Exploratory Data Analysis on Marvel and DC Characters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2741d81b",
   "metadata": {},
   "source": [
    "# DATASET OVERVIEW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "817423e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading the required liabries.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52eb3c37",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>good</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>84</td>\n",
       "      <td>65</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>bad</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>55</td>\n",
       "      <td>95</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>27</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>28</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>good</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>bad</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>28</td>\n",
       "      <td>72</td>\n",
       "      <td>28</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>611 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Alignment  Intelligence  Strength  Speed  Durability  \\\n",
       "0            3-D Man      good            50        31     43          32   \n",
       "1             A-Bomb      good            38       100     17          80   \n",
       "2         Abe Sapien      good            88        14     35          42   \n",
       "3           Abin Sur      good            50        90     53          64   \n",
       "4        Abomination       bad            63        80     53          90   \n",
       "..               ...       ...           ...       ...    ...         ...   \n",
       "606     Yellowjacket      good            88        10     12          28   \n",
       "607  Yellowjacket II      good            50        10     35          28   \n",
       "608             Ymir      good            50       100     27         100   \n",
       "609          Zatanna      good            75        10     23          28   \n",
       "610             Zoom       bad            50        10    100          28   \n",
       "\n",
       "     Power  Combat  Total  \n",
       "0       25      52    233  \n",
       "1       17      64    316  \n",
       "2       35      85    299  \n",
       "3       84      65    406  \n",
       "4       55      95    436  \n",
       "..     ...     ...    ...  \n",
       "606     12      14    164  \n",
       "607     31      28    182  \n",
       "608     83      28    388  \n",
       "609    100      56    292  \n",
       "610     72      28    288  \n",
       "\n",
       "[611 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the marvel dataset.\n",
    "marvel=pd.read_csv(\"C:\\\\Users\\\\abc\\\\Desktop\\\\charcters_stats.csv\")\n",
    "marvel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2090654f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>good</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>299</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>84</td>\n",
       "      <td>65</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>bad</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>55</td>\n",
       "      <td>95</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "0      3-D Man      good            50        31     43          32     25   \n",
       "1       A-Bomb      good            38       100     17          80     17   \n",
       "2   Abe Sapien      good            88        14     35          42     35   \n",
       "3     Abin Sur      good            50        90     53          64     84   \n",
       "4  Abomination       bad            63        80     53          90     55   \n",
       "\n",
       "   Combat  Total  \n",
       "0      52    233  \n",
       "1      64    316  \n",
       "2      85    299  \n",
       "3      65    406  \n",
       "4      95    436  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 records of the dataset.\n",
    "marvel.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ddc5b3b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(611, 9)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the number of rows and columns in the dataset.\n",
    "marvel.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88711535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Name', 'Alignment', 'Intelligence', 'Strength', 'Speed', 'Durability',\n",
       "       'Power', 'Combat', 'Total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Names of all the columns.\n",
    "marvel.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11f3c2cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name\n",
      "Alignment\n",
      "Intelligence\n",
      "Strength\n",
      "Speed\n",
      "Durability\n",
      "Power\n",
      "Combat\n",
      "Total\n"
     ]
    }
   ],
   "source": [
    "# Names of all the columns.\n",
    "for i in marvel.columns:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "acfb4b89",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "      <td>611.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>44.502455</td>\n",
       "      <td>29.083470</td>\n",
       "      <td>27.306056</td>\n",
       "      <td>41.839607</td>\n",
       "      <td>40.312602</td>\n",
       "      <td>43.206219</td>\n",
       "      <td>226.250409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>32.924722</td>\n",
       "      <td>32.610939</td>\n",
       "      <td>25.760173</td>\n",
       "      <td>36.543849</td>\n",
       "      <td>34.539910</td>\n",
       "      <td>33.270329</td>\n",
       "      <td>167.324034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>255.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>351.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>581.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Intelligence    Strength       Speed  Durability       Power  \\\n",
       "count    611.000000  611.000000  611.000000  611.000000  611.000000   \n",
       "mean      44.502455   29.083470   27.306056   41.839607   40.312602   \n",
       "std       32.924722   32.610939   25.760173   36.543849   34.539910   \n",
       "min        1.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "25%        1.000000    1.000000    1.000000    1.000000    0.000000   \n",
       "50%       50.000000   10.000000   23.000000   32.000000   37.000000   \n",
       "75%       75.000000   53.000000   42.000000   80.000000   67.000000   \n",
       "max      113.000000  100.000000  100.000000  120.000000  100.000000   \n",
       "\n",
       "           Combat       Total  \n",
       "count  611.000000  611.000000  \n",
       "mean    43.206219  226.250409  \n",
       "std     33.270329  167.324034  \n",
       "min      1.000000    5.000000  \n",
       "25%      1.000000    5.000000  \n",
       "50%     50.000000  255.000000  \n",
       "75%     70.000000  351.500000  \n",
       "max    101.000000  581.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Basic stats about the dataset.\n",
    "marvel.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4ac2b8",
   "metadata": {},
   "source": [
    "# DATA CLEANING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9c0acb4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 611 entries, 0 to 610\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Name          611 non-null    object\n",
      " 1   Alignment     608 non-null    object\n",
      " 2   Intelligence  611 non-null    int64 \n",
      " 3   Strength      611 non-null    int64 \n",
      " 4   Speed         611 non-null    int64 \n",
      " 5   Durability    611 non-null    int64 \n",
      " 6   Power         611 non-null    int64 \n",
      " 7   Combat        611 non-null    int64 \n",
      " 8   Total         611 non-null    int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 43.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Datatypes of all the columns of the dataset.\n",
    "marvel.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "56710cca",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>611 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "0    False      False         False     False  False       False  False   \n",
       "1    False      False         False     False  False       False  False   \n",
       "2    False      False         False     False  False       False  False   \n",
       "3    False      False         False     False  False       False  False   \n",
       "4    False      False         False     False  False       False  False   \n",
       "..     ...        ...           ...       ...    ...         ...    ...   \n",
       "606  False      False         False     False  False       False  False   \n",
       "607  False      False         False     False  False       False  False   \n",
       "608  False      False         False     False  False       False  False   \n",
       "609  False      False         False     False  False       False  False   \n",
       "610  False      False         False     False  False       False  False   \n",
       "\n",
       "     Combat  Total  \n",
       "0     False  False  \n",
       "1     False  False  \n",
       "2     False  False  \n",
       "3     False  False  \n",
       "4     False  False  \n",
       "..      ...    ...  \n",
       "606   False  False  \n",
       "607   False  False  \n",
       "608   False  False  \n",
       "609   False  False  \n",
       "610   False  False  \n",
       "\n",
       "[611 rows x 9 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check all the null values in dataset.\n",
    "marvel.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0b06afcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            0\n",
       "Alignment       3\n",
       "Intelligence    0\n",
       "Strength        0\n",
       "Speed           0\n",
       "Durability      0\n",
       "Power           0\n",
       "Combat          0\n",
       "Total           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum all the null values in the dataset.\n",
    "marvel.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "07055cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "606    False\n",
       "607    False\n",
       "608    False\n",
       "609    False\n",
       "610    False\n",
       "Name: Alignment, Length: 611, dtype: bool"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Null values in the Alignment column of the dataset.\n",
    "marvel.Alignment.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01dbf78a",
   "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>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Anti-Venom</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>90</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "      <td>459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Blackwulf</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>28</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>59</td>\n",
       "      <td>25</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Trickster</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "28   Anti-Venom       NaN            75        60     65          90     85   \n",
       "98    Blackwulf       NaN            50        28      8          30     59   \n",
       "560   Trickster       NaN             1         1      1           1      0   \n",
       "\n",
       "     Combat  Total  \n",
       "28       84    459  \n",
       "98       25    200  \n",
       "560       1      5  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset where Alignment column has nulll values.\n",
    "marvel[marvel.Alignment.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d13ed0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "good       432\n",
       "bad        165\n",
       "neutral     11\n",
       "Name: Alignment, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# types and number of alignment of characters.\n",
    "marvel.Alignment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "78132529",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace all null values in the Alignment column.\n",
    "marvel.fillna({'Alignment':'good'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f93705f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name            0\n",
       "Alignment       0\n",
       "Intelligence    0\n",
       "Strength        0\n",
       "Speed           0\n",
       "Durability      0\n",
       "Power           0\n",
       "Combat          0\n",
       "Total           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum all the null values in the dataset.\n",
    "marvel.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2dbb509",
   "metadata": {},
   "source": [
    "# Superheroes with the highest speed ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9d2101dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "good       435\n",
       "bad        165\n",
       "neutral     11\n",
       "Name: Alignment, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# types and number of alignment of characters.\n",
    "marvel.Alignment.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7dbb8e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>good</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>84</td>\n",
       "      <td>65</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>good</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>320</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>95</td>\n",
       "      <td>92</td>\n",
       "      <td>84</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "      <td>28</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>100</td>\n",
       "      <td>27</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>28</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>good</td>\n",
       "      <td>75</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Alignment  Intelligence  Strength  Speed  Durability  \\\n",
       "0            3-D Man      good            50        31     43          32   \n",
       "1             A-Bomb      good            38       100     17          80   \n",
       "2         Abe Sapien      good            88        14     35          42   \n",
       "3           Abin Sur      good            50        90     53          64   \n",
       "6        Adam Monroe      good            63        10     12         100   \n",
       "..               ...       ...           ...       ...    ...         ...   \n",
       "604            X-Man      good            88        53     53          95   \n",
       "606     Yellowjacket      good            88        10     12          28   \n",
       "607  Yellowjacket II      good            50        10     35          28   \n",
       "608             Ymir      good            50       100     27         100   \n",
       "609          Zatanna      good            75        10     23          28   \n",
       "\n",
       "     Power  Combat  Total  \n",
       "0       25      52    233  \n",
       "1       17      64    316  \n",
       "2       35      85    299  \n",
       "3       84      65    406  \n",
       "6       71      64    320  \n",
       "..     ...     ...    ...  \n",
       "604     92      84    465  \n",
       "606     12      14    164  \n",
       "607     31      28    182  \n",
       "608     83      28    388  \n",
       "609    100      56    292  \n",
       "\n",
       "[435 rows x 9 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset.\n",
    "good=marvel[marvel['Alignment']=='good']\n",
    "good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02a6d290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>52</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>good</td>\n",
       "      <td>38</td>\n",
       "      <td>100</td>\n",
       "      <td>17</td>\n",
       "      <td>80</td>\n",
       "      <td>17</td>\n",
       "      <td>64</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>good</td>\n",
       "      <td>88</td>\n",
       "      <td>14</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>good</td>\n",
       "      <td>50</td>\n",
       "      <td>90</td>\n",
       "      <td>53</td>\n",
       "      <td>64</td>\n",
       "      <td>84</td>\n",
       "      <td>65</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>good</td>\n",
       "      <td>63</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>100</td>\n",
       "      <td>71</td>\n",
       "      <td>64</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "0      3-D Man      good            50        31     43          32     25   \n",
       "1       A-Bomb      good            38       100     17          80     17   \n",
       "2   Abe Sapien      good            88        14     35          42     35   \n",
       "3     Abin Sur      good            50        90     53          64     84   \n",
       "6  Adam Monroe      good            63        10     12         100     71   \n",
       "\n",
       "   Combat  Total  \n",
       "0      52    233  \n",
       "1      64    316  \n",
       "2      85    299  \n",
       "3      65    406  \n",
       "6      64    320  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Glance at the top 5 records of the superheroes.\n",
    "good.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "44a4de6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>3</th>\n",
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       "      <td>53</td>\n",
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       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
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       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
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       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
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       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>23</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Speed\n",
       "0            3-D Man     43\n",
       "1             A-Bomb     17\n",
       "2         Abe Sapien     35\n",
       "3           Abin Sur     53\n",
       "6        Adam Monroe     12\n",
       "..               ...    ...\n",
       "604            X-Man     53\n",
       "606     Yellowjacket     12\n",
       "607  Yellowjacket II     35\n",
       "608             Ymir     27\n",
       "609          Zatanna     23\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a dataset from the good dataset which tells us about the Name and Speed of the superheroes.\n",
    "good_speed=good[['Name','Speed']]\n",
    "good_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "be8e8fc7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>...</th>\n",
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       "      <th>445</th>\n",
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       "      <th>127</th>\n",
       "      <td>Bumbleboy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Brother Voodoo</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>Garbage Man</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Guardian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Speed\n",
       "447     Quicksilver    100\n",
       "295         Impulse    100\n",
       "333          Krypto    100\n",
       "85       Black Bolt    100\n",
       "525        Stardust    100\n",
       "..              ...    ...\n",
       "445         Quantum      1\n",
       "127       Bumbleboy      1\n",
       "124  Brother Voodoo      1\n",
       "240     Garbage Man      1\n",
       "264        Guardian      1\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their speed.\n",
    "good_speed=good_speed.sort_values(by='Speed',ascending=False)\n",
    "good_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0bc6cd69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "            Name  Speed\n",
       "447  Quicksilver    100\n",
       "295      Impulse    100\n",
       "333       Krypto    100\n",
       "85    Black Bolt    100\n",
       "525     Stardust    100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest speed.\n",
    "good_speed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94704664",
   "metadata": {},
   "source": [
    "# Superheroes with the highest Intelligence ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6e81209e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>606</th>\n",
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       "      <td>88</td>\n",
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       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
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       "      <td>Ymir</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Intelligence\n",
       "0            3-D Man            50\n",
       "1             A-Bomb            38\n",
       "2         Abe Sapien            88\n",
       "3           Abin Sur            50\n",
       "6        Adam Monroe            63\n",
       "..               ...           ...\n",
       "604            X-Man            88\n",
       "606     Yellowjacket            88\n",
       "607  Yellowjacket II            50\n",
       "608             Ymir            50\n",
       "609          Zatanna            75\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Intelligence.\n",
    "good_intelligence=good[['Name','Intelligence']]\n",
    "good_intelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ad937c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Martian Manhunter</td>\n",
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       "      <th>589</th>\n",
       "      <td>Watcher</td>\n",
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       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Jean Grey</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Doctor Strange</td>\n",
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       "      <th>262</th>\n",
       "      <td>Green Goblin IV</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>Green Goblin III</td>\n",
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       "      <th>47</th>\n",
       "      <td>Atom IV</td>\n",
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       "      <td>Meteorite</td>\n",
       "      <td>1</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Intelligence\n",
       "361  Martian Manhunter           100\n",
       "589            Watcher           100\n",
       "306          Jean Grey           100\n",
       "198     Doctor Strange           100\n",
       "440        Professor X           100\n",
       "..                 ...           ...\n",
       "264           Guardian             1\n",
       "262    Green Goblin IV             1\n",
       "261   Green Goblin III             1\n",
       "47             Atom IV             1\n",
       "376          Meteorite             1\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Intelligence.\n",
    "good_intelligence=good_intelligence.sort_values(by='Intelligence',ascending=False)\n",
    "good_intelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "77677db3",
   "metadata": {},
   "outputs": [
    {
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       "\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>Intelligence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Martian Manhunter</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>589</th>\n",
       "      <td>Watcher</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>Jean Grey</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>Doctor Strange</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>Professor X</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Intelligence\n",
       "361  Martian Manhunter           100\n",
       "589            Watcher           100\n",
       "306          Jean Grey           100\n",
       "198     Doctor Strange           100\n",
       "440        Professor X           100"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest intelligence.\n",
    "good_intelligence.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15e44d4c",
   "metadata": {},
   "source": [
    "# Superheroes with the highest Strength ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2c87415a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Strength\n",
       "0            3-D Man        31\n",
       "1             A-Bomb       100\n",
       "2         Abe Sapien        14\n",
       "3           Abin Sur        90\n",
       "6        Adam Monroe        10\n",
       "..               ...       ...\n",
       "604            X-Man        53\n",
       "606     Yellowjacket        10\n",
       "607  Yellowjacket II        10\n",
       "608             Ymir       100\n",
       "609          Zatanna        10\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Strength.\n",
    "good_strength=good[['Name','Strength']]\n",
    "good_strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a76a72b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>Wonder Woman</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>Hulk</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>Silver Surfer</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Superman</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Thor</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Psylocke</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Quantum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>Danny Cooper</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>Cypher</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>Colossal Boy</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name  Strength\n",
       "600   Wonder Woman       100\n",
       "288           Hulk       100\n",
       "499  Silver Surfer       100\n",
       "537       Superman       100\n",
       "546           Thor       100\n",
       "..             ...       ...\n",
       "442       Psylocke         1\n",
       "445        Quantum         1\n",
       "175   Danny Cooper         1\n",
       "173         Cypher         1\n",
       "162   Colossal Boy         1\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Strength.\n",
    "good_strength=good_strength.sort_values(by='Strength',ascending=False)\n",
    "good_strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a8781d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>600</th>\n",
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       "      <th>499</th>\n",
       "      <td>Silver Surfer</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Superman</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Thor</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Name  Strength\n",
       "600   Wonder Woman       100\n",
       "288           Hulk       100\n",
       "499  Silver Surfer       100\n",
       "537       Superman       100\n",
       "546           Thor       100"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest Strength.\n",
    "good_strength.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c974e744",
   "metadata": {},
   "source": [
    "# Who is the most durable superhero ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d7fca80f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Name</th>\n",
       "      <th>Durability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>42</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>64</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>95</td>\n",
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       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Durability\n",
       "0            3-D Man          32\n",
       "1             A-Bomb          80\n",
       "2         Abe Sapien          42\n",
       "3           Abin Sur          64\n",
       "6        Adam Monroe         100\n",
       "..               ...         ...\n",
       "604            X-Man          95\n",
       "606     Yellowjacket          28\n",
       "607  Yellowjacket II          28\n",
       "608             Ymir         100\n",
       "609          Zatanna          28\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Durability.\n",
    "good_durability=good[['Name','Durability']]\n",
    "good_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7107b1fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name</th>\n",
       "      <th>Durability</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Stardust</td>\n",
       "      <td>110</td>\n",
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       "      <th>499</th>\n",
       "      <td>Silver Surfer</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Nova</td>\n",
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       "    <tr>\n",
       "      <th>185</th>\n",
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       "      <th>...</th>\n",
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       "      <th>466</th>\n",
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       "    <tr>\n",
       "      <th>468</th>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>Liz Sherman</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>Giant-Man</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Howard the Duck</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Durability\n",
       "525         Stardust         110\n",
       "499    Silver Surfer         101\n",
       "417             Nova         101\n",
       "185          Deadman         100\n",
       "267          Hancock         100\n",
       "..               ...         ...\n",
       "466          Ripcord           1\n",
       "468         Robin II           1\n",
       "343      Liz Sherman           1\n",
       "246        Giant-Man           1\n",
       "287  Howard the Duck           1\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Durability.\n",
    "good_durability=good_durability.sort_values(by='Durability',ascending=False)\n",
    "good_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "adcc75e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>Stardust</td>\n",
       "      <td>110</td>\n",
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       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>Nova</td>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>Deadman</td>\n",
       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>Hancock</td>\n",
       "      <td>100</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "              Name  Durability\n",
       "525       Stardust         110\n",
       "499  Silver Surfer         101\n",
       "417           Nova         101\n",
       "185        Deadman         100\n",
       "267        Hancock         100"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest Durability.\n",
    "good_durability=good_durability.head()\n",
    "good_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0afee4e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Stardust', 'Silver Surfer', 'Nova', 'Deadman', 'Hancock']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the 5 most durable superheroes.\n",
    "x=list(good_durability['Name'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c5dcb69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[110, 101, 101, 100, 100]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the durability of the top 5 superheroes.\n",
    "y=list(good_durability['Durability'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6c22e709",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 superheroes with highest Durability..\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['red','yellow','green','black','pink'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Durability of the Superheroes\",fontsize=15)\n",
    "plt.xlabel('Name of the Superheroes',fontsize=13)\n",
    "plt.ylabel('Count of the Durability',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9adcf20",
   "metadata": {},
   "source": [
    "# Superheroes with the highest Power ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ed1be7f9",
   "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>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Power\n",
       "0            3-D Man     25\n",
       "1             A-Bomb     17\n",
       "2         Abe Sapien     35\n",
       "3           Abin Sur     84\n",
       "6        Adam Monroe     71\n",
       "..               ...    ...\n",
       "604            X-Man     92\n",
       "606     Yellowjacket     12\n",
       "607  Yellowjacket II     31\n",
       "608             Ymir     83\n",
       "609          Zatanna    100\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Power.\n",
    "good_power=good[['Name','Power']]\n",
    "good_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce128e50",
   "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>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Shadow King</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Offspring</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Nova</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Goliath IV</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>Goliath II</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Arsenal</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>Renata Soliz</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Aztar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Power\n",
       "609       Zatanna    100\n",
       "487   Shadow King    100\n",
       "433       Phoenix    100\n",
       "419     Offspring    100\n",
       "418          Nova    100\n",
       "..            ...    ...\n",
       "255    Goliath IV      0\n",
       "253    Goliath II      0\n",
       "39        Arsenal      0\n",
       "462  Renata Soliz      0\n",
       "51          Aztar      0\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Power.\n",
    "good_power=good_power.sort_values(by='Power',ascending=False)\n",
    "good_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9e8270be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Shadow King</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>Phoenix</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>Offspring</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>Nova</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Power\n",
       "609      Zatanna    100\n",
       "487  Shadow King    100\n",
       "433      Phoenix    100\n",
       "419    Offspring    100\n",
       "418         Nova    100"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest Power.\n",
    "good_power=good_power.head()\n",
    "good_power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ec1ca5",
   "metadata": {},
   "source": [
    "# Which superhero has the highest Combat ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "52266abd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>Combat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Combat\n",
       "0            3-D Man      52\n",
       "1             A-Bomb      64\n",
       "2         Abe Sapien      85\n",
       "3           Abin Sur      65\n",
       "6        Adam Monroe      64\n",
       "..               ...     ...\n",
       "604            X-Man      84\n",
       "606     Yellowjacket      14\n",
       "607  Yellowjacket II      28\n",
       "608             Ymir      28\n",
       "609          Zatanna      56\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Combat.\n",
    "good_combat=good[['Name','Combat']]\n",
    "good_combat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "af7f1227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Combat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ares</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Century</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Hercules</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Shang-Chi</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Black Widow</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>Power Man</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>Psylocke</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>Captain Epic</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>Quantum</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>Guardian</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Combat\n",
       "36           Ares     101\n",
       "147       Century     100\n",
       "281      Hercules     100\n",
       "489     Shang-Chi     100\n",
       "94    Black Widow     100\n",
       "..            ...     ...\n",
       "439     Power Man       1\n",
       "442      Psylocke       1\n",
       "136  Captain Epic       1\n",
       "445       Quantum       1\n",
       "264      Guardian       1\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Combat.\n",
    "good_combat=good_combat.sort_values(by='Combat',ascending=False)\n",
    "good_combat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5ab1e67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "        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>Combat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Ares</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Century</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>Hercules</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Shang-Chi</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Black Widow</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Name  Combat\n",
       "36          Ares     101\n",
       "147      Century     100\n",
       "281     Hercules     100\n",
       "489    Shang-Chi     100\n",
       "94   Black Widow     100"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 records of the dataset.\n",
    "good_combat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f59a9b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Ares', 'Century', 'Hercules', 'Shang-Chi', 'Black Widow']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the 5 superheroes with highest combat.\n",
    "x=list(good_combat['Name'][0:5])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1b1f0420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101, 100, 100, 100, 100]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the combat of the top 5 superheroes.\n",
    "y=list(good_combat['Combat'][0:5])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "100d90fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 superheroes with highest Combat.\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['lawngreen','peru','tomato','deeppink','blue'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Combat of the Superheroes\",fontsize=15)\n",
    "plt.xlabel('Name of the Superheroes',fontsize=13)\n",
    "plt.ylabel('Count of the Combat',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd642132",
   "metadata": {},
   "source": [
    "# Who is the best superhero ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "15a4ff0c",
   "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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3-D Man</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A-Bomb</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Abe Sapien</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Abin Sur</td>\n",
       "      <td>406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Adam Monroe</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>X-Man</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>606</th>\n",
       "      <td>Yellowjacket</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>607</th>\n",
       "      <td>Yellowjacket II</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>608</th>\n",
       "      <td>Ymir</td>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>609</th>\n",
       "      <td>Zatanna</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Total\n",
       "0            3-D Man    233\n",
       "1             A-Bomb    316\n",
       "2         Abe Sapien    299\n",
       "3           Abin Sur    406\n",
       "6        Adam Monroe    320\n",
       "..               ...    ...\n",
       "604            X-Man    465\n",
       "606     Yellowjacket    164\n",
       "607  Yellowjacket II    182\n",
       "608             Ymir    388\n",
       "609          Zatanna    292\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of superheroes from the original dataset about the Total column.\n",
    "good_total=good[['Name','Total']]\n",
    "good_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6137af58",
   "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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Martian Manhunter</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Superman</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Stardust</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Thor</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Supergirl</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Bumbleboy</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Namor</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Brother Voodoo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>Nathan Petrelli</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>Omniscient</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>435 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Total\n",
       "361  Martian Manhunter    581\n",
       "537           Superman    579\n",
       "525           Stardust    568\n",
       "546               Thor    546\n",
       "536          Supergirl    544\n",
       "..                 ...    ...\n",
       "127          Bumbleboy      5\n",
       "405              Namor      5\n",
       "124     Brother Voodoo      5\n",
       "409    Nathan Petrelli      5\n",
       "421         Omniscient      5\n",
       "\n",
       "[435 rows x 2 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Total column.\n",
    "good_total=good_total.sort_values(by='Total',ascending=False)\n",
    "good_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5870cc26",
   "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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>Martian Manhunter</td>\n",
       "      <td>581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>537</th>\n",
       "      <td>Superman</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>Stardust</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>Thor</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>536</th>\n",
       "      <td>Supergirl</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name  Total\n",
       "361  Martian Manhunter    581\n",
       "537           Superman    579\n",
       "525           Stardust    568\n",
       "546               Thor    546\n",
       "536          Supergirl    544"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 superheroes with highest Total.\n",
    "good_total=good_total.head()\n",
    "good_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8e765317",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Martian Manhunter', 'Superman', 'Stardust', 'Thor', 'Supergirl']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the 5 superheroes with highest Total.\n",
    "x=list(good_total['Name'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af1495bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[581, 579, 568, 546, 544]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the Total of the top 5 superheroes.\n",
    "y=list(good_total['Total'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ab23f977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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To/ruueceI8lYrVZTt25dc++995p7773X3Hjjjc7Llrdt29blPkvGGDNjxgwjyfj4+JgWLVqYdu3amfDwcFOtWjXTvn37y94g+O677zZBQUGmXbt2plOnTqZkyZJGkmnRooVJTU11ec7Zs2dNixYtjCQTEhJimjRpYrp27WqaNWvmvFHuxTd0zljO5caDt17zK11qPzfvn23btpmKFSsa/e+G2XFxcea+++4zDRs2dL6OF9+4ukSJEsZqtZobb7zRdOnSxdx3332mevXqzvtrXe5+ZQCQHYIVAORSdsHKGGOOHTtmhg4daqpUqWL8/f1NyZIlTZs2bcw333yT5bzOnDljHn30URMVFWV8fX0z/RF74sQJ88gjj5hKlSqZgIAAU7lyZfPUU0+Zs2fPZvsH8dUGq4w6Ro8ebWrVqmUCAgJMSEiIue2228y8efOy7J/bYJWammqeeeYZU6VKFePn55ep1tz8kZ/h448/NnfccYcJCwszfn5+ply5cqZhw4YmISHBbN++PUf1/fHHH+btt982nTt3NjVr1jTFixc3fn5+JiIiwtxxxx1mzpw5LjeivdjMmTNNbGys8ff3N+Hh4ebBBx80x44du+x9rKKjo8358+fN008/bSpVqmT8/f1NdHS0GTlypDl37lyWy0lNTTXvvvuuadasmSlVqpTx9/c3FSpUME2bNjXjx483f/zxR5bLyY63XvMrBStjrv79Y8yF90hCQoKpW7euCQ4ONkFBQaZKlSqmffv2ZubMmS43Un7vvfdM9+7dTfXq1U1ISIgJCQkxtWrVMsOGDct0I24AyCmLMV68IQkAAHBhsVgUHR3tchggAKDw4RwrAAAAAHATwQoAAAAA3ESwAgAAAAA3cYNgAADyEac6A0DRwB4rAAAAAHATwQoAAAAA3MShgFlwOBz6888/FRISIovFkt/lAAAAAMgnxhidPn1akZGRslqz3y9FsMrCn3/+qaioqPwuAwAAAEAB8ccff6hChQrZTidYZSEkJETShRevePHi+VzNtSM1NVWLFy9WmzZt5Ofnl9/loBBjLMETGEfwFMYSPIWxlD9OnTqlqKgoZ0bIToELVocOHdJTTz2lr776Sna7Xdddd52mT5+uevXqSbqwK2706NF65513lJSUpAYNGuiNN97Q9ddf75xHcnKyhg0bpg8++EB2u10tW7bUm2++edmEebGMw/+KFy9OsPKi1NRUBQUFqXjx4mws4BbGEjyBcQRPYSzBUxhL+etKpwgVqItXJCUlqXHjxvLz89NXX32l3377TRMmTFDJkiWdfcaPH6+JEydq6tSpWrdunSIiItS6dWudPn3a2Wfw4MFauHCh5s+frzVr1ujMmTO6++67lZ6eng9rBQAAAKCoK1B7rF5++WVFRUVp5syZzrZKlSo5/2+M0eTJkzVy5Eh16tRJkjR79myFh4dr3rx56t+/v06ePKnp06drzpw5atWqlSRp7ty5ioqK0tKlSxUXF+fVdQIAAABQ9BWoYPX5558rLi5O9913n1auXKny5ctrwIABeuihhyRJe/fuVWJiotq0aeN8TkBAgJo1a6a1a9eqf//+2rBhg1JTU136REZGKjY2VmvXrs0yWCUnJys5Odn5+NSpU5Iu7G5NTU3Nq9XFJTJea15zuIuxBE9gHMFTGEvwFMZS/sjp612ggtWePXs0bdo0DRkyRE8//bR++uknPfbYYwoICFDPnj2VmJgoSQoPD3d5Xnh4uPbv3y9JSkxMlL+/v0qVKpWpT8bzL/Xiiy9q9OjRmdoXL16soKAgT6warsKSJUvyuwQUEYwleALjCJ7CWIKnMJa869y5cznqV6CClcPhUP369TVu3DhJ0o033qitW7dq2rRp6tmzp7PfpSeOGWOueDLZ5fqMGDFCQ4YMcT7OuPJHmzZtuHiFF6WmpmrJkiVq3bo1J2TCLYwleALjCJ7CWIKnMJbyR8bRbFdSoIJVuXLlVKtWLZe2mjVr6pNPPpEkRURESLqwV6pcuXLOPkeOHHHuxYqIiFBKSoqSkpJc9lodOXJEjRo1ynK5AQEBCggIyNTu5+fHoM0HvO7wFMYSPIFxBE9hLMFTGEveldPXukBdFbBx48basWOHS9vOnTsVHR0tSYqJiVFERITL7s+UlBStXLnSGZrq1asnPz8/lz6HDx/Wli1bsg1WAAAAAOCOArXH6oknnlCjRo00btw4denSRT/99JPeeecdvfPOO5IuHAI4ePBgjRs3TtWqVVO1atU0btw4BQUFqXv37pKkEiVKqG/fvho6dKhKly6t0NBQDRs2TLVr13ZeJRAAAAAAPKlABaubb75ZCxcu1IgRIzRmzBjFxMRo8uTJeuCBB5x9hg8fLrvdrgEDBjhvELx48WKXOyFPmjRJvr6+6tKli/MGwbNmzZKPj09+rBYAAACAIq5ABStJuvvuu3X33XdnO91isSghIUEJCQnZ9gkMDNSUKVM0ZcqUPKgQAAAAAFwVqHOsAAAAAKAwIlgBAAAAgJsIVgAAAADgJoIVAAAAALipwF28ApkdOHBAx44dy+8y8pzD4ZAkbdq0SVZr0c/8YWFhqlixYn6XAQAAAA8gWBVwBw4cUI3qNWQ/b8/vUvKczWbTBx98oKZNm8puvwbWN9Cm7Tu2ezVcEdKLHgI6AAAFA8GqgDt27Jjs5+3qpE4KU1h+l5OnfP83HOMVrzSl5XM1eeuYjmnB+QU6duyY1/4oPnDggGrUqHFthNZrKKTbbDZt3+7dgA4AADIjWBUSYQpTpCLzu4w8Zf3fKX8RipBDjnyupug5duyY7Ha77uj0L4WGhed3OXnK1/fCzcDvix+ktLT0fK4m75w49pe+XjDXqwEdAABkjWAFXGNCw8JVNjIqv8vIUz5WiySpTER5pTtMPlcDAACuBUX75AMAAAAA8AKCFQAAAAC4iWAFAAAAAG7iHCsAwFW7Fi7dfy1dtl/i0v0A4C6CFQDgqhw4cEDVq9fQ+SJ+f71r6bL9khQYaNMOL99bDwCKEoIVAOCqHDt27H+hqpNUpO+vl/ERGS8V8XvrScd03sv31gOAooZgBQDIpTCpSN9fL+PwvwiJe+sBAK6g6B80DgAAAAB5jGAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbvLN7wIAAMC16cCBAzp27Fh+l5HnHA6HJGnTpk2yWov+d9phYWGqWLFifpcBeB3BCgAAeN2BAwdUvUZ1nbefz+9S8pzNZtMHH3ygpk2bym6353c5eS7QFqgd23d4NVwR0oumwhbSCVYAAMDrjh07pvP28yrTrYz8y/rndzl5KtAnUJIU+UikzqcX7SCZciRFRz84qmPHjnntD+IDBw6oZs3qOneuaL+20rUX0oOCArVtm3dDujsIVgAAIN/4l/VXQIWA/C4jT/lbLwRH//L+Mg6Tz9UUPceOHdO5c+c1d7xUs3J+V5O3HJIOSVo1p+hfKGHbHulfw897NaS7i2AFAACAQq9mZemm6/O7iryVmi4d+l2qW0Py88nvanCpoh52AQAAACDPEawAAAAAwE0EKwAAAABwE8EKAAAAANxEsAIAAAAANxGsAAAAAMBNBCsAAAAAcBPBCgAAAADcRLACAAAAADcRrAAAAADATQQrAAAAAHATwQoAAAAA3ESwAgAAAAA3EawAAAAAwE0EKwAAAABwE8EKAAAAANxEsAIAAAAANxGsAAAAAMBNBCsAAAAAcBPBCgAAAADcRLACAAAAADcVqGCVkJAgi8Xi8i8iIsI53RijhIQERUZGymazqXnz5tq6davLPJKTkzVo0CCFhYUpODhY7du318GDB729KgAAAACuIQUqWEnS9ddfr8OHDzv/bd682Tlt/PjxmjhxoqZOnap169YpIiJCrVu31unTp519Bg8erIULF2r+/Plas2aNzpw5o7vvvlvp6en5sToAAAAArgG++V3ApXx9fV32UmUwxmjy5MkaOXKkOnXqJEmaPXu2wsPDNW/ePPXv318nT57U9OnTNWfOHLVq1UqSNHfuXEVFRWnp0qWKi4vz6roAAAAAuDYUuGC1a9cuRUZGKiAgQA0aNNC4ceNUuXJl7d27V4mJiWrTpo2zb0BAgJo1a6a1a9eqf//+2rBhg1JTU136REZGKjY2VmvXrs02WCUnJys5Odn5+NSpU5Kk1NRUpaam5tGa5ozD4ZDNZpOvfGUteDsYPcpqs7r8LMp85SubbHI4HF4bY86x5OsjH6vFK8vMLxnrV9TX09fXRzabd8eR9M9YuvARUnTfr7b/bYts18A26cLvMn+2SYE+gfK3+ntlmfklwBLwz88iPpwsPhavb5cyxpJDUmoRP0Ap1WFz+VmUOSTZbPL6Z1xWcrp8izHG5HEtOfbVV1/p3Llzuu666/TXX3/phRde0Pbt27V161bt2LFDjRs31qFDhxQZGel8Tr9+/bR//3598803mjdvnuLj411CkiS1adNGMTExevvtt7NcbkJCgkaPHp2pfd68eQoKCvLsSgIAAAAoNM6dO6fu3bvr5MmTKl68eLb9CtQeq7Zt2zr/X7t2bd16662qUqWKZs+erYYNG0qSLBbXb6CNMZnaLnWlPiNGjNCQIUOcj0+dOqWoqCi1adPmsi+eN2zatElNmzZVvOIVocyHSBYlVptVsTNitaXPFjnsjvwuJ08lKlEzNVOrVq1S3bp1vbLMjLF0X/wglYko75Vl5hcfq0WNYitq7ZYDSncUmO+OPO5o4iF9NHOKV8eR9M9YkuKlIrxdstmsmjEjVn36bJG9iG+TpEQpn7ZJkY9Eyr980d9jlVA5QQl7EpRskq/YvzBLOZSiP6f9mS9jadUcqW4Nrywy36Q6bFqyZ4ZaV+4jP6s9v8vJU5u2S017yOufcVnJOJrtSgpUsLpUcHCwateurV27dqlDhw6SpMTERJUrV87Z58iRIwoPD5ckRUREKCUlRUlJSSpVqpRLn0aNGmW7nICAAAUEBGRq9/Pzk5+fn4fWJnesVqvsdrvSlCaHivoH+wUOu6PIB6s0pckuu6xWq9fGmHMspaUX6bBxsXSHKdLrmpaWLrvdu+NI+mcsSWnSNbBdstsd10CwSpPyaZt0Pv28TBF+n0pyHv6XbJKV7CjawSo5Pdnr26WMsWSV5OfjlUXmOz+rXX4+RTtYWSXZ7fL6Z1xWcrr8An2kb3JysrZt26Zy5copJiZGERERWrJkiXN6SkqKVq5c6QxN9erVk5+fn0ufw4cPa8uWLZcNVgAAAADgjgK1x2rYsGFq166dKlasqCNHjuiFF17QqVOn1KtXL1ksFg0ePFjjxo1TtWrVVK1aNY0bN05BQUHq3r27JKlEiRLq27evhg4dqtKlSys0NFTDhg1T7dq1nVcJBAAAAABPK1DB6uDBg+rWrZuOHTumMmXKqGHDhvrhhx8UHR0tSRo+fLjsdrsGDBigpKQkNWjQQIsXL1ZISIhzHpMmTZKvr6+6dOkiu92uli1batasWfLxuUb2DQMAAADwugIVrObPn3/Z6RaLRQkJCUpISMi2T2BgoKZMmaIpU6Z4uDoAAAAAyFqBPscKAAAAAAoDghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABu8s1Jp/feey9XM+/Zs2eungcAAAAAhUmOglXv3r1lsVhkjMnxjC0WC8EKAAAAwDUhR8FqxYoVeV0HAAAAABRaOQpWzZo1y+s6AAAAAKDQ4uIVAAAAAOCmHO2xys5ff/2l9evXKykpSQ6HI9N0zrECAAAAcC3IVbByOBwaOHCg3n333SwDVQaCFQAAAIBrQa4OBXz11Vf19ttvq1u3bpo9e7aMMXrppZf0xhtvqFq1aqpfv76WLFni6VoBAAAAoEDKVbCaPXu24uLi9N5776lt27aSpHr16unhhx/Whg0bdOzYMW3YsMGjhQIAAABAQZWrYLVnzx5noLJaL8wiNTVVkhQcHKz4+Hi9++67HioRAAAAAAq2XAUrm80mf39/SVKxYsVksVh05MgR5/SIiAj98ccfnqkQAAAAAAq4XAWr6Oho7d27V5Lk5+enqlWr6uuvv3ZOX7p0qcLDwz1TIQAAAAAUcLkKVrfffrsWLFjgfNyjRw998MEHatGihZo3b66PPvpIXbp08ViRAAAAAFCQ5SpYDRs2TFOnTlVycrIkacSIERo4cKA2bdqkrVu3ql+/fkpISHCrsBdffFEWi0WDBw92thljlJCQoMjISNlsNjVv3lxbt251eV5ycrIGDRqksLAwBQcHq3379jp48KBbtQAAAADA5eQqWJUrV05xcXEKCAiQJPn4+GjKlCk6ceKEjh49qmnTpslms+W6qHXr1umdd95RnTp1XNrHjx+viRMnaurUqVq3bp0iIiLUunVrnT592tln8ODBWrhwoebPn681a9bozJkzuvvuu5Wenp7regAAAADgcnIVrMaMGaMtW7ZkO33r1q0aM2ZMrgo6c+aMHnjgAf3nP/9RqVKlnO3GGE2ePFkjR45Up06dFBsbq9mzZ+vcuXOaN2+eJOnkyZOaPn26JkyYoFatWunGG2/U3LlztXnzZi1dujRX9QAAAADAlfjm5kkJCQmqWrWqYmNjs5y+ZcsWjR49Ws8999xVz3vgwIG666671KpVK73wwgvO9r179yoxMVFt2rRxtgUEBKhZs2Zau3at+vfvrw0bNig1NdWlT2RkpGJjY7V27VrFxcVluczk5GTnYY2SdOrUKUkXLiGfcRn5/OJwOGSz2eQrX1lzl4MLDavN6vKzKPOVr2yyyeFweG2MOceSr498rBavLDO/ZKxfUV9PX18f2WzeHUfSP2PpwkdI0X2/2v63LbJdA9ukC7/L/NkmBfoEyt/q75Vl5pcAS8A/P4v4cLL4WLy+XcoYSw5JqUX8AKVUh83lZ1HmkGSzyeufcVnJ6fJzFayu5Ny5c/L1vfpZz58/Xxs2bND69eszTUtMTJSkTFcbDA8P1/79+519/P39XfZ0ZfTJeH5WXnzxRY0ePTpT++LFixUUFHTV6+FpH3zwQX6X4FWxM7IO7EVJHdVRG7XRoUOHdOjQIa8t91obS41iK+Z3CXmrTrQ6tWnk9XEkXVtjacY1sE2S6khsk/JcQuWE/C4h71WR1FT5MpYOSTr0u9cWma+W7JmR3yXkPV/pgw+8P5aycu7cuRz1y3H6OXDggPbt2+d8vH37dq1atSpTv6SkJL399tuqXLlyTmctSfrjjz/0+OOPa/HixQoMDMy2n8Xi+g20MSZT26Wu1GfEiBEaMmSI8/GpU6cUFRWlNm3aqHjx4jlcg7yxadMmNW3aVPGKV4Qi8rWWvGa1WRU7I1Zb+myRw+7I73LyVKISNVMztWrVKtWtW9cry8wYS/fFD1KZiPJeWWZ+8bFa1Ci2otZuOaB0h8nvcvLM0cRD+mjmFK+OI+mfsSTFS0V4u2SzWTVjRqz69NkiexHfJkmJUj5tkyIfiZR/+aK/xyqhcoIS9iQo2SRfsX9hlnIoRX9O+zNfxtKqOVLdGl5ZZL5Jddi0ZM8Mta7cR35We36Xk6c2bZea9pDXP+OyknE025XkOFjNnDlTo0ePlsVikcVi0dixYzV27NhM/Ywxslqtevfdd3NeraQNGzboyJEjqlevnrMtPT1dq1at0tSpU7Vjxw5JF/ZKlStXztnnyJEjzr1YERERSklJUVJSksteqyNHjqhRo0bZLjsgIMB5IY6L+fn5yc/P76rWw9OsVqvsdrvSlCaHivoH+wUOu6PIB6s0pckuu6xWq9fGmHMspaUX6bBxsXSHKdLrmpaWLrvdu+NI+mcsSWnSNbBdstsd10CwSpPyaZt0Pv28TBF+n0pyHv6XbJKV7CjawSo5Pdnr26WMsWSV5OfjlUXmOz+rXX4+RTtYWSXZ7fL6Z1xWcrr8HAerDh06qFKlSjLGqE+fPurXr59uvfVWlz4Wi0XFihVT/fr1VbHi1R2C07JlS23evNmlLT4+XjVq1NBTTz2lypUrKyIiQkuWLNGNN94oSUpJSdHKlSv18ssvS5Lq1asnPz8/LVmyxHkfrcOHD2vLli0aP378VdUDAAAAADmV42BVt25d5264lStXKj4+Xg0aNPBYISEhIZkuhhEcHKzSpUs72wcPHqxx48apWrVqqlatmsaNG6egoCB1795dklSiRAn17dtXQ4cOVenSpRUaGqphw4apdu3aatWqlcdqBQAAAICL5eriFTNnzvR0HTkyfPhw2e12DRgwQElJSWrQoIEWL16skJAQZ59JkybJ19dXXbp0kd1uV8uWLTVr1iz5+Fwj+4YBAAAAeJ1bVwWcP3++FixYoD179kiSqlSpos6dOzsPw3PXt99+6/LYYrEoISFBCQkJ2T4nMDBQU6ZM0ZQpUzxSAwAAAABcSa6CVUpKijp27Kivv/5axhiVKFFCkvTzzz/r448/1uzZs/Xpp5/m+4lmAAAAAOANubpN3csvv6yvvvpKDz74oA4cOKCkpCQlJSXpjz/+UL9+/fTVV185LygBAAAAAEVdroLV+++/r/bt2+vtt99WhQoVnO3ly5fXtGnT1K5dO82dO9djRQIAAABAQZarYLVv3z7FxcVlOz0uLs7lZsIAAAAAUJTlKliFhITo0KFD2U4/dOiQy5X6AAAAAKAoy1Wwatq0qaZOnapNmzZlmvbrr7/qjTfeULNmzdwuDgAAAAAKgxxfFbBy5cqaPHmy2rdvr9GjR+ubb75R/fr1dffdd6tWrVqSpK1bt2rRokUKDAzUmDFj8qxoAAAAAChIchys9u3bpzNnzkiSYmNjtWrVKj322GP67LPP9Nlnnzn7NW7cWK+//rozbAEAAABAUZfrGwTfdNNNWrNmjY4dO6Y9e/bIGKMqVaooLCzMk/UBAAAAQIGX62CVISwsjDAFAAAA4Jp2VcFq+/btWrVqVY77N23a9KoLAgAAAIDC5qqC1dixYzV27Ngc909PT7/qggAAAACgsLmqYNWhQwfVqVMnr2oBAAAAgELpqoJV586d1b1797yqBQAAAAAKpVzdIBgAAAAA8A+CFQAAAAC4iWAFAAAAAG7K8TlWK1asUM2aNfOyFgAAAAAolHIcrJo1a5aXdQAAAABAocWhgAAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4KZcB6s//vhDffr0UYUKFeTv76/ly5dLko4ePao+ffpo3bp1HisSAAAAAAqyXAWrvXv3qn79+vrkk090/fXXKz093TmtTJkyWr9+vd59912PFQkAAAAABVmO72N1sZEjR8rHx0dbtmyRzWZT2bJlXabfeeed+r//+z+PFAgAAAAABV2u9lgtXbpUjzzyiKKiomSxWDJNj46O1sGDB90uDgAAAAAKg1wFq1OnTqlcuXLZTk9JSVFaWlquiwIAAACAwiRXwSoqKkpbt27Ndvr333+vqlWr5rooAAAAAChMchWsOnXqpBkzZmjLli3OtoxDAj/88EN9/PHH6tKli2cqBAAAAIACLlfBauTIkapQoYIaNGigbt26yWKxaOzYsbr55pvVvXt31a1bV0OHDvV0rQAAAABQIOUqWBUvXlzff/+9+vbtq19++UXGGC1fvly7d+/WgAEDtGLFCgUGBnq6VgAAAAAokHJ1uXXpQrh6/fXX9frrr+vo0aMyxqhMmTJZXiUQAAAAAIqyXAeri5UpU8YTswEAAACAQsmtYLVz5079/vvvOn78uIwxmab37NnTndkDAAAAQKGQq2D1119/qVevXlqyZIkkZRmqLBYLwQoAAADANSFXwerRRx/VkiVL9Mgjj+j2229X6dKlPV0XAAAAABQauQpWS5YsUf/+/TV16lRP1wMAAAAAhU6uLrfucDh04403eroWAAAAACiUchWsGjVqpI0bN3q4FAAAAAAonHIVrCZOnKgFCxbok08+8XQ9AAAAAFDo5Ogcq9tvvz1TW0hIiLp06aLIyEhVrlxZPj4+LtMtFouWLVvmmSoBAAAAoADLUbDas2ePLBZLpvaKFStKkg4cOODZqgAAAACgEMlRsNq3b18elwEAAAAAhVeuzrE6cOCA7HZ7ttPtdjt7sQAAAABcM3IVrGJiYrRw4cJsp3/++eeKiYnJdVEAAAAAUJjkKlgZYy47PT09PctzsgAAAACgKMpVsJJ02eD0ww8/KDQ0NLezBgAAAIBCJUcXr5Ck1157Ta+99prz8eDBgzVy5MhM/f7++2+dPHlSPXv29EyFAAAAAFDA5ThYlSxZUtHR0ZKk/fv3q3Tp0goPD3fpY7FYVKxYMTVo0ECDBw/2aKEAAAAAUFDlOFj16tVLvXr1kiRZrVY988wz6t69e54VBgAAAACFRY6D1cUcDoen6wAAAACAQivXF6/IC9OmTVOdOnVUvHhxFS9eXLfeequ++uor53RjjBISEhQZGSmbzabmzZtr69atLvNITk7WoEGDFBYWpuDgYLVv314HDx709qoAAAAAuIYUqGBVoUIFvfTSS1q/fr3Wr1+v22+/Xffcc48zPI0fP14TJ07U1KlTtW7dOkVERKh169Y6ffq0cx6DBw/WwoULNX/+fK1Zs0ZnzpzR3XffrfT09PxaLQAAAABFXK4OBcwr7dq1c3k8duxYTZs2TT/88INq1aqlyZMna+TIkerUqZMkafbs2QoPD9e8efPUv39/nTx5UtOnT9ecOXPUqlUrSdLcuXMVFRWlpUuXKi4uLsvlJicnKzk52fn41KlTkqTU1FSlpqbmxarmmMPhkM1mk698ZS1YOdjjrDary8+izFe+sskmh8PhtTHmHEu+PvKxFu37zGWsX1FfT19fH9ls3h1H0j9j6cJHSNF9v9r+ty2yXQPbpAu/y/zZJgX6BMrf6u+VZeaXAEvAPz+L+HCy+Fi8vl3KGEsOSalF/Hv0VIfN5WdR5pBks8nrn3FZyenyLeZKd/vNJ+np6froo4/Uq1cv/fLLLwoMDFSVKlX0888/68Ybb3T2u+eee1SyZEnNnj1by5cvV8uWLXXixAmVKlXK2adu3brq0KGDRo8eneWyEhISspw2b948BQUFeX7lAAAAABQK586dU/fu3XXy5EkVL14823452mN16tQpBQcHy8fHx2MFZmfz5s269dZbdf78eRUrVkwLFy5UrVq1tHbtWknKdIn38PBw7d+/X5KUmJgof39/l1CV0ScxMTHbZY4YMUJDhgxxPj516pSioqLUpk2by7543rBp0yY1bdpU8YpXhCLytZa8ZrVZFTsjVlv6bJHDXrQvkJKoRM3UTK1atUp169b1yjIzxtJ98YNUJqK8V5aZX3ysFjWKrai1Ww4o3VEgvzvyiKOJh/TRzCleHUfSP2NJipeK8HbJZrNqxoxY9emzRfYivk2SEqV82iZFPhIp//JFf49VQuUEJexJULJJvmL/wizlUIr+nPZnvoylVXOkujW8ssh8k+qwacmeGWpduY/8rPb8LidPbdouNe0hr3/GZSXjaLYryVGwKlWqlObMmeO8vHqfPn3Uv39/NWjQIPcVZqN69erauHGj/v77b33yySfq1auXVq5c6Zxusbge2mOMydR2qSv1CQgIUEBAQKZ2Pz8/+fn5XeUaeJbVapXdblea0uRQUf9gv8BhdxT5YJWmNNlll9Vq9doYc46ltPQiHTYulu4wRXpd09LSZbd7dxxJ/4wlKU26BrZLdrvjGghWaVI+bZPOp5+XKcLvU0nOw/+STbKSHUU7WCWnJ3t9u5QxlqyS/PJ+H0CB4Ge1y8+naAcrqyS7XV7/jMtKTpefoyN9fXx8XC7+MGvWLO3evTt3lV2Bv7+/qlatqvr16+vFF19U3bp19dprryki4sK3opfueTpy5IhzL1ZERIRSUlKUlJSUbR8AAAAA8LQcBavo6GitWrXKpe1Ke4k8xRij5ORkxcTEKCIiQkuWLHFOS0lJ0cqVK9WoUSNJUr169eTn5+fS5/Dhw9qyZYuzDwAAAAB4Wo4OBYyPj9czzzyjTz75RCVLlpR04bLmI0eOzPY5FovlqvdqPf3002rbtq2ioqJ0+vRpzZ8/X99++62+/vprWSwWDR48WOPGjVO1atVUrVo1jRs3TkFBQc5DFEuUKKG+fftq6NChKl26tEJDQzVs2DDVrl3beZVAAAAAAPC0HAWrp59+WhUqVNA333yjxMRE7d+/X6VLl/b44XV//fWXevToocOHD6tEiRKqU6eOvv76a7Vu3VqSNHz4cNntdg0YMEBJSUlq0KCBFi9erJCQEOc8Jk2aJF9fX3Xp0kV2u10tW7bUrFmzvHLhDQAAAADXphzfx6pnz57q2bOnpAsnkT3zzDPOPUWeMn369MtOt1gsSkhIUEJCQrZ9AgMDNWXKFE2ZMsWjtQEAAABAdnJ1g+AVK1aoVq1anq4FAAAAAAqlXAWrZs2aOf//22+/ac+ePZKkKlWqqGbNmp6pDAAAAAAKiVwFK0lauXKlHnnkEe3YscOlvUaNGpo2bdr/bh4JAAAAAEVfroLV+vXrFRcXJ6vVqvj4eNWuXVvGGG3ZskUffPCB4uLitGbNGtWrV8/T9QIAAABAgZOrYDV69GiVKFFC33//vSpXruwybeTIkWrYsKFGjx6tzz//3CNFAgAAAEBBlqMbBF/qu+++04ABAzKFKkmKiYnRI488ojVr1rhdHAAAAAAUBrkKVna7XaVLl852elhYmOx2e66LAgAAAIDCJFfBqmrVqpc9zO+zzz5T1apVc10UAAAAABQmuQpWvXr10tKlS9WlSxdt2rRJKSkpSklJ0caNG9W1a1ctX75c8fHxnq4VAAAAAAqkXF28YsiQIfrll1/0wQcf6JNPPpEkWSwWGWNkjFG3bt30xBNPeLRQAAAAACiochWsrFar3n//fcXHx+vTTz/Vnj17ZIxRlSpV1LFjR7Vs2dLTdQIAAABAgZXrGwRLUqtWrdSqVStP1QIAAAAAhVKuzrECAAAAAPyDYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4KVfB6sCBA7Lb7dlOt9vtOnDgQK6LAgAAAIDCJFfBKiYmRgsXLsx2+ueff66YmJhcFwUAAAAAhUmugpUx5rLT09PTZbFYclUQAAAAABQ2uT7H6nLB6YcfflBoaGhuZw0AAAAAhYpvTju+9tpreu2115yPBw8erJEjR2bq9/fff+vkyZPq2bOnZyoEAAAAgAIux8GqZMmSio6OliTt379fpUuXVnh4uEsfi8WiYsWKqUGDBho8eLBHCwUAAACAgirHwapXr17q1auXJMlqteqZZ55R9+7d86wwAAAAACgschysLuZwODxdBwAAAAAUWtwgGAAAAADclOtgNX/+fDVu3Fhly5aVj49Ppn++vrnaGQYAAAAAhU6u0s8rr7yif//73ypdurQaNmyo0qVLe7ouAAAAACg0chWs3njjDTVo0EDLli2TzWbzdE0AAAAAUKjk6lDAxMRE/etf/yJUAQAAAIByGayqVKmikydPeroWAAAAACiUchWsnnjiCb377rs6ffq0p+sBAAAAgEInV+dY+fv7q0yZMqpZs6b69OmjmJgY+fj4ZOrXs2dPtwsEAAAAgIIuV8Gqd+/ezv+/8MILWfaxWCwEKwAAAADXhFwFqxUrVni6DgAAAAAotHIVrJo1a+bpOgAAAACg0MrVxSsAAAAAAP/I1R6rMWPGXLGPxWLRs88+m5vZAwAAAEChkqtglZCQkO00i8UiYwzBCgAAAMA1I1fBau/evZna0tLStHv3bk2aNEknT57U7Nmz3S4OAAAAAAqDXAWr6OjoLNurVKmi1q1bq2nTppo5c6bGjRvnVnEAAAAAUBh4/OIVFotF9957r9577z1PzxoAAAAACqQ8uSpgSkqKjh8/nhezBgAAAIACx+PBav369XrttddUs2ZNT88aAAAAAAqkXJ1jVbly5SzbT5w4odOnT8vX11fvvvuuW4UBAAAAQGGRq2BVsWJFWSwWlzaLxaKbbrpJ1113nfr166dKlSp5oj4AAAAAKPByFay+/fZbD5cBAAAAAIVXnly8AgAAAACuJbnaY5Xhhx9+0IIFC7Rnzx5JF+5j1bFjRzVs2NAjxQEAAABAYZDrYPXwww/rP//5j4wxLu2vvvqq+vXrp2nTprldHAAAAAAUBrk6FHDq1Kl65513FBcXp1WrVikpKUlJSUlatWqV7rjjDr3zzjt64403PF0rAAAAABRIuQpW7777rpo2baovv/xSt912m0qUKKESJUrotttu0xdffKEmTZronXfe8XStAAAAAFAg5SpY7dy5U507d850yXXpwmXXO3furF27dl31fF988UXdfPPNCgkJUdmyZdWhQwft2LHDpY8xRgkJCYqMjJTNZlPz5s21detWlz7JyckaNGiQwsLCFBwcrPbt2+vgwYNXXQ8AAAAA5ESuglVAQID+/vvvbKcnJSUpICDgque7cuVKDRw4UD/88IOWLFmitLQ0tWnTRmfPnnX2GT9+vCZOnKipU6dq3bp1ioiIUOvWrXX69Glnn8GDB2vhwoWaP3++1qxZozNnzujuu+9Wenr6VdcEAAAAAFeSq2DVoEEDvfXWWzp8+HCmaYcPH9bbb7+dqysDfv311+rdu7euv/561a1bVzNnztSBAwe0YcMGSRf2Vk2ePFkjR45Up06dFBsbq9mzZ+vcuXOaN2+eJOnkyZOaPn26JkyYoFatWunGG2/U3LlztXnzZi1dujQ3qwsAAAAAl5WrqwI+++yzatGihWrWrKn4+HjVqlVLkrR161Zn0Hn22WfdLu7kyZOSpNDQUEnS3r17lZiYqDZt2jj7BAQEqFmzZlq7dq369++vDRs2KDU11aVPZGSkYmNjtXbtWsXFxWVaTnJyspKTk52PT506JUlKTU1Vamqq2+vhDofDIZvNJl/5ylrEbztmtVldfhZlvvKVTTY5HA6vjTHnWPL1kY8182G8RUnG+hX19fT19ZHN5t1xJP0zli58hBTd96vtf9si2zWwTbrwu8yfbVKgT6D8rf5eWWZ+CbAE/POziA8ni4/F69uljLHkkJRaxA9OSnXYXH4WZQ5JNpu8/hmXlZwu32IuvV56Di1atEgDBgzQgQMHXNqjo6P15ptvqm3btrmZrZMxRvfcc4+SkpK0evVqSdLatWvVuHFjHTp0SJGRkc6+/fr10/79+/XNN99o3rx5io+PdwlKktSmTRvFxMTo7bffzrSshIQEjR49OlP7vHnzFBQU5NZ6AAAAACi8zp07p+7du+vkyZMqXrx4tv1yfR+rO++8U3v27NHPP/+sPXv2yBijKlWq6KabbpLV6v7XMY8++qh+/fVXrVmzJtO0Sy+aYYzJ8kIaOe0zYsQIDRkyxPn41KlTioqKUps2bS774nnDpk2b1LRpU8UrXhGKyNda8prVZlXsjFht6bNFDrsjv8vJU4lK1EzN1KpVq1S3bl2vLDNjLN0XP0hlIsp7ZZn5xcdqUaPYilq75YDSHbn67qhQOJp4SB/NnOLVcST9M5akeKkIb5dsNqtmzIhVnz5bZC/i2yQpUcqnbVLkI5HyL1/091glVE5Qwp4EJZvkK/YvzFIOpejPaX/my1haNUeqW8Mri8w3qQ6bluyZodaV+8jPas/vcvLUpu1S0x7y+mdcVjKOZruSXAcrSbJarapfv77q16/vzmwyGTRokD7//HOtWrVKFSpUcLZHRFz4AE9MTFS5cuWc7UeOHFF4eLizT0pKipKSklSqVCmXPo0aNcpyeQEBAVlebMPPz09+fn4eWafcslqtstvtSlOaHCrqH+wXOOyOIh+s0pQmu+yyWq1eG2POsZSWXqTDxsXSHaZIr2taWrrsdu+OI+mfsSSlSdfAdslud1wDwSpNyqdt0vn08zJF+H0qyXn4X7JJVrKjaAer5PRkr2+XMsaSVZKfj1cWme/8rHb5+RTtYGWVZLfL659xWcnp8nO8ayklJUUPP/ywJk+efNl+EydO1IABA5SWlpbTWTsZY/Too49qwYIFWr58uWJiYlymx8TEKCIiQkuWLHGpa+XKlc7QVK9ePfn5+bn0OXz4sLZs2ZJtsAIAAAAAd+R4j9V7772n6dOna9OmTZft17p1aw0fPlwNGjRQr169rqqYgQMHat68efrss88UEhKixMRESVKJEiVks9lksVg0ePBgjRs3TtWqVVO1atU0btw4BQUFqXv37s6+ffv21dChQ1W6dGmFhoZq2LBhql27tlq1anVV9QAAAABATuQ4WH388cdq27at8wqA2aldu7buvPNOffjhh1cdrKZNmyZJat68uUv7zJkz1bt3b0nS8OHDZbfbNWDAACUlJalBgwZavHixQkJCnP0nTZokX19fdenSRXa7XS1bttSsWbPk43ON7B8GAAAA4FU5Dla//PKLnnrqqRz1bdasmV555ZWrLiYnFyi0WCxKSEhQQkJCtn0CAwM1ZcoUTZky5aprAAAAAICrleNzrJKSklS6dOkc9S1durSSkpJyXRQAAAAAFCY5DlbFixfX0aNHc9T32LFjLofmAQAAAEBRluNgFRsbq2+++SZHfRcvXqzrr78+10UBAAAAQGGS42DVuXNnLV++XAsWLLhsv4ULF2rZsmW699573S4OAAAAAAqDHAerfv36qUaNGrr//vs1YsQI7d2712X63r179fTTT+v+++9XjRo19NBDD3m8WAAAAAAoiHJ8VcCAgAAtWrRId911l15++WWNHz9eISEhKl68uE6fPq1Tp07JGKOaNWvqiy++UGBgYF7WDQAAAAAFRo73WElSdHS0NmzYoClTpqhJkyby9fVVYmKifHx81LRpU02ZMkUbNmxQTExMXtULAAAAAAVOjvdYZQgICNDAgQM1cODAvKgHAAAAAAqdq9pjBQAAAADIjGAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgpgIVrFatWqV27dopMjJSFotFn376qct0Y4wSEhIUGRkpm82m5s2ba+vWrS59kpOTNWjQIIWFhSk4OFjt27fXwYMHvbgWAAAAAK41BSpYnT17VnXr1tXUqVOznD5+/HhNnDhRU6dO1bp16xQREaHWrVvr9OnTzj6DBw/WwoULNX/+fK1Zs0ZnzpzR3XffrfT0dG+tBgAAAIBrjG9+F3Cxtm3bqm3btllOM8Zo8uTJGjlypDp16iRJmj17tsLDwzVv3jz1799fJ0+e1PTp0zVnzhy1atVKkjR37lxFRUVp6dKliouL89q6AAAAALh2FKhgdTl79+5VYmKi2rRp42wLCAhQs2bNtHbtWvXv318bNmxQamqqS5/IyEjFxsZq7dq12Qar5ORkJScnOx+fOnVKkpSamqrU1NQ8WqOccTgcstls8pWvrAVrB6PHWW1Wl59Fma98ZZNNDofDa2PMOZZ8feRjtXhlmfklY/2K+nr6+vrIZvPuOJL+GUsXPkKK7vvV9r9tke0a2CZd+F3mzzYp0CdQ/lZ/rywzvwRYAv75WcSHk8XH4vXtUsZYckhKLeIHKKU6bC4/izKHJJtNXv+My0pOl28xxpg8riVXLBaLFi5cqA4dOkiS1q5dq8aNG+vQoUOKjIx09uvXr5/279+vb775RvPmzVN8fLxLSJKkNm3aKCYmRm+//XaWy0pISNDo0aMztc+bN09BQUGeWykAAAAAhcq5c+fUvXt3nTx5UsWLF8+2X6HZY5XBYnH9BtoYk6ntUlfqM2LECA0ZMsT5+NSpU4qKilKbNm0u++J5w6ZNm9S0aVPFK14RisjXWvKa1WZV7IxYbemzRQ67I7/LyVOJStRMzdSqVatUt25drywzYyzdFz9IZSLKe2WZ+cXHalGj2Ipau+WA0h0F8rsjjziaeEgfzZzi1XEk/TOWpHipCG+XbDarZsyIVZ8+W2Qv4tskKVHKp21S5COR8i9f9PdYJVROUMKeBCWb5Cv2L8xSDqXoz2l/5stYWjVHqlvDK4vMN6kOm5bsmaHWlfvIz2rP73Ly1KbtUtMe8vpnXFYyjma7kkITrCIiLnx4JyYmqly5cs72I0eOKDw83NknJSVFSUlJKlWqlEufRo0aZTvvgIAABQQEZGr38/OTn5+fp1YhV6xWq+x2u9KUJoeK+gf7BQ67o8gHqzSlyS67rFar18aYcyylpRfpsHGxdIcp0uualpYuu92740j6ZyxJadI1sF2y2x3XQLBKk/Jpm3Q+/bxMEX6fSnIe/pdskpXsKNrBKjk92evbpYyxZJXk5+OVReY7P6tdfj5FO1hZJdnt8vpnXFZyuvxCc6RvTEyMIiIitGTJEmdbSkqKVq5c6QxN9erVk5+fn0ufw4cPa8uWLZcNVgAAAADgjgK1x+rMmTP6/fffnY/37t2rjRs3KjQ0VBUrVtTgwYM1btw4VatWTdWqVdO4ceMUFBSk7t27S5JKlCihvn37aujQoSpdurRCQ0M1bNgw1a5d23mVQAAAAADwtAIVrNavX68WLVo4H2ec99SrVy/NmjVLw4cPl91u14ABA5SUlKQGDRpo8eLFCgkJcT5n0qRJ8vX1VZcuXWS329WyZUvNmjVLPj7XyL5hAAAAAF5XoIJV8+bNdbmLFFosFiUkJCghISHbPoGBgZoyZYqmTJmSBxUCAAAAQGaF5hwrAAAAACioCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAAAAAOAmghUAAAAAuIlgBQAAAABuIlgBAAAAgJsIVgAAAADgJoIVAAAAALiJYAUAAAAAbiJYAQAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4qssHqzTffVExMjAIDA1WvXj2tXr06v0sCAAAAUEQVyWD14YcfavDgwRo5cqR++eUXNWnSRG3bttWBAwfyuzQAAAAARVCRDFYTJ05U37599eCDD6pmzZqaPHmyoqKiNG3atPwuDQAAAEAR5JvfBXhaSkqKNmzYoH//+98u7W3atNHatWuzfE5ycrKSk5Odj0+ePClJOnHihFJTU/Ou2Bw4deqUAgMDdUzH5JAjX2vJaz6BPqp6rqoOBR5SuknP73Ly1AmdUKACderUKR0/ftwry8wYSyeOHZbDkeaVZeYXXx8fnataRn8d2q+09KI7lv4+cVSBgd4dR9I/Y0k6JhXh7VJgoI/OnauqwMBDMkV8mySdkPJpm6REyaQZrywzvzh8HDpX7pwc+x0y6UV7XXVMXt8uZYylDdulU+e8ssh841Cgzvme0+qfA2VV0R5Luw5IgYHy+mdcVk6fPi1JMubyr7nFXKlHIfPnn3+qfPny+u6779SoUSNn+7hx4zR79mzt2LEj03MSEhI0evRob5YJAAAAoBD5448/VKFChWynF7k9VhksFovLY2NMprYMI0aM0JAhQ5yPHQ6HTpw4odKlS2f7HHjeqVOnFBUVpT/++EPFixfP73JQiDGW4AmMI3gKYwmewljKH8YYnT59WpGRkZftV+SCVVhYmHx8fJSYmOjSfuTIEYWHh2f5nICAAAUEBLi0lSxZMq9KxBUUL16cjQU8grEET2AcwVMYS/AUxpL3lShR4op9itzFK/z9/VWvXj0tWbLEpX3JkiUuhwYCAAAAgKcUuT1WkjRkyBD16NFD9evX16233qp33nlHBw4c0MMPP5zfpQEAAAAogopksOratauOHz+uMWPG6PDhw4qNjdWiRYsUHR2d36XhMgICAjRq1KhMh2UCV4uxBE9gHMFTGEvwFMZSwVbkrgoIAAAAAN5W5M6xAgAAAABvI1gBAAAAgJsIVgAAAADgJoIVJEmVKlXS5MmT87uMPFGU1w2A982aNYt7HSJL+/btk8Vi0caNG/O7FOCKEhISdMMNN1yxH39H5RzBqoDo3bu3LBZLlpeEHzBggCwWi3r37u32crL7g2DdunXq16+f2/O/nIwPHF9fXx06dMhl2uHDh+Xr6yuLxaJ9+/blaR15pXfv3urQoUN+l5Hvjhw5ov79+6tixYoKCAhQRESE4uLi9P333+d3aSgirjTGLBaLPv300/wt8iqw7SgcLBbLZf954jMahUth/7wbNmyYli1blt9lFClF8nLrhVVUVJTmz5+vSZMmyWazSZLOnz+vDz74QBUrVnR7/qmpqdlOK1OmjNvzz6nIyEi99957GjFihLNt9uzZKl++vA4cOOC1OgqqlJQU+fv753cZuda5c2elpqZq9uzZqly5sv766y8tW7ZMJ06cyNe6Cvvrin94a4wxZnCxw4cPO///4Ycf6rnnntOOHTucbTabTUlJSXmybMZiwVTYP++KFSumYsWKuT0fXMSgQOjVq5e55557TO3atc3cuXOd7e+//76pXbu2ueeee0yvXr2c7V999ZVp3LixKVGihAkNDTV33XWX+f33353T9+7daySZDz/80DRr1swEBASYGTNmGEku/0aNGmWMMSY6OtpMmjTJ+fwJEyaY2NhYExQUZCpUqGAeeeQRc/r0aef0mTNnmhIlSpivv/7a1KhRwwQHB5u4uDjz559/ZruOGTU988wzplq1ai7Tqlevbp599lkjyezdu9cYY0xaWprp06ePqVSpkgkMDDTXXXedmTx5cpav2yuvvGIiIiJMaGioGTBggElJSXH2iY6ONmPHjjXx8fGmWLFiJioqyrz99tvO6StWrDCSTFJSkrPtl19+canlSus7atSoTK/tihUrjDHGHDx40HTp0sWULFnShIaGmvbt2zvne/E6jBs3zpQrV85ER0dn+xoWdElJSUaS+fbbb7OcnjEGfvnll0zPyXi9Mn4fX3zxhalTp44JCAgwt9xyi/n1119d5vXdd9+ZJk2amMDAQFOhQgUzaNAgc+bMGef06Oho8/zzz5tevXqZ4sWLm549ezp/j//3f/9nrrvuOmOz2Uznzp3NmTNnzKxZs0x0dLQpWbKkefTRR01aWppzXnPmzDH16tUzxYoVM+Hh4aZbt27mr7/+ck7PqHnp0qWmXr16xmazmVtvvdVs377dA68qLnalMRYdHe3yPsx4P/3++++mffv2pmzZsiY4ONjUr1/fLFmyJNNzLx0zxlx4/0dFRRmbzWY6dOhgXn31VVOiRAnn8zLewxd7/PHHTbNmzZyPP/roIxMbG2sCAwNNaGioadmypTlz5sxltx0ouDK2JZfK2MZ98sknpnnz5sZms5k6deqYtWvXuvT7+OOPTa1atYy/v7+Jjo42r776qsv07MYiCo6C/nlnjDHvvPOOqVChgnPbNWHCBJdxO2rUKFO3bl3n4+z+Hrn0b0Rkj0MBC5j4+HjNnDnT+XjGjBnq06dPpn5nz57VkCFDtG7dOi1btkxWq1UdO3aUw+Fw6ffUU0/pscce07Zt29SyZUtNnjxZxYsX1+HDh3X48GENGzYsyzqsVqtef/11bdmyRbNnz9by5cs1fPhwlz7nzp3Tq6++qjlz5mjVqlU6cOBAtvO7WPv27ZWUlKQ1a9ZIktasWaMTJ06oXbt2Lv0cDocqVKig//73v/rtt9/03HPP6emnn9Z///tfl34rVqzQ7t27tWLFCs2ePVuzZs3SrFmzXPpMmDBB9evX1y+//KIBAwbokUce0fbt269Ya07Xd9iwYerSpYvuuOMO52vbqFEjnTt3Ti1atFCxYsW0atUqrVmzRsWKFdMdd9yhlJQU57yXLVumbdu2acmSJfriiy+uqq6CJOPbr08//VTJycluzevJJ5/Uq6++qnXr1qls2bJq3769c6/r5s2bFRcXp06dOunXX3/Vhx9+qDVr1ujRRx91mccrr7yi2NhYbdiwQc8++6ykC7/H119/XfPnz9fXX3+tb7/9Vp06ddKiRYu0aNEizZkzR++8844+/vhj53xSUlL0/PPPa9OmTfr000+1d+/eLA/7GTlypCZMmKD169fL19c3y/cu3HOlMbZu3TpJ0syZM3X48GHn4zNnzujOO+/U0qVL9csvvyguLk7t2rXLtJf80jHz448/qk+fPhowYIA2btyoFi1a6IUXXriqmg8fPqxu3bqpT58+2rZtm3PMGWOy3XagcBs5cqSGDRumjRs36rrrrlO3bt2UlpYmSdqwYYO6dOmi+++/X5s3b1ZCQoKeffbZTJ9bWW2/UHAU9M+77777Tg8//LAef/xxbdy4Ua1bt9bYsWOvWEtR+Xsk3+R3ssMFGd8SHD161AQEBJi9e/eaffv2mcDAQHP06NFMe6wudeTIESPJbN682Rjzzzcll+7hye5btit9G/Hf//7XlC5d2mU+klz2kr3xxhsmPDw823lc/O3N4MGDTXx8vDHGmPj4ePPEE09k2kuUlQEDBpjOnTs7H/fq1ctER0e77F247777TNeuXV3W7V//+pfzscPhMGXLljXTpk0zxuR8j9WV1jerb62nT59uqlevbhwOh7MtOTnZ2Gw288033zifFx4ebpKTk7Nd78Lk448/NqVKlTKBgYGmUaNGZsSIEWbTpk3GmKv7Bm/+/PnOPsePHzc2m818+OGHxhhjevToYfr16+ey3NWrVxur1Wrsdrsx5sLvvUOHDi59svo99u/f3wQFBbnskY2LizP9+/fPdh1/+uknI8n5nIv3WGX48ssvjSRnPfCcy40xY4yRZBYuXHjF+dSqVctMmTLF+TirMdOtWzdzxx13uLR17dr1qvZYbdiwwUgy+/bty7KOrJ6Pgu1Ke6zeffddZ9vWrVuNJLNt2zZjjDHdu3c3rVu3dnnek08+aWrVquV8nNVYRMFTkD/vunbtau666y6XtgceeOCKe6yy+nuEPVY5xx6rAiYsLEx33XWXZs+erZkzZ+quu+5SWFhYpn67d+9W9+7dVblyZRUvXlwxMTGSlOnb1/r16+eqjhUrVqh169YqX768QkJC1LNnTx0/flxnz5519gkKClKVKlWcj8uVK6cjR47kaP59+/bVRx99pMTERH300UfZfrP/1ltvqX79+ipTpoyKFSum//znP5nW8frrr5ePj89l66hTp47z/xaLRRERETmuNUNu1nfDhg36/fffFRIS4vx2KzQ0VOfPn9fu3bud/WrXrl1kjmPu3Lmz/vzzT33++eeKi4vTt99+q5tuuinTt7FXcuuttzr/HxoaqurVq2vbtm2SLryus2bNcr6mxYoVU1xcnBwOh/bu3et8Xlbj/9LfY3h4uCpVquRynHl4eLjL7/aXX37RPffco+joaIWEhKh58+aSMr/fLh5n5cqVk6SrHme4styMsbNnz2r48OGqVauWSpYsqWLFimn79u1X3GZu27bNZSxKyvT4SurWrauWLVuqdu3auu+++/Sf//wnz87FQcFwuW3Btm3b1LhxY5f+jRs31q5du5Senu5sy+3nN7ynIH/e7dixQ7fccotL26WPs1KU/h7JDwSrAqhPnz6aNWuWZs+enW3gaNeunY4fP67//Oc/+vHHH/Xjjz9KksvhZZIUHBx81cvfv3+/7rzzTsXGxuqTTz7Rhg0b9MYbb0hyvQCGn5+fy/MsFouMMTlaRmxsrGrUqKFu3bqpZs2aio2NzdTnv//9r5544gn16dNHixcv1saNGxUfH59pHbOq49JDIi/Xx2q98Da4uPasLvSRm/V1OByqV6+eNm7c6PJv586d6t69u7Nfbn5PBVlgYKBat26t5557TmvXrlXv3r01atSoHL/W2bFYLJIuvK79+/d3eU03bdqkXbt2uYSmrF7XrH6PlxsfZ8+eVZs2bVSsWDHNnTtX69at08KFCyVlfr9dPJ+La4XnZTfGsvPkk0/qk08+0dixY7V69Wpt3LhRtWvXvuI2MyfbNKvVmqnfxePax8dHS5Ys0VdffaVatWppypQpql69ussfRShaLrctMMY42zJkNc6K2udCUVVQP+9yOs4uxbhzD1cFLIAuPv8mLi4u0/Tjx49r27Ztevvtt9WkSRNJcp6vdCX+/v4u34hlZf369UpLS9OECROcG4ZLz2vyhIzzFqZNm5bl9NWrV6tRo0YaMGCAs+3ivTyeknFFxMOHD6tUqVKSlKt7kGT12t5000368MMPVbZsWRUvXtztWgurWrVq6dNPP3V5rW+88UZJ2b/WP/zwg/NqmElJSdq5c6dq1Kgh6cLrunXrVlWtWjXPa9++fbuOHTuml156SVFRUZIuvEdQsGSMMenCH7WXvhdXr16t3r17q2PHjpIunHOVk1s71KpVSz/88INL26WPy5Qpoy1btri0bdy4MdMf140bN1bjxo313HPPKTo6WgsXLtSQIUNytF1G0VGrVq1Mn9lr167Vdddd53L0BQqngvJ5V6NGDf30008ubXx25T32WBVAPj4+2rZtm7Zt25blRrZUqVIqXbq03nnnHf3+++9avny5hgwZkqN5V6pUSWfOnNGyZct07NgxnTt3LlOfKlWqKC0tTVOmTNGePXs0Z84cvfXWW26v16UeeughHT16VA8++GCW06tWrar169frm2++0c6dO/Xss886T0T3pKpVqyoqKkoJCQnauXOnvvzyS02YMOGq51OpUiX9+uuv2rFjh44dO6bU1FQ98MADCgsL0z333KPVq1dr7969WrlypR5//HEdPHjQ4+uS344fP67bb79dc+fO1a+//qq9e/fqo48+0vjx43XPPffIZrOpYcOGeumll/Tbb79p1apVeuaZZ7Kc15gxY7Rs2TJt2bJFvXv3VlhYmPNeP0899ZS+//57DRw4UBs3btSuXbv0+eefa9CgQR5fp4oVK8rf39/5fvj888/1/PPPe3w5yJkrjTHpwntx2bJlSkxMdB5yV7VqVS1YsMD5bW/37t1ztDfxscce09dff63x48dr586dmjp1qr7++muXPrfffrvWr1+v9957T7t27dKoUaNcgtaPP/6ocePGaf369Tpw4IAWLFigo0ePqmbNms56L912oOgaOnSoli1bpueff147d+7U7NmzNXXq1Bxd/AkFR0H/vBs0aJAWLVqkiRMnateuXXr77bf11VdfZdqLBc8iWBVQxYsXz3YPh9Vq1fz587VhwwbFxsbqiSee0CuvvJKj+TZq1EgPP/ywunbtqjJlymj8+PGZ+txwww2aOHGiXn75ZcXGxur999/Xiy++6Nb6ZMXX11dhYWHy9c16x+nDDz+sTp06qWvXrmrQoIGOHz/usvfKU/z8/PTBBx9o+/btqlu3rl5++eWrvuqXdCEoVq9e3XlO2HfffaegoCCtWrVKFStWVKdOnVSzZk316dNHdru9SO7BKlasmBo0aKBJkyapadOmio2N1bPPPquHHnpIU6dOlXThSpepqamqX7++Hn/88Wxf65deekmPP/646tWrp8OHD+vzzz93Hvddp04drVy5Urt27VKTJk1044036tlnn3Wey+BJZcqU0axZs/TRRx+pVq1aeumll/Tqq696fDnImZyMsQkTJmjJkiWKiopyflM8adIklSpVSo0aNVK7du0UFxenm2666YrLa9iwod59911NmTJFN9xwgxYvXpzpj6O4uDg9++yzGj58uG6++WadPn1aPXv2dE4vXry4Vq1apTvvvFPXXXednnnmGU2YMEFt27aVlPW2A0XXTTfdpP/+97+aP3++YmNj9dxzz2nMmDHcYLiQKeifd40bN9Zbb72liRMnqm7duvr666/1xBNPKDAw0LMvBFxYTE5PigEAL/j222/VokULJSUlqWTJkvldDgAAecLbn3cPPfSQtm/frtWrV+f5sq5VnGMFAAAAFDGvvvqqWrdureDgYH311VeaPXu23nzzzfwuq0gjWAEAAABFzE8//aTx48fr9OnTqly5sl5//fVsz2uHZ3AoIAAAAAC4iYtXAAAAAICbCFYAAAAA4CaCFQAAAAC4iWAFAAAAAG4iWAEAAACAmwhWAACvOnHihPr06aPy5cvLarXqhhtuyNV89u3bJ4vFooSEBI/Wdy359ttvZbFYNGvWrPwuBQAKPYIVAOSBjD9YLRaL3nrrrSz7WCwW3XHHHV6uLP8NGzZM77//vvr166f33ntP48aNu2z/hIQEffrpp94pLgcWLVqkNm3aqEKFCgoICFC5cuXUqFEjDR8+XMeOHcvv8gAA+YRgBQB5bPTo0Tp79mx+l1FgfP3117rjjjs0atQo/etf/9Kdd9552f6jR48uMMHq6aef1l133aWjR49qwIABevPNNzVgwABFRETo9ddf1759+/K7RABAPvHN7wIAoCirX7++1q9fr0mTJumZZ57J73IKhMTERJUqVSq/y7hqR48e1fjx49WgQQOtWbNGvr6uH6EnT56Uj49PPlV3dRwOh5KTk72+3DNnzqhYsWJeXy4AeAN7rAAgD3Xu3Fm33HKLXnnllRwdJrZ48WJ17dpVlStXls1mU8mSJdWmTRutXLkyU9/mzZurUqVK2rdvnzp27KiSJUuqVKlS6t27t86cOSOHw6Fx48YpJiZGAQEBuvHGG7V69epM8zHGaNq0aapXr56CgoIUEhKiFi1aaMWKFTlez+PHj+uxxx5TxYoV5e/vr8jISD344IM6fPiws09CQoIsFouMMZo9e7bzUMnszu/JOJxSkkv/SpUqZer76aefql69egoMDFS5cuX05JNPKi0tLVO/Xbt2qUePHipXrpz8/f1VqVIlPfnkkznao7h7926lp6frtttuyxSqJKlEiRIuoSFjfbPai1WpUiU1b97cpc1isah3795aunSpGjZsqKCgIIWHh+uxxx7TmTNnMs3j5MmTeuqpp1S1alUFBASoTJky6tatm/bs2ePSb9asWbJYLFq6dKmef/55ValSRQEBAfrwww9d+r377ruqVauWAgICFB0drfHjx2f5Oqxfv14dO3ZUWFiYAgICVL16dY0dOzbT650xPvfs2aN7771XoaGhCgkJcU7fsmWLOnfu7DKfMWPGZBn4crqu58+fV0JCgmrUqKGgoCAVL15cNWrU0GOPPZblugCAJ7HHCgDy2Msvv6wWLVrohRde0OTJky/bd9asWfr7778VHx+vcuXK6dChQ3r33XfVsmVLrVixQk2aNHHpf/bsWbVo0UItWrTQSy+9pA0bNujdd9+V3W5XWFiYfvrpJw0aNEipqal69dVX1b59e+3fv1/Fixd3zqNHjx764IMPdO+99yo+Pl7Jycl6//331bp1ay1YsEDt27e/bM2nTp3Sbbfdph07dqhXr1665ZZbtGXLFr399ttavHix1q1bp/DwcHXq1ElVq1ZVjx491KRJE/Xr10+S1KhRoyznW7NmTc2ZMydT/0v3eCxatEhvvvmmHn74YT344IP67LPP9Oqrr6pUqVJ6+umnnf02bNig22+/XSVLllT//v1Vvnx5/frrr3r99df13XffaeXKlfLz88t2PStXrixJ+uKLLzRkyBBFRkZe9nXJjZ9//lkff/yxHnroIfXs2VMrVqzQlClT9Ouvv2r58uWyWi98H3ry5Ek1atRIBw4cUJ8+fXT99dfr8OHDmjZtmho0aKD169crOjraZd7Dhg1TWlqaHnroIRUvXlzVq1d3hphp06bpyJEjevDBB1WiRAnNnTtXTz31lCpUqKDu3bs757Fo0SJ17NhRVatW1dChQxUaGqrvv/9ezz33nDZu3KiPPvrIZZlnzpxRs2bNdNttt2ns2LE6cuSIcz2bNm0qq9WqgQMHqkKFCvrmm280atQoff/99/ryyy9zta4DBw7UjBkz1KNHDw0ePFgOh0O7d+/WkiVLPP67AoBMDADA41asWGEkmRdffNEYY8wdd9xh/P39zd69e519JJm4uDiX5505cybTvBITE03p0qVN27ZtXdqbNWtmJJkJEya4tHfu3NlYLBZz8803m9TUVGf7Z599ZiSZadOmOds++eQTI8m89dZbLvNITU019erVM5UqVTIOh+Oy6zpy5EgjyUyePNmlfe7cuUaSeeihh1zaJZlevXpddp456b93714jyQQFBbm8rg6Hw1x//fUmIiLCpX+dOnXMddddZ06dOuXSvmDBAiPJzJw584q1PProo0aS8ff3N02aNDFPPvmk+fjjj01SUlKmvqNGjTKSXGrLEB0dbZo1a5ZpPSWZhQsXurQ/9thjRpKZO3eus23QoEEmMDDQbNy40aXvvn37TEhIiMvrNXPmTCPJVK9e3Zw7d86lf8Y4LVeunMs6nD171oSFhZmGDRs62+x2uylbtqxp0qSJy7gyxpiJEycaSWbFihXOtozx+dxzz2Va/8aNGxur1Wo2bNjg0v7QQw8ZSeb999/P1bqWKlXK3HnnnZmWBwDewKGAAOAFL7/8stLS0q54nlVwcLDz/2fOnNHx48fl4+OjBg0a6Mcff8zU38fHRwMHDnRpa9y4sYwx6t+/v8shaxl7u37//Xdn2/vvv6/g4GB16NBBx44dc/77+++/1a5dO+3bt0+7du26bM0LFy5UaGioBgwY4NLevXt3Va1aVQsXLrzs893VoUMHl8MDLRaLWrRoocTEROchdJs3b9avv/6q+++/X8nJyS7rettttyk4OFiLFy++4rJef/11zZ49Ww0bNtSPP/6oV155Rffee68iIiL01FNPKT093a11qV69ujp06ODS9u9//1uSnK+jMUbz5s1T48aNVb58eZd1CQ4OVsOGDbNcl0ceeUQ2my3L5cbHx6tkyZLOx0FBQWrYsKHL737JkiU6cuSIevbsqb///ttluRkXIMlquUOGDHF5fPToUX333Xe66667dNNNN7lMe/bZZyVJCxYsyNW6lixZUlu2bNHmzZuzXE8AyEscCggAXlCnTh11795d77//voYNG5btvZt2796tkSNH6ptvvtHff//tMi3jfKOLlStXTgEBAS5tGReGuPRcpIz248ePO9u2bdums2fPKiIiItva//rrL1133XXZTt+zZ49uuOGGTIfRWSwWXX/99frss8906tQpl8MPPSnjEL2LlS5dWtKFdS1WrJi2bdsmSRozZozGjBmT5Xz++uuvKy7LYrGoZ8+e6tmzp5KTk7V161Z98803mjRpksaPH6+SJUtqxIgRuV6XmjVrZmorV66cSpYsqd27d0u6EEyOHz+uZcuWqUyZMlnOJ+MwuotVq1Yt2+Vm9xpeOlYk6aGHHtJDDz2U5XwufQ3LlCmjEiVKuLRlnBd1/fXXZ3p+VFSUSpQo4exztev62muv6V//+pfq1KmjmJgYtWjRQnfffbfuueeeLF8TAPAkghUAeMkLL7ygjz76SP/+97/19ddfZ5p++vRpNWnSROfOndPgwYNVu3ZthYSEyGq16sUXX9Ty5cszPedyV6HLbpoxxuX/oaGhmS5kcLHY2NjLrdZlXbysvHK51yBj+Rk/Bw8erLvuuivLvld7pcKAgADddNNNuummm9SpUyfVrFlT06dPdwarrIJwhqwurHG55xhjnNMy1qVFixYu55BdSVBQULbTcnI1w4zlvvTSS6pXr16WfS497yyrZV7NmLjadc3Yy/rVV1/p22+/1fLlyzVjxgw1aNBAK1asyHaPHQB4AsEKALwkOjpajzzyiCZPnpxlSFq+fLkOHz6sGTNmKD4+3mVaXl2q/brrrtOOHTt08803Z9qzkFOVK1fWzp07lZqammmv1W+//aawsLA821uVUxl73KxWq1q1auXx+VevXl2lSpXSoUOHnG2hoaGSpBMnTrjsPTx//rwOHz6sqlWrZprPb7/9lqnt8OHDOnnypHOvUpkyZVSyZEmdPHkyT9YlOxmvYVBQkFvLrVKliiRp69atmaYdPHhQJ0+edPbJzbqWKlVK3bt3d150Y/To0UpISND8+fMzva8AwJPYLw4AXvTMM8+oePHizvNmLpax1+DSb/QXL16c5flVntCjRw8ZYzRixIgs9yTk5PC4jh076sSJE3r77bdd2ufPn6/ff/9dnTp1cqvGYsWKKSkpya153HDDDapdu7beeecdl3PMMqSlpenEiROXnUdiYqJ++eWXLKetXr1aJ06cUK1atZxtGUFk6dKlLn0nTZokh8OR5Xx27NiR6WbIL7/8sqQLr7N0IRw+8MAD+vnnnzV//vws55Nx9T1PiouLU9myZTV+/Pgsbx1gt9t1+vTpK86nTJkyaty4sRYtWqSNGze6TBs7dqwkOcfM1axrenp6psNnJTnP47rS7xcA3MUeKwDwotKlS2v48OFZ7oG67bbbFBERoaFDh2rfvn2qUKGCNm7cqDlz5qh27dp5ckJ+xiXWp02bpo0bN6pdu3YKCwvTwYMH9f333+v333/PdK+gSw0fPlwff/yxHnvsMf3yyy+6+eabnZdbr1ChQrbnNOVUgwYNtHTpUr3yyiuKiopScHCw2rVrd1XzsFgseu+993T77bfrhhtucF62+9y5c/r999+1YMECvfjii+rdu3e28zh48KBuvvlm3XzzzWrdurViYmKUkpKiTZs26f3335efn5/GjRvn7N+qVSvVqFFDzz33nI4fP66YmBitWbNGP/zwg8LCwrJcRu3atfWvf/1LDz30kKpVq6YVK1bo448/VrNmzdStWzdnv7Fjx+q7775T9+7dtXDhQt16663y9/fX/v37tWjRItWrVy/b+4PlVlBQkN577z116NBBNWrUUJ8+fVStWjX9/fff2r59uxYsWKCFCxdmuj9XVl5//XU1bdpUzZo108CBA1W+fHktXrxYn3/+ueLi4tS1a9erXtfTp0+rXLlyat++vW644QaFh4dr//79euutt1SsWDG3Az4AXJH3L0QIAEXfpZdbv9jZs2dNuXLlsrzc+qZNm0xcXJwpWbKkKVasmGnWrJlZtWqV6dWrl7l0k92sWTMTHR2daf4Zl9e++NLXGZTNpcvfe+89c9ttt5mQkBATEBBgoqOjTceOHc38+fNztL7Hjh0zjz76qKlQoYLx8/MzERERpm/fvubQoUM5riE727dvN7fffrspVqyYkeRc54zLrY8aNSrTc7K71Pm+fftM//79TXR0tPHz8zOhoaHmpptuMv/+97/NgQMHLlvH6dOnzRtvvGE6dOhgKleubIKDg42/v7+Jjo42DzzwgPn5558zPWfHjh0mLi7O2Gw2U6JECXPfffeZgwcPZnu59V69epklS5aYW265xQQGBpqyZcuaRx99NNMl4o25MI7GjBljYmNjTWBgoClWrJipUaOGefDBB80PP/zg7He58ZAxTrO61HxWY84YYzZv3mweeOABExkZafz8/EzZsmXNrbfeasaMGWOOHz/u7Jfd+Lx4Ph07djShoaHGz8/PVKtWzSQkJJjz58/nal2Tk5PNv//9b3PLLbeY0NBQ4+/vbypWrGh69Ohhfvvtt2zrAABPsRjjhTOLAQDAZVksFvXq1cvje5oAAN7BOVYAAAAA4CaCFQAAAAC4iWAFAAAAAG7iqoAAABQAnPIMAIUbe6wAAAAAwE0EKwAAAABwE8EKAAAAANxEsAIAAAAANxGsAAAAAMBNBCsAAAAAcBPBCgAAAADcRLACAAAAADf9PzThtEWDHpVuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 superheroes with highest Total.\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['purple','slategray','navy','forestgreen','gold'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Total of the Superheroes\",fontsize=15)\n",
    "plt.xlabel('Name of the Superheroes',fontsize=13)\n",
    "plt.ylabel('Count of the Total',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d485f78",
   "metadata": {},
   "source": [
    "# Supervillains with the highest speed ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "10ef3d81",
   "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>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>bad</td>\n",
       "      <td>63</td>\n",
       "      <td>80</td>\n",
       "      <td>53</td>\n",
       "      <td>90</td>\n",
       "      <td>55</td>\n",
       "      <td>95</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>bad</td>\n",
       "      <td>88</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>99</td>\n",
       "      <td>100</td>\n",
       "      <td>56</td>\n",
       "      <td>526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>bad</td>\n",
       "      <td>50</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>85</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>bad</td>\n",
       "      <td>75</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>bad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>bad</td>\n",
       "      <td>38</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>63</td>\n",
       "      <td>50</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>bad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>bad</td>\n",
       "      <td>38</td>\n",
       "      <td>16</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>41</td>\n",
       "      <td>60</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>bad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>bad</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "      <td>28</td>\n",
       "      <td>72</td>\n",
       "      <td>28</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name Alignment  Intelligence  Strength  Speed  Durability  \\\n",
       "4       Abomination       bad            63        80     53          90   \n",
       "5           Abraxas       bad            88       100     83          99   \n",
       "11       Air-Walker       bad            50        85    100          85   \n",
       "16            Amazo       bad            75       100    100         100   \n",
       "17             Ammo       bad             1         1      1           1   \n",
       "..              ...       ...           ...       ...    ...         ...   \n",
       "586            Warp       bad            38        10     23          28   \n",
       "590       Weapon XI       bad             1         1      1           1   \n",
       "593  Willis Stryker       bad            38        16     23          28   \n",
       "605     Yellow Claw       bad             1         1      1           1   \n",
       "610            Zoom       bad            50        10    100          28   \n",
       "\n",
       "     Power  Combat  Total  \n",
       "4       55      95    436  \n",
       "5      100      56    526  \n",
       "11     100      40    460  \n",
       "16     100     100    575  \n",
       "17       0       1      5  \n",
       "..     ...     ...    ...  \n",
       "586     63      50    212  \n",
       "590      0       1      5  \n",
       "593     41      60    206  \n",
       "605      0       1      5  \n",
       "610     72      28    288  \n",
       "\n",
       "[165 rows x 9 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset.\n",
    "bad=marvel[marvel['Alignment']=='bad']\n",
    "bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5d025d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Name</th>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
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       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>bad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "           Name Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "4   Abomination       bad            63        80     53          90     55   \n",
       "5       Abraxas       bad            88       100     83          99    100   \n",
       "11   Air-Walker       bad            50        85    100          85    100   \n",
       "16        Amazo       bad            75       100    100         100    100   \n",
       "17         Ammo       bad             1         1      1           1      0   \n",
       "\n",
       "    Combat  Total  \n",
       "4       95    436  \n",
       "5       56    526  \n",
       "11      40    460  \n",
       "16     100    575  \n",
       "17       1      5  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Glance at the top 5 records of the supervillains.\n",
    "bad.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "02229b09",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "               Name  Speed\n",
       "4       Abomination     53\n",
       "5           Abraxas     83\n",
       "11       Air-Walker    100\n",
       "16            Amazo    100\n",
       "17             Ammo      1\n",
       "..              ...    ...\n",
       "586            Warp     23\n",
       "590       Weapon XI      1\n",
       "593  Willis Stryker     23\n",
       "605     Yellow Claw      1\n",
       "610            Zoom    100\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make a dataset from the bad dataset which tells us about the Name and Speed of the supervillains.\n",
    "bad_speed=bad[['Name','Speed']]\n",
    "bad_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "376f00d5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Cheetah II</td>\n",
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      ],
      "text/plain": [
       "                Name  Speed\n",
       "610             Zoom    100\n",
       "11        Air-Walker    100\n",
       "16             Amazo    100\n",
       "535   Superboy-Prime    100\n",
       "242      General Zod     96\n",
       "..               ...    ...\n",
       "256  Granny Goodness      1\n",
       "254      Goliath III      1\n",
       "503         Siren II      1\n",
       "154      Cheetah III      1\n",
       "153       Cheetah II      1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their speed.\n",
    "bad_speed=bad_speed.sort_values(by='Speed',ascending=False)\n",
    "bad_speed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c484059a",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "               Name  Speed\n",
       "610            Zoom    100\n",
       "11       Air-Walker    100\n",
       "16            Amazo    100\n",
       "535  Superboy-Prime    100\n",
       "242     General Zod     96"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest speed.\n",
    "bad_speed.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "210f1c32",
   "metadata": {},
   "source": [
    "# Who is the most intelligent supervillain ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a796acf4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Willis Stryker</td>\n",
       "      <td>38</td>\n",
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       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Intelligence\n",
       "4       Abomination            63\n",
       "5           Abraxas            88\n",
       "11       Air-Walker            50\n",
       "16            Amazo            75\n",
       "17             Ammo             1\n",
       "..              ...           ...\n",
       "586            Warp            38\n",
       "590       Weapon XI             1\n",
       "593  Willis Stryker            38\n",
       "605     Yellow Claw             1\n",
       "610            Zoom            50\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Intelligence.\n",
    "bad_intelligence=bad[['Name','Intelligence']]\n",
    "bad_intelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ba590ab8",
   "metadata": {},
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Intelligence\n",
       "386  Mister Mxyzptlk           113\n",
       "338       Lex Luthor           100\n",
       "336           Leader           100\n",
       "122         Brainiac           100\n",
       "194      Doctor Doom           100\n",
       "..               ...           ...\n",
       "460      Redeemer II             1\n",
       "154      Cheetah III             1\n",
       "153       Cheetah II             1\n",
       "152        Cheetah I             1\n",
       "461     Redeemer III             1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Intelligence.\n",
    "bad_intelligence=bad_intelligence.sort_values(by='Intelligence',ascending=False)\n",
    "bad_intelligence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3ff62ed3",
   "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>Intelligence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Mister Mxyzptlk</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>Lex Luthor</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>Leader</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Brainiac</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>Doctor Doom</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Intelligence\n",
       "386  Mister Mxyzptlk           113\n",
       "338       Lex Luthor           100\n",
       "336           Leader           100\n",
       "122         Brainiac           100\n",
       "194      Doctor Doom           100"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest intelligence.\n",
    "bad_intelligence.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1838f064",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Mister Mxyzptlk', 'Lex Luthor', 'Leader', 'Brainiac', 'Doctor Doom']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Names of the top 5 supervillains with highest Intelligence. \n",
    "x=list(bad_intelligence['Name'][0:5])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "46357d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[113, 100, 100, 100, 100]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the Intelligenceof the top 5 supervillains.\n",
    "y=list(bad_intelligence['Intelligence'][0:5])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "692ab11e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 supervillains with highest Intelligence.\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['black','rosybrown','red','orangered','salmon'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Intelligence of the Supervillains\",fontsize=15)\n",
    "plt.xlabel('Name of the Supervillains',fontsize=13)\n",
    "plt.ylabel('Count of the Intelligence',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dacf015",
   "metadata": {},
   "source": [
    "# Supervillains with the highest Strength ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "466da319",
   "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>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Strength\n",
       "4       Abomination        80\n",
       "5           Abraxas       100\n",
       "11       Air-Walker        85\n",
       "16            Amazo       100\n",
       "17             Ammo         1\n",
       "..              ...       ...\n",
       "586            Warp        10\n",
       "590       Weapon XI         1\n",
       "593  Willis Stryker        16\n",
       "605     Yellow Claw         1\n",
       "610            Zoom        10\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Strength.\n",
    "bad_strength=bad[['Name','Strength']]\n",
    "bad_strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "71a417d6",
   "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>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Black Adam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Superboy-Prime</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Thanos</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Lady Deathstrike</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>Lady Bullseye</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>Curse</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>Bloodwraith</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>Cy-Gor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Strength\n",
       "180          Darkseid       100\n",
       "84         Black Adam       100\n",
       "535    Superboy-Prime       100\n",
       "542            Thanos       100\n",
       "5             Abraxas       100\n",
       "..                ...       ...\n",
       "335  Lady Deathstrike         1\n",
       "334     Lady Bullseye         1\n",
       "168             Curse         1\n",
       "109       Bloodwraith         1\n",
       "169            Cy-Gor         1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Strength.\n",
    "bad_strength=bad_strength.sort_values(by='Strength',ascending=False)\n",
    "bad_strength"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9920649c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Black Adam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Superboy-Prime</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>Thanos</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Strength\n",
       "180        Darkseid       100\n",
       "84       Black Adam       100\n",
       "535  Superboy-Prime       100\n",
       "542          Thanos       100\n",
       "5           Abraxas       100"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest Strength.\n",
    "bad_strength.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7c355d8",
   "metadata": {},
   "source": [
    "# Who is the most durable supervillain ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f376f06a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>Durability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Durability\n",
       "4       Abomination          90\n",
       "5           Abraxas          99\n",
       "11       Air-Walker          85\n",
       "16            Amazo         100\n",
       "17             Ammo           1\n",
       "..              ...         ...\n",
       "586            Warp          28\n",
       "590       Weapon XI           1\n",
       "593  Willis Stryker          28\n",
       "605     Yellow Claw           1\n",
       "610            Zoom          28\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Durability.\n",
    "bad_durability=bad[['Name','Durability']]\n",
    "bad_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "5db3870c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>Durability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Doomsday</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Lobo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Cyborg Superman</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Black Adam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>Omega Red</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Billy Kincaid</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>Overtkill</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>Razor-Fist II</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Bird-Man II</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Durability\n",
       "201         Doomsday         120\n",
       "180         Darkseid         100\n",
       "345             Lobo         100\n",
       "171  Cyborg Superman         100\n",
       "84        Black Adam         100\n",
       "..               ...         ...\n",
       "420        Omega Red           1\n",
       "75     Billy Kincaid           1\n",
       "425        Overtkill           1\n",
       "452    Razor-Fist II           1\n",
       "79       Bird-Man II           1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Durability.\n",
    "bad_durability=bad_durability.sort_values(by='Durability',ascending=False)\n",
    "bad_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "bb981254",
   "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>Durability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Doomsday</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>Lobo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>Cyborg Superman</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>Black Adam</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Durability\n",
       "201         Doomsday         120\n",
       "180         Darkseid         100\n",
       "345             Lobo         100\n",
       "171  Cyborg Superman         100\n",
       "84        Black Adam         100"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest Durability.\n",
    "bad_durability=bad_durability.head()\n",
    "bad_durability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ba1a6976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Doomsday', 'Darkseid', 'Lobo', 'Cyborg Superman', 'Black Adam']"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the 5 most durable supervillains.\n",
    "x=list(bad_durability['Name'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "acd04b1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[120, 100, 100, 100, 100]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the durability of the top 5 supervillains.\n",
    "y=list(bad_durability['Durability'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bf6a8b97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 supervillains with highest durability.\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['purple','yellow','green','black','red'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Durability of the Supervillains\",fontsize=15)\n",
    "plt.xlabel('Name of the Supervillains',fontsize=13)\n",
    "plt.ylabel('Count of the Durability',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "998061e1",
   "metadata": {},
   "source": [
    "# Supervillains with the highest Power ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4bb7d4e0",
   "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>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Power\n",
       "4       Abomination     55\n",
       "5           Abraxas    100\n",
       "11       Air-Walker    100\n",
       "16            Amazo    100\n",
       "17             Ammo      0\n",
       "..              ...    ...\n",
       "586            Warp     63\n",
       "590       Weapon XI      0\n",
       "593  Willis Stryker     41\n",
       "605     Yellow Claw      0\n",
       "610            Zoom     72\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Power.\n",
    "bad_power=bad[['Name','Power']]\n",
    "bad_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f2302557",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Ultron</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Onslaught</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Sinestro</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Mister Mxyzptlk</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>Lady Deathstrike</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>Cheetah III</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Blizzard</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>Doctor Octopus</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>Granny Goodness</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name  Power\n",
       "564            Ultron    100\n",
       "422         Onslaught    100\n",
       "180          Darkseid    100\n",
       "501          Sinestro    100\n",
       "386   Mister Mxyzptlk    100\n",
       "..                ...    ...\n",
       "335  Lady Deathstrike      0\n",
       "154       Cheetah III      0\n",
       "103          Blizzard      0\n",
       "197    Doctor Octopus      0\n",
       "256   Granny Goodness      0\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Power.\n",
    "bad_power=bad_power.sort_values(by='Power',ascending=False)\n",
    "bad_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "19774698",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Power</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>Ultron</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>Onslaught</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Sinestro</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>Mister Mxyzptlk</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  Power\n",
       "564           Ultron    100\n",
       "422        Onslaught    100\n",
       "180         Darkseid    100\n",
       "501         Sinestro    100\n",
       "386  Mister Mxyzptlk    100"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest Power.\n",
    "bad_power=bad_power.head()\n",
    "bad_power"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3474f855",
   "metadata": {},
   "source": [
    "# Supervillains with the highest Combat ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "61c2d097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Combat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Combat\n",
       "4       Abomination      95\n",
       "5           Abraxas      56\n",
       "11       Air-Walker      40\n",
       "16            Amazo     100\n",
       "17             Ammo       1\n",
       "..              ...     ...\n",
       "586            Warp      50\n",
       "590       Weapon XI       1\n",
       "593  Willis Stryker      60\n",
       "605     Yellow Claw       1\n",
       "610            Zoom      28\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Combat.\n",
    "bad_combat=bad[['Name','Combat']]\n",
    "bad_combat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "1b818bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\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>Combat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Sabretooth</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Ra's Al Ghul</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Rick Flag</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Redeemer II</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>Redeemer III</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Black Abbott</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Bird-Man</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>Bird-Man II</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Combat\n",
       "475    Sabretooth     100\n",
       "16          Amazo     100\n",
       "449  Ra's Al Ghul     100\n",
       "464     Rick Flag      95\n",
       "180      Darkseid      95\n",
       "..            ...     ...\n",
       "460   Redeemer II       1\n",
       "461  Redeemer III       1\n",
       "83   Black Abbott       1\n",
       "78       Bird-Man       1\n",
       "79    Bird-Man II       1\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Combat.\n",
    "bad_combat=bad_combat.sort_values(by='Combat',ascending=False)\n",
    "bad_combat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "dc15a4bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Combat</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>Sabretooth</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>Ra's Al Ghul</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>464</th>\n",
       "      <td>Rick Flag</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>Darkseid</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Combat\n",
       "475    Sabretooth     100\n",
       "16          Amazo     100\n",
       "449  Ra's Al Ghul     100\n",
       "464     Rick Flag      95\n",
       "180      Darkseid      95"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains who have the highest combat.\n",
    "bad_combat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0ebfa1d",
   "metadata": {},
   "source": [
    "# Who is the best Supervillain ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e556ca71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Name</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Abomination</td>\n",
       "      <td>436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Abraxas</td>\n",
       "      <td>526</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Air-Walker</td>\n",
       "      <td>460</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>575</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Ammo</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>Warp</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>590</th>\n",
       "      <td>Weapon XI</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Willis Stryker</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Yellow Claw</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>Zoom</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Total\n",
       "4       Abomination    436\n",
       "5           Abraxas    526\n",
       "11       Air-Walker    460\n",
       "16            Amazo    575\n",
       "17             Ammo      5\n",
       "..              ...    ...\n",
       "586            Warp    212\n",
       "590       Weapon XI      5\n",
       "593  Willis Stryker    206\n",
       "605     Yellow Claw      5\n",
       "610            Zoom    288\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a new dataset of supervillains from the original dataset about the Total column.\n",
    "bad_total=bad[['Name','Total']]\n",
    "bad_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "eea6308f",
   "metadata": {},
   "outputs": [
    {
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       "    }\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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Superboy-Prime</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>General Zod</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Dormammu</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Doomsday</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Blizzard</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Luke Campbell</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>Darkside</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>Moloch</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>Goliath III</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Total\n",
       "535  Superboy-Prime    579\n",
       "242     General Zod    579\n",
       "16            Amazo    575\n",
       "203        Dormammu    546\n",
       "201        Doomsday    545\n",
       "..              ...    ...\n",
       "103        Blizzard      5\n",
       "349   Luke Campbell      5\n",
       "181        Darkside      5\n",
       "390          Moloch      5\n",
       "254     Goliath III      5\n",
       "\n",
       "[165 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the dataset in descending order of their Total column.\n",
    "bad_total=bad_total.sort_values(by='Total',ascending=False)\n",
    "bad_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "4b3d453f",
   "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>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>535</th>\n",
       "      <td>Superboy-Prime</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>General Zod</td>\n",
       "      <td>579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Amazo</td>\n",
       "      <td>575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>Dormammu</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>Doomsday</td>\n",
       "      <td>545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name  Total\n",
       "535  Superboy-Prime    579\n",
       "242     General Zod    579\n",
       "16            Amazo    575\n",
       "203        Dormammu    546\n",
       "201        Doomsday    545"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 supervillains with highest Total.\n",
    "bad_total=bad_total.head()\n",
    "bad_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "8e6ce219",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Superboy-Prime', 'General Zod', 'Amazo', 'Dormammu', 'Doomsday']"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Name of the 5 supervillains with highest Total.\n",
    "x=list(bad_total['Name'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cfdd9970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[579, 579, 575, 546, 545]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count of the Total of the top 5 supervillains.\n",
    "y=list(bad_total['Total'])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "683e8b1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar-plot for top 5 supervillains with highest Total.\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(x,y,color=['bisque','darkorange','lawngreen','olive','crimson'],ec='black',lw=1)\n",
    "plt.grid(True)\n",
    "plt.title(\"Total of the Supervillains\",fontsize=15)\n",
    "plt.xlabel('Name of the Supervillains',fontsize=13)\n",
    "plt.ylabel('Count of the Total',fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f03b34",
   "metadata": {},
   "source": [
    "# What range of Intelligence most of the characters have ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "aa9bfed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      50\n",
       "1      38\n",
       "2      88\n",
       "3      50\n",
       "4      63\n",
       "       ..\n",
       "606    88\n",
       "607    50\n",
       "608    50\n",
       "609    75\n",
       "610    50\n",
       "Name: Intelligence, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Intelligence column of the dataset.\n",
    "marvel['Intelligence']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5a00260c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Intelligence column of the dataset.\n",
    "sns.displot(marvel['Intelligence'],color=\"purple\",kde=True)\n",
    "plt.title(\"INTELLIGENCE\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Intelligency of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8d0aa8e",
   "metadata": {},
   "source": [
    "# What range of Strength most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "fc821743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       31\n",
       "1      100\n",
       "2       14\n",
       "3       90\n",
       "4       80\n",
       "      ... \n",
       "606     10\n",
       "607     10\n",
       "608    100\n",
       "609     10\n",
       "610     10\n",
       "Name: Strength, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Strength column of the dataset.\n",
    "marvel['Strength']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8c5bb78b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Strength column of the dataset.\n",
    "sns.displot(marvel['Strength'],color=\"lawngreen\",kde=True)\n",
    "plt.title(\"STRENGTH\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Strength of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e045c542",
   "metadata": {},
   "source": [
    "# What range of Speed most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f445ea7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       43\n",
       "1       17\n",
       "2       35\n",
       "3       53\n",
       "4       53\n",
       "      ... \n",
       "606     12\n",
       "607     35\n",
       "608     27\n",
       "609     23\n",
       "610    100\n",
       "Name: Speed, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Speed column of the dataset.\n",
    "marvel['Speed']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f47e37dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Speed column of the dataset.\n",
    "sns.displot(marvel['Speed'],color=\"red\",kde=True)\n",
    "plt.title(\"SPEED\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Speed of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59623aac",
   "metadata": {},
   "source": [
    "# What range of Durability most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3682af36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       32\n",
       "1       80\n",
       "2       42\n",
       "3       64\n",
       "4       90\n",
       "      ... \n",
       "606     28\n",
       "607     28\n",
       "608    100\n",
       "609     28\n",
       "610     28\n",
       "Name: Durability, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Durability column of the dataset.\n",
    "marvel['Durability']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "2114bbba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Durabiity column of the dataset.\n",
    "sns.displot(marvel['Durability'],color=\"blue\",kde=True)\n",
    "plt.title(\"DURABILITY\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Durability of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bfa0e64",
   "metadata": {},
   "source": [
    "# What range of Power most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "f8fcd16b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       25\n",
       "1       17\n",
       "2       35\n",
       "3       84\n",
       "4       55\n",
       "      ... \n",
       "606     12\n",
       "607     31\n",
       "608     83\n",
       "609    100\n",
       "610     72\n",
       "Name: Power, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Power column of the dataset.\n",
    "marvel['Power']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "66ba00fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Power column of the dataset.\n",
    "sns.displot(marvel['Power'],color=\"yellow\",kde=True)\n",
    "plt.title(\"POWER\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Power of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d70a794",
   "metadata": {},
   "source": [
    "# What range of Combat most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "826a91e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      52\n",
       "1      64\n",
       "2      85\n",
       "3      65\n",
       "4      95\n",
       "       ..\n",
       "606    14\n",
       "607    28\n",
       "608    28\n",
       "609    56\n",
       "610    28\n",
       "Name: Combat, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combat column of the dataset.\n",
    "marvel['Combat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "629b9fdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Combat column of the dataset.\n",
    "sns.displot(marvel['Combat'],color=\"maroon\",kde=True)\n",
    "plt.title(\"COMBAT\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Combat of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "907460d0",
   "metadata": {},
   "source": [
    "# What range of Total most of the characters have ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1706506e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      233\n",
       "1      316\n",
       "2      299\n",
       "3      406\n",
       "4      436\n",
       "      ... \n",
       "606    164\n",
       "607    182\n",
       "608    388\n",
       "609    292\n",
       "610    288\n",
       "Name: Total, Length: 611, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total column of the dataset.\n",
    "marvel['Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "3e567ea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displot for the Total column of the dataset.\n",
    "sns.displot(marvel['Total'],color=\"magenta\",kde=True)\n",
    "plt.title(\"TOTAL\",fontsize=15)\n",
    "plt.ylabel('Number of the Characters',fontsize=13)\n",
    "plt.xlabel('Total of the Characters',fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97c16475",
   "metadata": {},
   "source": [
    "# Which allignment of character have the highest value of Total ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "41be611e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 5,\n",
       " 392,\n",
       " 490,\n",
       " 5,\n",
       " 256,\n",
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       " 231,\n",
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       " 299,\n",
       " 470,\n",
       " 5,\n",
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       " 347,\n",
       " 420,\n",
       " 280,\n",
       " 5,\n",
       " 255,\n",
       " 5,\n",
       " 210,\n",
       " 5,\n",
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       " 377,\n",
       " 334,\n",
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       " 170,\n",
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       " 367,\n",
       " 335,\n",
       " 125,\n",
       " 487,\n",
       " 544,\n",
       " 579,\n",
       " 323,\n",
       " 243,\n",
       " 189,\n",
       " 376,\n",
       " 546,\n",
       " 482,\n",
       " 225,\n",
       " 5,\n",
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       " 400,\n",
       " 295,\n",
       " 309,\n",
       " 5,\n",
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       " 350,\n",
       " 5,\n",
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       " 369,\n",
       " 133,\n",
       " 467,\n",
       " 5,\n",
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       " 491,\n",
       " 5,\n",
       " 464,\n",
       " 337,\n",
       " 246,\n",
       " 492,\n",
       " 5,\n",
       " 324,\n",
       " 332,\n",
       " 5,\n",
       " 199,\n",
       " 369,\n",
       " 369,\n",
       " 446,\n",
       " 480,\n",
       " 5,\n",
       " 90,\n",
       " 336,\n",
       " 465,\n",
       " 164,\n",
       " 182,\n",
       " 388,\n",
       " 292]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of all values in total column of good datatset.\n",
    "good=list(good['Total'])\n",
    "good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c6e67089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[436,\n",
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       " 160,\n",
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       " 215,\n",
       " 258,\n",
       " 198,\n",
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       " 285,\n",
       " 428,\n",
       " 157,\n",
       " 5,\n",
       " 213,\n",
       " 323,\n",
       " 5,\n",
       " 5,\n",
       " 579,\n",
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       " 485,\n",
       " 271,\n",
       " 171,\n",
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       " 383,\n",
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       " 5,\n",
       " 206,\n",
       " 5,\n",
       " 288]"
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     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of all values in total column of bad datatset.\n",
    "bad=list(bad['Total'])\n",
    "bad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "c8630f91",
   "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>Alignment</th>\n",
       "      <th>Intelligence</th>\n",
       "      <th>Strength</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Durability</th>\n",
       "      <th>Power</th>\n",
       "      <th>Combat</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Bizarro</td>\n",
       "      <td>neutral</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "      <td>85</td>\n",
       "      <td>550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>Deadpool</td>\n",
       "      <td>neutral</td>\n",
       "      <td>50</td>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>Deathstroke</td>\n",
       "      <td>neutral</td>\n",
       "      <td>75</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>36</td>\n",
       "      <td>90</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>Galactus</td>\n",
       "      <td>neutral</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>83</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "      <td>70</td>\n",
       "      <td>553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>Juggernaut</td>\n",
       "      <td>neutral</td>\n",
       "      <td>44</td>\n",
       "      <td>100</td>\n",
       "      <td>42</td>\n",
       "      <td>100</td>\n",
       "      <td>74</td>\n",
       "      <td>70</td>\n",
       "      <td>430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>Man-Bat</td>\n",
       "      <td>neutral</td>\n",
       "      <td>38</td>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "      <td>70</td>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>Raven</td>\n",
       "      <td>neutral</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>29</td>\n",
       "      <td>70</td>\n",
       "      <td>62</td>\n",
       "      <td>40</td>\n",
       "      <td>261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>Red Hood</td>\n",
       "      <td>neutral</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "      <td>95</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>Sandman</td>\n",
       "      <td>neutral</td>\n",
       "      <td>50</td>\n",
       "      <td>75</td>\n",
       "      <td>47</td>\n",
       "      <td>97</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>The Comedian</td>\n",
       "      <td>neutral</td>\n",
       "      <td>63</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>80</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>Toad</td>\n",
       "      <td>neutral</td>\n",
       "      <td>50</td>\n",
       "      <td>34</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>28</td>\n",
       "      <td>275</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name Alignment  Intelligence  Strength  Speed  Durability  Power  \\\n",
       "82        Bizarro   neutral            75        95    100         100     95   \n",
       "186      Deadpool   neutral            50        15     30         100    100   \n",
       "189   Deathstroke   neutral            75        30     35         100     36   \n",
       "238      Galactus   neutral           100       100     83         100    100   \n",
       "318    Juggernaut   neutral            44       100     42         100     74   \n",
       "357       Man-Bat   neutral            38        18     50          70     33   \n",
       "451         Raven   neutral            50        10     29          70     62   \n",
       "454      Red Hood   neutral            75        12     23          28     24   \n",
       "477       Sandman   neutral            50        75     47          97     62   \n",
       "544  The Comedian   neutral            63        14     17          10     12   \n",
       "557          Toad   neutral            50        34     58          56     49   \n",
       "\n",
       "     Combat  Total  \n",
       "82       85    550  \n",
       "186     100    395  \n",
       "189      90    366  \n",
       "238      70    553  \n",
       "318      70    430  \n",
       "357      30    239  \n",
       "451      40    261  \n",
       "454      95    257  \n",
       "477      56    387  \n",
       "544      80    196  \n",
       "557      28    275  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dataset of neutral characters.\n",
    "neutral=marvel[marvel['Alignment']=='neutral']\n",
    "neutral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "cb7dae5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[550, 395, 366, 553, 430, 239, 261, 257, 387, 196, 275]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of all values in total column of neutral datatset.\n",
    "neutral=list(neutral['Total'])\n",
    "neutral"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "6c2abcdc",
   "metadata": {},
   "outputs": [
    {
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       "  247,\n",
       "  254,\n",
       "  381,\n",
       "  5,\n",
       "  308,\n",
       "  392,\n",
       "  275,\n",
       "  257,\n",
       "  5,\n",
       "  324,\n",
       "  339,\n",
       "  442,\n",
       "  5,\n",
       "  276,\n",
       "  253,\n",
       "  296,\n",
       "  271,\n",
       "  160,\n",
       "  5,\n",
       "  347,\n",
       "  538,\n",
       "  423,\n",
       "  350,\n",
       "  5,\n",
       "  232,\n",
       "  422,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  267,\n",
       "  5,\n",
       "  194,\n",
       "  517,\n",
       "  5,\n",
       "  392,\n",
       "  490,\n",
       "  5,\n",
       "  256,\n",
       "  5,\n",
       "  239,\n",
       "  5,\n",
       "  257,\n",
       "  364,\n",
       "  111,\n",
       "  147,\n",
       "  228,\n",
       "  414,\n",
       "  260,\n",
       "  372,\n",
       "  5,\n",
       "  5,\n",
       "  271,\n",
       "  5,\n",
       "  262,\n",
       "  220,\n",
       "  198,\n",
       "  285,\n",
       "  218,\n",
       "  237,\n",
       "  195,\n",
       "  291,\n",
       "  393,\n",
       "  357,\n",
       "  329,\n",
       "  448,\n",
       "  389,\n",
       "  281,\n",
       "  280,\n",
       "  346,\n",
       "  414,\n",
       "  5,\n",
       "  198,\n",
       "  5,\n",
       "  273,\n",
       "  5,\n",
       "  5,\n",
       "  480,\n",
       "  231,\n",
       "  399,\n",
       "  266,\n",
       "  229,\n",
       "  291,\n",
       "  299,\n",
       "  470,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  347,\n",
       "  420,\n",
       "  280,\n",
       "  5,\n",
       "  255,\n",
       "  5,\n",
       "  210,\n",
       "  5,\n",
       "  568,\n",
       "  377,\n",
       "  334,\n",
       "  434,\n",
       "  170,\n",
       "  325,\n",
       "  367,\n",
       "  335,\n",
       "  125,\n",
       "  487,\n",
       "  544,\n",
       "  579,\n",
       "  323,\n",
       "  243,\n",
       "  189,\n",
       "  376,\n",
       "  546,\n",
       "  482,\n",
       "  225,\n",
       "  5,\n",
       "  5,\n",
       "  400,\n",
       "  295,\n",
       "  309,\n",
       "  5,\n",
       "  428,\n",
       "  5,\n",
       "  5,\n",
       "  224,\n",
       "  350,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  369,\n",
       "  133,\n",
       "  467,\n",
       "  5,\n",
       "  5,\n",
       "  491,\n",
       "  5,\n",
       "  464,\n",
       "  337,\n",
       "  246,\n",
       "  492,\n",
       "  5,\n",
       "  324,\n",
       "  332,\n",
       "  5,\n",
       "  199,\n",
       "  369,\n",
       "  369,\n",
       "  446,\n",
       "  480,\n",
       "  5,\n",
       "  90,\n",
       "  336,\n",
       "  465,\n",
       "  164,\n",
       "  182,\n",
       "  388,\n",
       "  292],\n",
       " [436,\n",
       "  526,\n",
       "  460,\n",
       "  575,\n",
       "  5,\n",
       "  5,\n",
       "  381,\n",
       "  496,\n",
       "  5,\n",
       "  493,\n",
       "  288,\n",
       "  370,\n",
       "  366,\n",
       "  5,\n",
       "  227,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  525,\n",
       "  305,\n",
       "  323,\n",
       "  191,\n",
       "  5,\n",
       "  5,\n",
       "  220,\n",
       "  309,\n",
       "  424,\n",
       "  5,\n",
       "  5,\n",
       "  416,\n",
       "  246,\n",
       "  311,\n",
       "  451,\n",
       "  194,\n",
       "  266,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  5,\n",
       "  540,\n",
       "  506,\n",
       "  5,\n",
       "  238,\n",
       "  289,\n",
       "  461,\n",
       "  429,\n",
       "  36,\n",
       "  5,\n",
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       "  372,\n",
       "  546,\n",
       "  449,\n",
       "  316,\n",
       "  217,\n",
       "  327,\n",
       "  370,\n",
       "  5,\n",
       "  512,\n",
       "  535,\n",
       "  5,\n",
       "  341,\n",
       "  579,\n",
       "  352,\n",
       "  257,\n",
       "  298,\n",
       "  5,\n",
       "  5,\n",
       "  272,\n",
       "  281,\n",
       "  268,\n",
       "  5,\n",
       "  272,\n",
       "  379,\n",
       "  5,\n",
       "  290,\n",
       "  296,\n",
       "  417,\n",
       "  198,\n",
       "  287,\n",
       "  241,\n",
       "  356,\n",
       "  256,\n",
       "  283,\n",
       "  5,\n",
       "  5,\n",
       "  236,\n",
       "  174,\n",
       "  227,\n",
       "  278,\n",
       "  282,\n",
       "  486,\n",
       "  418,\n",
       "  5,\n",
       "  371,\n",
       "  450,\n",
       "  531,\n",
       "  373,\n",
       "  498,\n",
       "  398,\n",
       "  445,\n",
       "  384,\n",
       "  254,\n",
       "  277,\n",
       "  5,\n",
       "  5,\n",
       "  339,\n",
       "  315,\n",
       "  268,\n",
       "  256,\n",
       "  306,\n",
       "  5,\n",
       "  505,\n",
       "  5,\n",
       "  200,\n",
       "  213,\n",
       "  227,\n",
       "  222,\n",
       "  152,\n",
       "  329,\n",
       "  5,\n",
       "  148,\n",
       "  210,\n",
       "  5,\n",
       "  5,\n",
       "  360,\n",
       "  264,\n",
       "  160,\n",
       "  351,\n",
       "  215,\n",
       "  258,\n",
       "  198,\n",
       "  376,\n",
       "  285,\n",
       "  428,\n",
       "  157,\n",
       "  5,\n",
       "  213,\n",
       "  323,\n",
       "  5,\n",
       "  5,\n",
       "  579,\n",
       "  413,\n",
       "  272,\n",
       "  485,\n",
       "  271,\n",
       "  171,\n",
       "  161,\n",
       "  477,\n",
       "  373,\n",
       "  285,\n",
       "  444,\n",
       "  334,\n",
       "  383,\n",
       "  5,\n",
       "  284,\n",
       "  167,\n",
       "  212,\n",
       "  5,\n",
       "  206,\n",
       "  5,\n",
       "  288],\n",
       " [550, 395, 366, 553, 430, 239, 261, 257, 387, 196, 275]]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a data variable.\n",
    "data=list([good,bad,neutral])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ee6cba99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box-plot of good, bad and neutral characters about the Total column.\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.boxplot(data)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83990627",
   "metadata": {},
   "source": [
    "# Who is the most intelligent characters ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "cb028ee7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box-plot to compare the intelligence among the good, bad and neutral characters.\n",
    "sns.boxplot(data=marvel, x=\"Alignment\", y=\"Intelligence\")\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cd224eb",
   "metadata": {},
   "source": [
    "# SUMMARY"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a50af54",
   "metadata": {},
   "source": [
    "* Startdust is the most durable superhero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8430177a",
   "metadata": {},
   "source": [
    "* Ares has the highest combat among superhero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d74e87",
   "metadata": {},
   "source": [
    "* Martain Mahunter is the best superhero."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ad35196",
   "metadata": {},
   "source": [
    "* Mister Mxyzptlk is the most intelligent supervillain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da388c28",
   "metadata": {},
   "source": [
    "* Doomsday is the most durable supervillain."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70550a80",
   "metadata": {},
   "source": [
    "* Superboy-Prime and  General Zod are the best supervillains."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd8a88ec",
   "metadata": {},
   "source": [
    "* Most of the characters have the Intelligency between 1 and 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81191522",
   "metadata": {},
   "source": [
    "* Most of the characters have the Strength between 1 and 20."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "559656a8",
   "metadata": {},
   "source": [
    "* Most of the characters have the speed between 1 and 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83c6ae84",
   "metadata": {},
   "source": [
    "* Most of the characters have the durability between 1 and 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55cc3bfd",
   "metadata": {},
   "source": [
    "* Most of the characters have the power between 0 and 10, and also there are so much characters who don't have any power means they are powerless."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb422e6",
   "metadata": {},
   "source": [
    "* Most of the characters have the combat between 1 and 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afbffe2",
   "metadata": {},
   "source": [
    "* Most of the characters have the total between 1 and 10."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da27c3de",
   "metadata": {},
   "source": [
    "* Among good, bad and neutral characters the maximum Total is of good character."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b9e3f8",
   "metadata": {},
   "source": [
    "* Among good, bad and neutral characters the maximum Intelligence is of bad character."
   ]
  }
 ],
 "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}