  Symbiote Costume  Speed Force  \\\n",
       "0              False       False             False        False   \n",
       "1              False       False             False        False   \n",
       "2              False       False             False        False   \n",
       "3              False       False             False        False   \n",
       "4              False       False             False        False   \n",
       "..               ...         ...               ...          ...   \n",
       "642            False       False             False        False   \n",
       "643            False       False             False        False   \n",
       "644            False       False             False        False   \n",
       "645            False       False             False        False   \n",
       "646            False       False             False        False   \n",
       "\n",
       "     Phoenix Force  Molecular Dissipation  Vision - Cryo  Omnipresent  \\\n",
       "0            False                  False          False        False   \n",
       "1            False                  False          False        False   \n",
       "2            False                  False          False        False   \n",
       "3            False                  False          False        False   \n",
       "4            False                  False          False        False   \n",
       "..             ...                    ...            ...          ...   \n",
       "642          False                  False          False        False   \n",
       "643          False                  False          False        False   \n",
       "644          False                  False          False        False   \n",
       "645          False                  False          False        False   \n",
       "646          False                  False          False        False   \n",
       "\n",
       "     Omniscient  Power Count  \n",
       "0         False            7  \n",
       "1         False           16  \n",
       "2         False            1  \n",
       "3         False            8  \n",
       "4         False           14  \n",
       "..          ...          ...  \n",
       "642       False            3  \n",
       "643       False            6  \n",
       "644       False           19  \n",
       "645       False            8  \n",
       "646       False            4  \n",
       "\n",
       "[647 rows x 178 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Note: we can use sum() with True and False values and they will\n",
    "# automatically be cast to 1s and 0s\n",
    "heroes_and_powers_df[\"Power Count\"] = sum(\n",
    "    [heroes_and_powers_df[power_name] for power_name in powers_df.index]\n",
    ")\n",
    "heroes_and_powers_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering the Question\n",
    "\n",
    "Now we can plot the height vs. the count of powers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "ax.scatter(\n",
    "    x=heroes_and_powers_df[\"Height\"], y=heroes_and_powers_df[\"Power Count\"], alpha=0.3\n",
    ")\n",
    "\n",
    "ax.set_xlabel(\"Height (cm)\")\n",
    "ax.set_ylabel(\"Number of Superpowers\")\n",
    "ax.set_title(\"Height vs. Power Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm...what is that stack of values off below zero? What is a \"negative\" height?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identifying and Handling Invalid values\n",
    "\n",
    "One of the trickier tasks in data cleaning is identifying invalid or impossible values. In these cases, you have to apply your domain knowledge rather than any particular computational technique. For example, if you were looking at data containing dates of past home sales, and one of those dates was 100 years in the future, pandas wouldn't flag that as an issue, but you as a data scientist should be able to identify it.\n",
    "\n",
    "In this case, we are looking at heights, which are 1-dimensional, positive numbers. In theory we could have a very tiny height close to 0 cm because the hero is microscopic, but it does not make sense that we would have a height below zero.\n",
    "\n",
    "Let's take a look at a sample of those negative heights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "      <th>Power Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>Chameleon</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Crimson Crusader</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>-</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>Luna</td>\n",
       "      <td>Female</td>\n",
       "      <td>-</td>\n",
       "      <td>Human</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>JJ Powell</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>ABC Studios</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>Male</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 name  Gender Eye color   Race        Hair color  Height  \\\n",
       "150         Chameleon    Male         -      -                 -   -99.0   \n",
       "163  Crimson Crusader    Male      blue      -  Strawberry Blond   -99.0   \n",
       "364              Luna  Female         -  Human                 -   -99.0   \n",
       "315         JJ Powell    Male         -      -                 -   -99.0   \n",
       "630         Weapon XI    Male         -      -                 -   -99.0   \n",
       "\n",
       "         Publisher Skin color Alignment  Weight  ...  Reality Warping  \\\n",
       "150      DC Comics          -       bad   -99.0  ...            False   \n",
       "163  Marvel Comics          -      good   -99.0  ...            False   \n",
       "364  Marvel Comics          -      good   -99.0  ...            False   \n",
       "315    ABC Studios          -      good   -99.0  ...            False   \n",
       "630  Marvel Comics          -       bad   -99.0  ...            False   \n",
       "\n",
       "     Odin Force  Symbiote Costume  Speed Force  Phoenix Force  \\\n",
       "150       False             False        False          False   \n",
       "163       False             False        False          False   \n",
       "364       False             False        False          False   \n",
       "315       False             False        False          False   \n",
       "630       False             False        False          False   \n",
       "\n",
       "     Molecular Dissipation  Vision - Cryo  Omnipresent  Omniscient  \\\n",
       "150                  False          False        False       False   \n",
       "163                  False          False        False       False   \n",
       "364                  False          False        False       False   \n",
       "315                  False          False        False       False   \n",
       "630                  False          False        False       False   \n",
       "\n",
       "     Power Count  \n",
       "150            1  \n",
       "163            4  \n",
       "364            1  \n",
       "315            1  \n",
       "630            7  \n",
       "\n",
       "[5 rows x 178 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "heroes_and_powers_df[heroes_and_powers_df[\"Height\"] < 0].sample(5, random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like not only are those heights negative, those weights are negative also, and all of them are set to exactly -99.0.\n",
    "\n",
    "It seems like this data source probably filled in -99.0 as the height or weight whenever it was unknown, instead of just leaving it as NaN.\n",
    "\n",
    "Depending on the purpose of the analysis, maybe this would be a useful piece of information, but for our current question, let's go ahead and drop the records where the height is -99.0. We'll make a new temporary dataframe to make sure we don't accidentally delete anything that will be needed in a future question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "      <th>...</th>\n",
       "      <th>Reality Warping</th>\n",
       "      <th>Odin Force</th>\n",
       "      <th>Symbiote Costume</th>\n",
       "      <th>Speed Force</th>\n",
       "      <th>Phoenix Force</th>\n",
       "      <th>Molecular Dissipation</th>\n",
       "      <th>Vision - Cryo</th>\n",
       "      <th>Omnipresent</th>\n",
       "      <th>Omniscient</th>\n",
       "      <th>Power Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>Male</td>\n",
       "      <td>yellow</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Icthyo Sapien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Dark Horse Comics</td>\n",
       "      <td>blue</td>\n",
       "      <td>good</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Ungaran</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>red</td>\n",
       "      <td>good</td>\n",
       "      <td>90.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>Male</td>\n",
       "      <td>green</td>\n",
       "      <td>Human / Radiation</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>203.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>441.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Absorbing Man</td>\n",
       "      <td>Male</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>122.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>642</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Strawberry Blond</td>\n",
       "      <td>165.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>52.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Frost Giant</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>304.8</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>white</td>\n",
       "      <td>good</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>Yoda</td>\n",
       "      <td>Male</td>\n",
       "      <td>brown</td>\n",
       "      <td>Yoda's species</td>\n",
       "      <td>White</td>\n",
       "      <td>66.0</td>\n",
       "      <td>George Lucas</td>\n",
       "      <td>green</td>\n",
       "      <td>good</td>\n",
       "      <td>17.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>Female</td>\n",
       "      <td>blue</td>\n",
       "      <td>Human</td>\n",
       "      <td>Black</td>\n",
       "      <td>170.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>good</td>\n",
       "      <td>57.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>-</td>\n",
       "      <td>Brown</td>\n",
       "      <td>185.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>81.0</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>484 rows × 178 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name  Gender Eye color               Race        Hair color  \\\n",
       "0             A-Bomb    Male    yellow              Human           No Hair   \n",
       "1         Abe Sapien    Male      blue      Icthyo Sapien           No Hair   \n",
       "2           Abin Sur    Male      blue            Ungaran           No Hair   \n",
       "3        Abomination    Male     green  Human / Radiation           No Hair   \n",
       "5      Absorbing Man    Male      blue              Human           No Hair   \n",
       "..               ...     ...       ...                ...               ...   \n",
       "642  Yellowjacket II  Female      blue              Human  Strawberry Blond   \n",
       "643             Ymir    Male     white        Frost Giant           No Hair   \n",
       "644             Yoda    Male     brown     Yoda's species             White   \n",
       "645          Zatanna  Female      blue              Human             Black   \n",
       "646             Zoom    Male       red                  -             Brown   \n",
       "\n",
       "     Height          Publisher Skin color Alignment  Weight  ...  \\\n",
       "0     203.0      Marvel Comics          -      good   441.0  ...   \n",
       "1     191.0  Dark Horse Comics       blue      good    65.0  ...   \n",
       "2     185.0          DC Comics        red      good    90.0  ...   \n",
       "3     203.0      Marvel Comics          -       bad   441.0  ...   \n",
       "5     193.0      Marvel Comics          -       bad   122.0  ...   \n",
       "..      ...                ...        ...       ...     ...  ...   \n",
       "642   165.0      Marvel Comics          -      good    52.0  ...   \n",
       "643   304.8      Marvel Comics      white      good   -99.0  ...   \n",
       "644    66.0       George Lucas      green      good    17.0  ...   \n",
       "645   170.0          DC Comics          -      good    57.0  ...   \n",
       "646   185.0          DC Comics          -       bad    81.0  ...   \n",
       "\n",
       "     Reality Warping  Odin Force  Symbiote Costume  Speed Force  \\\n",
       "0              False       False             False        False   \n",
       "1              False       False             False        False   \n",
       "2              False       False             False        False   \n",
       "3              False       False             False        False   \n",
       "5              False       False             False        False   \n",
       "..               ...         ...               ...          ...   \n",
       "642            False       False             False        False   \n",
       "643            False       False             False        False   \n",
       "644            False       False             False        False   \n",
       "645            False       False             False        False   \n",
       "646            False       False             False        False   \n",
       "\n",
       "     Phoenix Force  Molecular Dissipation  Vision - Cryo  Omnipresent  \\\n",
       "0            False                  False          False        False   \n",
       "1            False                  False          False        False   \n",
       "2            False                  False          False        False   \n",
       "3            False                  False          False        False   \n",
       "5            False                  False          False        False   \n",
       "..             ...                    ...            ...          ...   \n",
       "642          False                  False          False        False   \n",
       "643          False                  False          False        False   \n",
       "644          False                  False          False        False   \n",
       "645          False                  False          False        False   \n",
       "646          False                  False          False        False   \n",
       "\n",
       "     Omniscient  Power Count  \n",
       "0         False            7  \n",
       "1         False           16  \n",
       "2         False            1  \n",
       "3         False            8  \n",
       "5         False           11  \n",
       "..          ...          ...  \n",
       "642       False            3  \n",
       "643       False            6  \n",
       "644       False           19  \n",
       "645       False            8  \n",
       "646       False            4  \n",
       "\n",
       "[484 rows x 178 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "question_2_df = heroes_and_powers_df[heroes_and_powers_df[\"Height\"] != -99.0].copy()\n",
    "question_2_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering the Question, Again\n",
    "\n",
    "Now we can redo that plot without those negative heights:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "ax.scatter(x=question_2_df[\"Height\"], y=question_2_df[\"Power Count\"], alpha=0.3)\n",
    "\n",
    "ax.set_xlabel(\"Height (cm)\")\n",
    "ax.set_ylabel(\"Number of Superpowers\")\n",
    "ax.set_title(\"Height vs. Power Count\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, that makes more sense. It looks like there is not much of a relationship between height and number of superpowers.\n",
    "\n",
    "Now we can go on to answering the second half of question 2: *And does this differ based on gender?*\n",
    "\n",
    "To indicate multiple categories within a scatter plot, we can use color to add a third dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(16, 8))\n",
    "\n",
    "# Select subsets\n",
    "question_2_male = question_2_df[question_2_df[\"Gender\"] == \"Male\"]\n",
    "question_2_female = question_2_df[question_2_df[\"Gender\"] == \"Female\"]\n",
    "question_2_other = question_2_df[\n",
    "    (question_2_df[\"Gender\"] != \"Male\") & (question_2_df[\"Gender\"] != \"Female\")\n",
    "]\n",
    "\n",
    "# Plot data with different colors\n",
    "ax.scatter(\n",
    "    x=question_2_male[\"Height\"],\n",
    "    y=question_2_male[\"Power Count\"],\n",
    "    alpha=0.5,\n",
    "    color=\"cyan\",\n",
    "    label=\"Male\",\n",
    ")\n",
    "ax.scatter(\n",
    "    x=question_2_female[\"Height\"],\n",
    "    y=question_2_female[\"Power Count\"],\n",
    "    alpha=0.5,\n",
    "    color=\"gray\",\n",
    "    label=\"Female\",\n",
    ")\n",
    "ax.scatter(\n",
    "    x=question_2_other[\"Height\"],\n",
    "    y=question_2_other[\"Power Count\"],\n",
    "    alpha=0.5,\n",
    "    color=\"yellow\",\n",
    "    label=\"Other\",\n",
    ")\n",
    "\n",
    "# Customize appearance\n",
    "ax.set_xlabel(\"Height (cm)\")\n",
    "ax.set_ylabel(\"Number of Superpowers\")\n",
    "ax.set_title(\"Height vs. Power Count\")\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that there is still no clear relationship between count of powers and height, regardless of gender. We do however note that \"Male\" is the most common gender, and that male superheroes tend to be taller, on average."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Perform Data Aggregation Required to Answer Third Question\n",
    "\n",
    "Recall that the third question is: *What are the 5 most common superpowers in Marvel Comics vs. DC Comics?*\n",
    "\n",
    "We'll need to keep using `heroes_and_powers_df` since we require information from both `heroes_df` and `powers_df`.\n",
    "\n",
    "Your resulting `question_3_df` should contain aggregated data, with columns `Superpower Name`, `Marvel Comics` (containing the count of occurrences in Marvel Comics), and `DC Comics` (containing the count of occurrences in DC Comics). Each row should represent a superpower.\n",
    "\n",
    "In other words, `question_3_df` should look like this:\n",
    "\n",
    "![question 3 df](images/question_3.png)\n",
    "\n",
    "Don't worry if the rows or columns are in a different order, all that matters is that you have the right rows and columns with all the data.\n",
    "\n",
    "***Hint:*** refer to the [documentation for `.groupby`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html) and treat each publisher as a group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "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>Superpower Name</th>\n",
       "      <th>DC Comics</th>\n",
       "      <th>Marvel Comics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agility</td>\n",
       "      <td>71</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Accelerated Healing</td>\n",
       "      <td>51</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lantern Power Ring</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dimensional Awareness</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cold Resistance</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Phoenix Force</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>Molecular Dissipation</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>Vision - Cryo</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>Omnipresent</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Omniscient</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Superpower Name  DC Comics  Marvel Comics\n",
       "0                  Agility         71            126\n",
       "1      Accelerated Healing         51            100\n",
       "2       Lantern Power Ring         11              0\n",
       "3    Dimensional Awareness          7             16\n",
       "4          Cold Resistance         15             25\n",
       "..                     ...        ...            ...\n",
       "162          Phoenix Force          0              1\n",
       "163  Molecular Dissipation          1              0\n",
       "164          Vision - Cryo          1              0\n",
       "165            Omnipresent          0              1\n",
       "166             Omniscient          0              1\n",
       "\n",
       "[167 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here (create more cells as needed)\n",
    "\n",
    "# drop irrelevant rows and columns\n",
    "\n",
    "question_3_df = heroes_and_powers_df.copy()\n",
    "\n",
    "# rows with a relevant publisher\n",
    "question_3_df = question_3_df[\n",
    "    (question_3_df[\"Publisher\"] == \"Marvel Comics\")\n",
    "    | (question_3_df[\"Publisher\"] == \"DC Comics\")\n",
    "]\n",
    "\n",
    "# columns for Publisher and powers\n",
    "relevant_cols = list(powers_df.index)\n",
    "relevant_cols.append(\"Publisher\")\n",
    "question_3_df = question_3_df[relevant_cols]\n",
    "\n",
    "question_3_df\n",
    "#group by publisher\n",
    "question_3_df = question_3_df.groupby(\"Publisher\").sum()\n",
    "question_3_df\n",
    "#transpose for each record to represent a superpower\n",
    "\n",
    "# Now transpose so that each record represents a superpower\n",
    "question_3_df = question_3_df.T\n",
    "question_3_df\n",
    "# reset indexto superpower name\n",
    "\n",
    "question_3_df.index.name = \"Superpower Name\"\n",
    "question_3_df.reset_index(inplace=True)\n",
    "\n",
    "question_3_df.columns.name = None\n",
    "\n",
    "question_3_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code below checks that you have the correct dataframe structure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "# Checking that you made a dataframe called question_3_df\n",
    "assert type(question_3_df) == pd.DataFrame\n",
    "\n",
    "# Checking the shape\n",
    "assert question_3_df.shape == (167, 3)\n",
    "\n",
    "# Checking the column names\n",
    "assert sorted(list(question_3_df.columns)) == [\n",
    "    \"DC Comics\",\n",
    "    \"Marvel Comics\",\n",
    "    \"Superpower Name\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Answering the Question\n",
    "\n",
    "The code below uses the dataframe you created to find and plot the most common superpowers in Marvel Comics and DC Comics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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>Superpower Name</th>\n",
       "      <th>Marvel Comics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Super Strength</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Durability</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Stamina</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Super Speed</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agility</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Superpower Name  Marvel Comics\n",
       "17  Super Strength            204\n",
       "5       Durability            154\n",
       "25         Stamina            150\n",
       "30     Super Speed            137\n",
       "0          Agility            126"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "marvel_most_common = question_3_df.drop(\"DC Comics\", axis=1)\n",
    "marvel_most_common = marvel_most_common.sort_values(\n",
    "    by=\"Marvel Comics\", ascending=False\n",
    ")[:5]\n",
    "marvel_most_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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>Superpower Name</th>\n",
       "      <th>DC Comics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Super Strength</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Stamina</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Flight</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Super Speed</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Durability</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Superpower Name  DC Comics\n",
       "17  Super Strength        109\n",
       "25         Stamina         90\n",
       "8           Flight         86\n",
       "30     Super Speed         79\n",
       "5       Durability         71"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "dc_most_common = question_3_df.drop(\"Marvel Comics\", axis=1)\n",
    "dc_most_common = dc_most_common.sort_values(by=\"DC Comics\", ascending=False)[:5]\n",
    "dc_most_common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PB9yuu/WcSJ6XOfKK/CU1NVUdO3aUl5eXvv/+e3300UeZ/l83evRoDR8+XHv37lXFihXVo0cPXb16NdP8Oi+6W89pafJrnsffpnlMLhf27lifffaZKVq0qPHw8DCNGzc2o0aNMj/++KNtuiNV4yVLltj6nDlzxnh6epqlS5caY4x5/PHHzVNPPWW37m+++cYUKFDAXLx40RhzrYLcsWPH247ZGPtKc5qxY8caNzc3c/r0aVvbpk2bjK+vr7l06ZJd33LlypkPP/zQNp+Xl5fdr2kvvfSSadCggTHm2q+Ybm5u5tNPP7VNP3v2rPHy8rrpL0zGXPslT5LdL3HvvfeeCQwMvOV2uJWb/frVrVs3U6VKFYf274oVK265zqpVq5pp06bZxsuUKWNat26dbt2RkZG2cWXxVwFj0v/ymSYyMtI888wztvGhQ4eaiIiIW8abF9zqeM7oWM5IRtv+scces43HxsYaSXa/lGzbts1IMrGxscaYjH9Zvtmxn5HTp08bSWbfvn23jDk73M3ntKefftqurUGDBrbjfubMmaZSpUomNTXVNj05Odl4enqa9evXG2OMCQ0NNYsWLbJbxr/+9S/TqFEjY4wxH374ofH39zfnz5+3TZ8xY0aevWKtcePGZurUqcYYY65cuWICAgJsv0y+/PLL6X4tHD16tNNXrBmT/qoBY4z5888/TcGCBc33339vjDHm8uXLpnjx4mbOnDmu+ZDAbbqbz4n5Pc/LDHnFnadPnz6mYMGCxtvb2zY8+uijxpibX7H25ZdfmkKFCtm2tzEm0yvWPvnkE1ufAwcOGEnm4MGDxpjM8+u86G4+p93teR5/m94Zf5tyxZqTOnfurD///FMrV65Uq1attGXLFtWpU8fugc1Z1ahRI9u//f39ValSJVuVfdeuXZozZ46tal+4cGG1atVKqampOnr0qG2+evXqZWvMZcqUsXs2wa5du5SUlKRixYrZxXb06FH99ttvtn5hYWHy8fGxjQcHB+v06dOSpN9//11XrlxR/fr1bdP9/PxUqVKlW8YjSV5eXnZXNly/7OxijJHFYnFonhv3zfnz5zVixAhVrVpVRYoUUeHChfXLL7+k+1Xg+uMibTztuHCVAQMGaPHixbp06ZKuXLmihQsXql+/fi5dR3Zx5njO6ravUaOG7d+BgYGSpOrVq6dru9nxdrNjX7r2y3PPnj11zz33yNfXV2XLlpWkdLHklLv5nHaz79KuXbt05MgR+fj42OLx9/fXpUuX9Ntvv+nvv//WyZMn1b9/f7uYx48fbzvXHTx4UDVr1pSXl1em68wrDh06pB9++EHdu3eXJBUqVEjdunXTrFmzbNPvvfdeu3muP0e7SnBwsNq2bWtb7+rVq3Xp0iV16dLF5esCnHE3nxMzQp5HXnGnatq0qfbu3Wsb/v3vf99ynkOHDik0NNTumU+Z/V93/b4LDg6WdPP9lFfdzee0/Jzn8bdp3sHLC26Dh4eHWrRooRYtWujVV1/Vk08+qbFjx6pv374qUOBazdJcd0l32iWyWZH2BUlNTdXAgQP13HPPpetz/cOmvb29bzvmm7lx+ampqQoODtaWLVvS9b3+DR83PvzWYrHYLklP2zY3ngyu32Y3k9Gyszqvsw4ePKiyZcs6tH9v3HYvvfSS1q9fr7ffflvly5eXp6enHn30UdvlyDfj6InzVtq3by+r1arly5fLarUqOTlZnTt3duk6spOjx3NWt/31x1baNs+o7Wa3V9zs2JeubfvQ0FB9/PHHCgkJUWpqqsLDw7N0HGSX/HROuz6eunXrauHChen6FC9eXJcuXZJ07TaBBg0a2E0vWLCgpKyfs/KCmTNn6urVqypZsqStzRgjNzc3xcfHZ5igZdfne/LJJ/X4449rypQpmj17trp162aXtAK5LT+dE8nzriGvuPN4e3urfPnyDs3jSDHC0f2Ul+Wnc1p+yfP42zTvoLDmQlWrVrW9hjvtV7/Y2FjVrl1bkmzPrLjR9u3bbSea+Ph4/frrr6pcubIkqU6dOjpw4IDD/2E4E7N07T+PlJSUW85Xp04dxcXFqVChQgoLC3Nq3eXKlZObm5t++OEHhYaGSpISExN1+PBhNWnSxNbP3d09SzFlt82bN2vfvn164YUXHNq/N/rmm2/Ut29fPfLII5KuPZ/j2LFj6fpt37493XjaceGozLZhoUKF1KdPH82ePVtWq1Xdu3e/o/+wvf54zuhYzuq2z25nzpzRwYMH9eGHH+qBBx6QJH377bc5Hset3A3ntLR4evfubTee9hnq1KmjpUuXqkSJEvL19U23PD8/P5UsWVK///67evXqlek658+fr4sXL9re4nbj9zcvuHr1qubNm6d33nlHLVu2tJvWuXNnLVy4UJUrV9batWvtpu3cufO21pvZ+adNmzby9vbWjBkz9OWXX+rrr7++rfUA2e1uOCeS5zmGvOLuVLlyZZ04cUJ//fWX7WrB65/rlFV5+djNirvhnJYWT37M8/jbNG+hsOaEM2fOqEuXLurXr59q1KghHx8f7dy5U5MmTdLDDz8s6dprshs2bKg33nhDYWFh+u9//6tXXnklw+W99tprKlasmAIDAzV69GgFBASoY8eOkqSXX35ZDRs21LPPPqsBAwbI29tbBw8eVHR0tKZNm+bSmKVrl5lv2rRJ9913n6xWq4oWLZrh8po3b65GjRqpY8eOevPNN1WpUiX9+eefWrt2rTp27Jily3t9fHzUp08fvfTSS/L391eJEiU0duxYFShQwK76HRYWpq+//lrdu3eX1WpVQEBAlj+3s5KTkxUXF2f3SuOJEyeqXbt26t27twoWLJjl/Xuj8uXLa9myZWrfvr0sFovGjBmT4S9f3333nSZNmqSOHTsqOjpan376qdasWePU5wkLC1NSUpI2bdpku5Q57ST15JNPqkqVKrZ13gmycjxndCxnddtnt6JFi6pYsWL66KOPFBwcrBMnTmjkyJE5Hkeau/mcJkmffvqp6tWrp/vvv18LFy7UDz/8oJkzZ0qSevXqpbfeeksPP/ywXnvtNZUqVUonTpzQsmXL9NJLL6lUqVKKiorSc889J19fX0VGRio5OVk7d+5UfHy8hg0bpp49e2r06NHq37+/XnnlFR07dkxvv/22E3sie61evVrx8fHq37+//Pz87KY9+uijmjlzppYtW6bJkyfr5ZdfVv/+/bV3717bLRfO/ioZFham77//XseOHbPdglGgQAEVLFhQffv21ahRo1S+fPk8c1sFcDefE8nzMkZekb+0aNFC5cqVU58+fTRp0iSdO3fO9vICR/6vu1l+nZfczec0KX/kefxtegfI2Ue63R0uXbpkRo4caerUqWP8/PyMl5eXqVSpknnllVfMhQsXbP1+/vln07BhQ+Pp6Wlq1aplNmzYkOEDBFetWmWqVatm3N3dzb333mv3KnNjjPnhhx9MixYtTOHChY23t7epUaOGef31123TM3roq7Mxr1y50pQvX94UKlQo3WvYb5SYmGiGDBliQkJCjJubmwkNDTW9evUyJ06cyHS+KVOm2JabtowbX8Nev359M3LkSFufbdu22V7pnHbIZvTAw+XLlxtXHNJ9+vSxvfq3UKFCpnjx4qZ58+Zm1qxZJiUlxdYvq/s37SGNaY4ePWqaNm1qPD09TWhoqJk+fXqGD1gdN26c6dq1q/Hy8jKBgYG2h42nkQMPiDTGmKefftoUK1Ysw9eBP/DAA6Zq1aq3s9lyVFaO54yO5axu+xu/T9dv67TlXL+9b/Uwd2PSH/vR0dGmSpUqxmq1mho1apgtW7Zk+cHIrnY3n9Mkmffee8+0aNHCWK1WU6ZMGbN48WK7ZcXGxprevXubgIAAY7VazT333GMGDBhgEhISbH0WLlxoatWqZdzd3U3RokXNgw8+aJYtW2abvm3bNlOzZk3j7u5uatWqZT7//PM89/KCdu3amTZt2mQ4bdeuXUaS2bVrl/niiy9M+fLljdVqNREREbYH9KY9dNjRlxccOnTIdtxIsnvF/W+//WYkmUmTJrnyowK35W4+J5LnZYy84s50s4e632o/HDx40Nx3333G3d3dVK5c2axatcpIMuvWrTPGZO1h/sbcPL/OK+7mc1p+yPP42/TOYDEmD980fJfbsmWLmjZtqvj4eLvnVeRn58+fV8mSJfXOO++of//+uR1OvmCMUeXKlTVw4EANGzYst8PBHSwvntMsFouWL19u+yUVjnv99df1wQcf6OTJky5f9nfffaeIiAidOnXKdjsOcLfIi+fE3Eaeh7zqu+++0/33368jR47YvTQD/5MXz2nkeXefO/VvU24FRa7as2ePfvnlF9WvX18JCQl67bXXJCndJb7IHqdPn9b8+fP1xx9/6IknnsjtcADkAe+//77uvfdeFStWTN99953eeustDR482KXrSE5O1smTJzVmzBh17dqVohpwlyLPQ161fPlyFS5cWBUqVNCRI0f0/PPP67777qOoBuSiO/lvUwpryHVvv/22Dh06JHd3d9WtW1fffPNNrjxfIz8KDAxUQECAPvroo0yfswIgfzl8+LDGjx+vf/75R6VLl9aLL76oUaNGuXQdixcvVv/+/VWrVi3Nnz/fpcsGkLeQ5yEvOnfunEaMGKGTJ08qICBAzZs31zvvvJPbYQH52p38tym3ggIAAAAAAABOKJDbAQAAAAAAAAB3IgprAAAAAAAAgBMorAEAAAAAAABOoLAGAAAAAAAAOIHCGgAAAAAAAOAECmsAAAAAAACAEyisAQAAAAAAAE6gsAYAAAAAAAA4gcIaAAAAAAAA4IT/BxGNPsgr2kzeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell without changes\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 5))\n",
    "\n",
    "ax1.bar(\n",
    "    x=marvel_most_common[\"Superpower Name\"], height=marvel_most_common[\"Marvel Comics\"]\n",
    ")\n",
    "ax2.bar(x=dc_most_common[\"Superpower Name\"], height=dc_most_common[\"DC Comics\"])\n",
    "\n",
    "ax1.set_ylabel(\"Count of Superheroes\")\n",
    "ax2.set_ylabel(\"Count of Superheroes\")\n",
    "ax1.set_title(\"Frequency of Top Superpowers in Marvel Comics\")\n",
    "ax2.set_title(\"Frequency of Top Superpowers in DC Comics\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like super strength is the most popular power in both Marvel Comics and DC Comics. Overall, the top 5 powers are fairly similar — 4 out of 5 overlap, although Marvel contains agility whereas DC contains flight."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Formulate and Answer Your Own Question\n",
    "\n",
    "For the remainder of this lab, you'll be focusing on coming up with and answering your own question, just like we did above.  Your question should not be overly simple, and should require both descriptive statistics and data visualization to answer.  In case you're unsure of what questions to ask, some sample questions have been provided below.\n",
    "\n",
    "Pick one of the following questions to investigate and answer, or come up with one of your own!\n",
    "\n",
    "* Which powers have the highest chance of co-occurring in a hero (e.g. super strength and flight)?\n",
    "* What is the distribution of skin colors amongst alien heroes?\n",
    "* How are eye color and hair color related in this dataset?\n",
    "\n",
    "Explain your question below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace None with appropriate text:\n",
    "\"\"\"\n",
    " I choose to investigate the distribution of skin colors amongst alien heroes to give \n",
    " insights into the diversity of alien characters in the superhero universe.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some sample cells have been provided to give you room to work. Feel free to create more cells as needed.\n",
    "\n",
    "Be sure to include thoughtful, well-labeled visualizations to back up your analysis!\n",
    "\n",
    "(There is no solution branch for this part, and feel free to move on to the next lesson if you have already spent more than 90 minutes.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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>name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Eye color</th>\n",
       "      <th>Race</th>\n",
       "      <th>Hair color</th>\n",
       "      <th>Height</th>\n",
       "      <th>Publisher</th>\n",
       "      <th>Skin color</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Ardina</td>\n",
       "      <td>Female</td>\n",
       "      <td>white</td>\n",
       "      <td>Alien</td>\n",
       "      <td>Orange</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>gold</td>\n",
       "      <td>good</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>110</th>\n",
       "      <td>Blackwulf</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>Alien</td>\n",
       "      <td>White</td>\n",
       "      <td>188.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>Century</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Alien</td>\n",
       "      <td>White</td>\n",
       "      <td>201.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>grey</td>\n",
       "      <td>good</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>Doomsday</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>Alien</td>\n",
       "      <td>White</td>\n",
       "      <td>244.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>bad</td>\n",
       "      <td>412.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>Evilhawk</td>\n",
       "      <td>Male</td>\n",
       "      <td>red</td>\n",
       "      <td>Alien</td>\n",
       "      <td>Black</td>\n",
       "      <td>191.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>green</td>\n",
       "      <td>bad</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>Indigo</td>\n",
       "      <td>Female</td>\n",
       "      <td>-</td>\n",
       "      <td>Alien</td>\n",
       "      <td>Purple</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>DC Comics</td>\n",
       "      <td>-</td>\n",
       "      <td>neutral</td>\n",
       "      <td>-99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>Silver Surfer</td>\n",
       "      <td>Male</td>\n",
       "      <td>white</td>\n",
       "      <td>Alien</td>\n",
       "      <td>No Hair</td>\n",
       "      <td>193.0</td>\n",
       "      <td>Marvel Comics</td>\n",
       "      <td>silver</td>\n",
       "      <td>good</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              name  Gender Eye color   Race Hair color  Height      Publisher  \\\n",
       "41          Ardina  Female     white  Alien     Orange   193.0  Marvel Comics   \n",
       "110      Blackwulf    Male       red  Alien      White   188.0  Marvel Comics   \n",
       "166        Century    Male     white  Alien      White   201.0  Marvel Comics   \n",
       "229       Doomsday    Male       red  Alien      White   244.0      DC Comics   \n",
       "247       Evilhawk    Male       red  Alien      Black   191.0  Marvel Comics   \n",
       "341         Indigo  Female         -  Alien     Purple   -99.0      DC Comics   \n",
       "600  Silver Surfer    Male     white  Alien    No Hair   193.0  Marvel Comics   \n",
       "\n",
       "    Skin color Alignment  Weight  \n",
       "41        gold      good    98.0  \n",
       "110          -         -    88.0  \n",
       "166       grey      good    97.0  \n",
       "229          -       bad   412.0  \n",
       "247      green       bad   106.0  \n",
       "341          -   neutral   -99.0  \n",
       "600     silver      good   101.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset to include only alien heroes\n",
    "alien_heroes_df = heroes_df[heroes_df[\"Race\"] == \"Alien\"]\n",
    "alien_heroes_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Skin color\n",
       "-         3\n",
       "gold      1\n",
       "grey      1\n",
       "green     1\n",
       "silver    1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the unique values of skin colors among alien heroes\n",
    "skin_color_distribution = alien_heroes_df[\"Skin color\"].value_counts()\n",
    "skin_color_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the distribution of skin colors\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.barplot(x=skin_color_distribution.index, y=skin_color_distribution.values, palette=\"viridis\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Skin Color\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title(\"Distribution of Skin Colors Amongst Alien Heroes\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "In this lab, you demonstrated your mastery of using pandas to clean and aggregate data in order to answer several business questions. This included identifying and handling missing values, text requiring preprocessing, and invalid values. You also performed aggregation and reshaping tasks such as transposing, joining, and grouping data. Great job, there was a lot here!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "vscode": {
   "interpreter": {
    "hash": "7525ddc99344d304bee386e4294f627a69ef3ddf90a93199e89e426185c49e1b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}