Exercise 8_solution-Visualization of data (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise - Visualization of Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. Import the dataset from the AutoMPG dataset file. Assign it to a variable called mpg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
       "      <td>11.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>buick skylark 320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</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>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>398 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin                   car name\n",
       "0    18.0    8  307.0  130  3504  12.0  70       1  chevrolet chevelle malibu\n",
       "1    15.0    8  350.0  165  3693  11.5  70       1          buick skylark 320\n",
       "2    18.0    8  318.0  150  3436  11.0  70       1         plymouth satellite\n",
       "3    16.0    8  304.0  150  3433  12.0  70       1              amc rebel sst\n",
       "4    17.0    8  302.0  140  3449  10.5  70       1                ford torino\n",
       "..    ...  ...    ...  ...   ...   ...  ..     ...                        ...\n",
       "393  27.0    4  140.0   86  2790  15.6  82       1            ford mustang gl\n",
       "394  44.0    4   97.0   52  2130  24.6  82       2                  vw pickup\n",
       "395  32.0    4  135.0   84  2295  11.6  82       1              dodge rampage\n",
       "396  28.0    4  120.0   79  2625  18.6  82       1                ford ranger\n",
       "397  31.0    4  119.0   82  2720  19.4  82       1                 chevy s-10\n",
       "\n",
       "[398 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df=pd.read_csv(\"auto-mpg.csv\")\n",
    "mpg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. Perform basic EDA to understand the structure of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>350.0</td>\n",
       "      <td>165</td>\n",
       "      <td>3693</td>\n",
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       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>318.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3436</td>\n",
       "      <td>11.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth satellite</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.0</td>\n",
       "      <td>8</td>\n",
       "      <td>304.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3433</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>amc rebel sst</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17.0</td>\n",
       "      <td>8</td>\n",
       "      <td>302.0</td>\n",
       "      <td>140</td>\n",
       "      <td>3449</td>\n",
       "      <td>10.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>ford torino</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mpg  cyl   disp   hp    wt   acc  yr  origin                   car name\n",
       "0  18.0    8  307.0  130  3504  12.0  70       1  chevrolet chevelle malibu\n",
       "1  15.0    8  350.0  165  3693  11.5  70       1          buick skylark 320\n",
       "2  18.0    8  318.0  150  3436  11.0  70       1         plymouth satellite\n",
       "3  16.0    8  304.0  150  3433  12.0  70       1              amc rebel sst\n",
       "4  17.0    8  302.0  140  3449  10.5  70       1                ford torino"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>27.0</td>\n",
       "      <td>4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>86</td>\n",
       "      <td>2790</td>\n",
       "      <td>15.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford mustang gl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
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       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
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       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>135.0</td>\n",
       "      <td>84</td>\n",
       "      <td>2295</td>\n",
       "      <td>11.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge rampage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>28.0</td>\n",
       "      <td>4</td>\n",
       "      <td>120.0</td>\n",
       "      <td>79</td>\n",
       "      <td>2625</td>\n",
       "      <td>18.6</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>ford ranger</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp  hp    wt   acc  yr  origin         car name\n",
       "393  27.0    4  140.0  86  2790  15.6  82       1  ford mustang gl\n",
       "394  44.0    4   97.0  52  2130  24.6  82       2        vw pickup\n",
       "395  32.0    4  135.0  84  2295  11.6  82       1    dodge rampage\n",
       "396  28.0    4  120.0  79  2625  18.6  82       1      ford ranger\n",
       "397  31.0    4  119.0  82  2720  19.4  82       1       chevy s-10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 398 entries, 0 to 397\n",
      "Data columns (total 9 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   mpg       398 non-null    float64\n",
      " 1   cyl       398 non-null    int64  \n",
      " 2   disp      398 non-null    float64\n",
      " 3   hp        398 non-null    object \n",
      " 4   wt        398 non-null    int64  \n",
      " 5   acc       398 non-null    float64\n",
      " 6   yr        398 non-null    int64  \n",
      " 7   origin    398 non-null    int64  \n",
      " 8   car name  398 non-null    object \n",
      "dtypes: float64(3), int64(4), object(2)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "mpg_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "      <td>398.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.514573</td>\n",
       "      <td>5.454774</td>\n",
       "      <td>193.425879</td>\n",
       "      <td>2970.424623</td>\n",
       "      <td>15.568090</td>\n",
       "      <td>76.010050</td>\n",
       "      <td>1.572864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.815984</td>\n",
       "      <td>1.701004</td>\n",
       "      <td>104.269838</td>\n",
       "      <td>846.841774</td>\n",
       "      <td>2.757689</td>\n",
       "      <td>3.697627</td>\n",
       "      <td>0.802055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>104.250000</td>\n",
       "      <td>2223.750000</td>\n",
       "      <td>13.825000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>17.175000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>46.600000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>455.000000</td>\n",
       "      <td>5140.000000</td>\n",
       "      <td>24.800000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mpg         cyl        disp           wt         acc  \\\n",
       "count  398.000000  398.000000  398.000000   398.000000  398.000000   \n",
       "mean    23.514573    5.454774  193.425879  2970.424623   15.568090   \n",
       "std      7.815984    1.701004  104.269838   846.841774    2.757689   \n",
       "min      9.000000    3.000000   68.000000  1613.000000    8.000000   \n",
       "25%     17.500000    4.000000  104.250000  2223.750000   13.825000   \n",
       "50%     23.000000    4.000000  148.500000  2803.500000   15.500000   \n",
       "75%     29.000000    8.000000  262.000000  3608.000000   17.175000   \n",
       "max     46.600000    8.000000  455.000000  5140.000000   24.800000   \n",
       "\n",
       "               yr      origin  \n",
       "count  398.000000  398.000000  \n",
       "mean    76.010050    1.572864  \n",
       "std      3.697627    0.802055  \n",
       "min     70.000000    1.000000  \n",
       "25%     73.000000    1.000000  \n",
       "50%     76.000000    1.000000  \n",
       "75%     79.000000    2.000000  \n",
       "max     82.000000    3.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(398, 9)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg         float64\n",
       "cyl           int64\n",
       "disp        float64\n",
       "hp           object\n",
       "wt            int64\n",
       "acc         float64\n",
       "yr            int64\n",
       "origin        int64\n",
       "car name     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 4. Check and handle the missing values, if any."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mpg         0\n",
       "cyl         0\n",
       "disp        0\n",
       "hp          0\n",
       "wt          0\n",
       "acc         0\n",
       "yr          0\n",
       "origin      0\n",
       "car name    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 5. Create a plot to check the relationship between horsepower and acceleration. Note down your insight for the same. Beautify the graph using various customizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1521ffbe100>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"hp\",y=\"acc\",data=mpg_df,color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 6. Generate subplots to display the histograms for acceleration, displacement and weight and kilometer_per_litre. Note down your insight for the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Illegal argument(s) to subplot: (2, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-ad66f4ef9613>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpg_df\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'acc'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36msubplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1028\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1029\u001b[0m     \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgcf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1030\u001b[1;33m     \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1031\u001b[0m     \u001b[0mbbox\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbbox\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1032\u001b[0m     \u001b[0mbyebye\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py\u001b[0m in \u001b[0;36madd_subplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1417\u001b[0m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_axstack\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1418\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1419\u001b[1;33m             \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubplot_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprojection_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1420\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1421\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_axes_internal\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_subplots.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, fig, *args, **kwargs)\u001b[0m\n\u001b[0;32m     69\u001b[0m                 \u001b[1;31m# num - 1 for converting from MATLAB to python indexing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf'Illegal argument(s) to subplot: {args}'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_params\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Illegal argument(s) to subplot: (2, 2)"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplot(2,2)\n",
    "ax[0][0]=plt.hist(mpg_df['acc'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 7. Create a plot to check the relationship between displacement and weight for origin 2, color the datapoints based on no of cylinders. Note down your insight for the same and save the plot as an image file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>hp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "      <th>car name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>46</td>\n",
       "      <td>1835</td>\n",
       "      <td>20.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen 1131 deluxe sedan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>110.0</td>\n",
       "      <td>87</td>\n",
       "      <td>2672</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>90</td>\n",
       "      <td>2430</td>\n",
       "      <td>14.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>audi 100 ls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25.0</td>\n",
       "      <td>4</td>\n",
       "      <td>104.0</td>\n",
       "      <td>95</td>\n",
       "      <td>2375</td>\n",
       "      <td>17.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>saab 99e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>121.0</td>\n",
       "      <td>113</td>\n",
       "      <td>2234</td>\n",
       "      <td>12.5</td>\n",
       "      <td>70</td>\n",
       "      <td>2</td>\n",
       "      <td>bmw 2002</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>354</th>\n",
       "      <td>34.5</td>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>?</td>\n",
       "      <td>2320</td>\n",
       "      <td>15.8</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>renault 18i</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>28.1</td>\n",
       "      <td>4</td>\n",
       "      <td>141.0</td>\n",
       "      <td>80</td>\n",
       "      <td>3230</td>\n",
       "      <td>20.4</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>peugeot 505s turbo diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>30.7</td>\n",
       "      <td>6</td>\n",
       "      <td>145.0</td>\n",
       "      <td>76</td>\n",
       "      <td>3160</td>\n",
       "      <td>19.6</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>volvo diesel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>36.0</td>\n",
       "      <td>4</td>\n",
       "      <td>105.0</td>\n",
       "      <td>74</td>\n",
       "      <td>1980</td>\n",
       "      <td>15.3</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>volkswagen rabbit l</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>44.0</td>\n",
       "      <td>4</td>\n",
       "      <td>97.0</td>\n",
       "      <td>52</td>\n",
       "      <td>2130</td>\n",
       "      <td>24.6</td>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      mpg  cyl   disp   hp    wt   acc  yr  origin  \\\n",
       "19   26.0    4   97.0   46  1835  20.5  70       2   \n",
       "20   25.0    4  110.0   87  2672  17.5  70       2   \n",
       "21   24.0    4  107.0   90  2430  14.5  70       2   \n",
       "22   25.0    4  104.0   95  2375  17.5  70       2   \n",
       "23   26.0    4  121.0  113  2234  12.5  70       2   \n",
       "..    ...  ...    ...  ...   ...   ...  ..     ...   \n",
       "354  34.5    4  100.0    ?  2320  15.8  81       2   \n",
       "359  28.1    4  141.0   80  3230  20.4  81       2   \n",
       "360  30.7    6  145.0   76  3160  19.6  81       2   \n",
       "375  36.0    4  105.0   74  1980  15.3  82       2   \n",
       "394  44.0    4   97.0   52  2130  24.6  82       2   \n",
       "\n",
       "                         car name  \n",
       "19   volkswagen 1131 deluxe sedan  \n",
       "20                    peugeot 504  \n",
       "21                    audi 100 ls  \n",
       "22                       saab 99e  \n",
       "23                       bmw 2002  \n",
       "..                            ...  \n",
       "354                   renault 18i  \n",
       "359     peugeot 505s turbo diesel  \n",
       "360                  volvo diesel  \n",
       "375           volkswagen rabbit l  \n",
       "394                     vw pickup  \n",
       "\n",
       "[70 rows x 9 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = mpg_df[mpg_df.origin==2]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x152204e87f0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x='origin', y='disp', data = mpg_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 8. Display the frequency distribution of the Origin variable. Note down your insight for the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([249.,   0.,   0.,   0.,   0.,  70.,   0.,   0.,   0.,  79.]),\n",
       " array([1. , 1.2, 1.4, 1.6, 1.8, 2. , 2.2, 2.4, 2.6, 2.8, 3. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nq6tb/rP0zsu/bBr7q/u5PwH85Vn6TH2MjVjX1MfXiHVNfXyNUteMxtcm4OXd9E8D/wL87jTH1zl7WibJnfQ+fd+YZBF4H70PJaiqvwHupfdp8xHgf4A/7JZN/NYHI9T2XuDngNuTAJys3l3fLqP39gx6/4CfqqovTrGu3wP+JMlJ4H+BXdUbTRPdZyPUBfAW4EtV9d99q05yf10NvA14tDsvCvAeesE5yzE2Sl2zGF+j1DWL8TVKXTD98bUZ2JfeHy56CbC/qj6X5I/76pro+PL2A5LUoBfyOXdJ0goMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktSg/wed9KbFwFgOzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(mpg_df['origin'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 9. Check the relationship of multiple variables wrt kilometer_per_litre. Note down your insight for the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:283: UserWarning: Data must have variance to compute a kernel density estimate.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1522209cf70>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1138.38x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df,hue=\"mpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152204b18e0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='disp',y='mpg',data=mpg_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1522062f220>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='hp',y='mpg',data=mpg_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15220828910>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kw8hk9Py+Nnzn04xzakfk3LeYaBc7zsHnI0yqr8bmpTMxGFcQ8vsQChKOvxdJG89M9ppwMHUObq++S30NCe4gin8I4YWQv3zMKmbkYzKy2hxcrzwBZPULFHMOisIZrR5XL2zGPb/dj/aegdQK2kz2rsggJtSGEQ75XV99e+EaEpxHonqGEPp6LlMba/DojZcizmxr3Ry70Sdn1YaDluvJ54raMcugPdzRm7btcEcv2rr7bakxZLRqvuupRN0gbeKVkeyrFjRjS+sRrL9+GmIKO5K0lS0ZrlQ1gZyq8SQYIyv+IYSqSG7duAv11RX4+lWT0xKSvGirNbIpr13UgnNqK/H0bZdjMKbkjMTJZpoxM12snDclta2+ugInTvfj+kfsmatsJhz1dTQWz5Bdjer5/vxm1FWFcPy9iO2rb6P53njzdFRXBjAYUxAM+LDx5umpZvJutFUUv4L7iOIfQugVybXrdnreVmukmJc+0YqV86Zg3KhKS1/+bKYZMyU8InRmBbts9gWpG6T6/2LmKpsJR32tJl5lk92qySsfP4B+vs1uer+8fRYiUfErDFXE1DPE0CqScrDVZlPM+Zo1jMwFZqaLvuiZeVCdqXoZCp0rIxPO6oXNWLP9YF6JV1aSz4xMWW+8exon3osYmkz0821204srcK2Ji/gV3EdW/EMUu2LqnSbb6jifL7+ZuWBSfXXK/KXdXhHwpY7bF43bOlfaJ6/IYBzvdEXAzPjGJy4yTbwyW7XnijAyfGJ6vDUVEqo3mejn2+6bXiGUy7U6lJAV/xDFjYbbdmDm4FRXx1a//Gbmgs7IoGFj9Il1ValtFzeOMpyr2nAw7QkiFlMynijMnJLqk9eEmjBGhYO466k9uHbdTqzcug93XjnZdNV++5O7sffYe/jbqT60dfcDQNYGNLlCQvVPTXVVIaxffOZc1ZueljlNDSAi1xyt5XKtDiVkxT9EsSum3mlUOZ++7XL0DcRx6GQv7n3uTNij/stvtjLOZi4ws6Nrt9WEQ2lzVRsOpoVkzmlqyChApz456B2h+qxhq6v2+uoKfO3jkzMqgGbzc+TyJ+hX74rCCAYoVYzOR5SWFa2e5zVr/+Cao7VcrtWhhCj+IYwdMfVu4PMRGkZWQqliVFUE8OB1Uy331c0VE2/1iUE/V+3d6R25FrQ0ZhSg00cHmTklc30O6k1rxdymlNLPNp4WbSSXtuyDWk5aPwdtPQO48dFXM7KiNy2ZASBRBVRV+loZNi2Z4ahCLpdrdaggph7BM6hf/vGjEo/8x3UOymzNSOw2F+ifIMxs4droIHWb3j6eK0ZdvWkVYm/XrpZ3LP8onrzlMmx46RB2H+kynIPBuGKYFR1XElnRZv0LjnZGbOkiJngDWfELniLbqj6XOcdOc4H+CULNptU/UWijg9Rt+qzhXDHq6k3r3ff6C3pq0a6WFYXx/fnN+M6njecg6PcZHiPg9xmet/p/1dkuYZZDAyd77lYS0StE9Cciep2IvpvcPpqIXiCiA8nftU7JIJQf2Vb1ubJKjerxF4r+CWJL6xGsWdSS8URxXt2IrE8ZVlomqjetixtHYa3BMfJ5ask1Bw3VFRnnsWZRCxqqKwzPW+tsV+WXMMvyx7HqnJTIUKli5h4iCgL4PYAvA/gMgFPM/AMi+gaAWmZenm0sqc7pTZwoIHassw+zVv0uY/uO5R/F+FFh05DNzsig7Y5B/fnVhoOp4wQDPgR8hMGYgliy41bA70NDdQUCgTPrqWznoy84Z3RMJ2zqsZiCtp4BxOKKocyqDJHBGA629eKBbQdSnciKqXyqHduu85MS0tlxvTonJ+4oPck/g8kfBjAPwOzk9g0AtgPIqvgF7+FUmn0uJ21FwJfWV7e6wp9REM1IjmxK3ExhGDkc60dWpM79/hf244bLz88ahZOv09kNJ2cg4MPZNWHT/6udyw53DKIy6EN7zwCA4v0mdl8zUuqhcBytx09EfgCtAN4P4N+ZeTkRdTFzjWafTmbOau6RFb/z5LtysrsOvlYOsy9zR28045iP3ngpVjy7N6scRmOuWdSCB7a9hef3teWtMNRzV5OkjnZGMLWxBstmX4C6qhDOrglj3FmV8PmobJWTeo711RVYNvsC1ISD6IvGcXHjKIyuKuzztfuaceoaHEqUpB4/M8cBXEJENQCeIaIpVt9LREsALAGAc8891yEJBaCwlZNTafbZnLRGxxwR8ueUw8jOvuyJRHbr8/va8nZaqnKoUThTG2uyxt+XY4y6eo5qJrDKjuUfBaqKG1NLMdeMlHooHFfCOZm5CwmTzlUAThDReABI/m4zec86Zp7GzNPq6+vdEHPYYsUBqcfJ8r1mDkqjYxplnurlyFUtU/3bqsJQ5VAjfZbNvsAw/l6dPzudzm7hxOdr95ilKiE9FHBsxU9E9QAGmbmLiMIAPgZgFYBfArgBwA+Sv591SgbBGoWsnIwSh4pNs9eam8IhP2IKp5VlrqsKYePN0/F2R1/Kxv/+hqqccuTKblX/1isM1Qk6GFcQ1DhB1XO//4X9WLWgGRUB35BbeRrN9Xl1I4r6fO2+ZnKNJ45fcywpfiL6jMHm9wD8mZkNV+wAxgPYkLTz+wBsZuatRPQHAJuJ6IsA/gbgswXILego5iIvJOvVbhOG1txk1ktgUn01BmJKyqav3Z5NDiMFodr41XPVK6BYTMGbJ7rTSjSsWdSCi8aORCDgw+SxI/G9+R/AYEyBwhiSRcaM5roY7L5mso1Xrr4Vt7Dk3CWiXwOYCUCNS5sNYCeACwH8T2Z+3CkBAXHu5qLYi9wLXxKto27t4paU01RlQm0Ym5fOTCsnoG63YpvPN6rnna6I4bE2L52ZiojROkDzrbHjdcrdcVru8ttFsc5dBcDfMfOJ5GBjATwM4DIALwJwVPEL2Sm2kYUXHJBac5NZ6QKjcgNWTSpmoZlmmB0rFlcyZD7aGcG9z+3HirlNqAkHMaE2jPGjwmWr9IHyd5yWu/xOY9W5O1FV+knaAFzIzKcADJq8R3AJOy7yUjsgtY461WmqZUJtOFVuQL/dCZNKyORYQf+Zr4xW5t1HurD08VZ89T/+hFDAX9ZKHyh/x2m5y+80Vlf8/4+ItgL4j+TfCwG8SERVALockUywzFBoZKG1w6/ZfhCrFzZn2PgbqissOQet+DvMHLcqoSBlyLB6YTNCQUodg8HYtGQGIoMKuvsH0dU3aOgALYWTsdhjah3YC1oaUVcVQsPICtRqIqGclqEYnAg+GEpYtfETEqUWPgSAkCi/sIWdzP7SIDb+7HjBRl8sisI43NGbiiLxEWHcqEr4CCmb/KlIFH3ROGJxRiQaw8meKM6rG4GJdVWp87QyF7kct0Ci1MLtT+5OJS91RQaxZvtBPHjd1FTpCCuZu6X4bOw6ZiymYH9bN5Y+3pr3OF64JiWqx9zGbzlzl4jGIWHTVwC8yszv2iuiOaL4c1PuF3k2Z1xdVShDiag159t7BtJ8GVacevk4bo3GAZCRuWt2rFI4Ge06ZjHjiHPVG5gpfks2fiK6BcArAOYjYebZSUQ32yuiUAylttEXi95PMbWxBivmNqEvGsO7p/sznNfLt+zBstkXZPgyorE46qsrsHZxCzYtmYG1i1tQX12Rto8Vx222+v76zF39OHp53HYy2nXMYsYR56q3sWrjvwvAVGbuAAAiqgPwEoBHnBJMGF5o/RT6EghPLZtpmnmr7Q8bCvhRVeHPyAFYvbAZYU3DFKOa9HOaGuD3nRmnripkGumkz9zN5lsphf/FrmMWM85Q8DuVGief4q1G9RwF0K35uxvAEVskEASkr7D1JRA6eqOGERoMpPrDzlr1O8x/aAdOR2IppQ8kbhB3PbUHsfiZLlgBH+Gxmy7FhNowpjbW4OdLZuCbn/w7vHWiB7c/uTvVaQowbnSuyrql9QhWLWjOWj8/25NDrs5ceqzun6sbmV3jZEMaqBeH6iOZ/9CO1LVtZ/czq87djQA+gDPlFa5GwvTzFgAw8w9tkcYEsfF7E6dqq/dFY/jI6u2p7UZF0B7+wgcxakQQ161/OW1V+dSymVi45g8ZY2//2mws+snLZxyNi6eh4awQjr83kObkNfMdGMl6sncAgzElI6pH62w2mycAeTk/83GW6h3larmFiXVVBR230M+43P1OpcQuH0mxCVwHkz/qXeLZ5OuRliUQhhRORG2ofor27vQSCLuPdGHDS4ewackMHH+vHx29UfzLs6/jG5+4KMMEpD4d6L8wh072pie4Pb4Lm5fOzGigvnzLHqyY24Slj7fmtEfH4pySZ832g6k+t1aareubuedKussnSa+jN4rrH3nF1DF9/wtnks26IoO4/4X9+P78ZsPjFtMfQBqoF47TPhKriv83AP4ZwETNe5iZm22RQig7Cs0WtrIKNIrBvvPKySBC2mreyMa+pfUI1i5uSQtBXLuoBd/+xd60Y2TLBFazb83s0UY3PfVJYfeRLkecn2b7RwbjUBROm8NsYwd8lBGCumpBMxRFgeAdnPaRWFX8TwD4GoC9SIRzCsOcQlYkVp8SzEpIvHs6vRm5UaLXnVdOzija5vch1UVKRZsJrP9y9UXjWe3RRjc99Ulh5dZ9jjg/zfY/2NaD3oGY5a5fg3Elo4T08i17sHnpzJwyC+7hdAKaVeduOzP/ipkPMfPb6o8tEghlSSEp8fnU/TcKT9U3Cm/vGUD9yAo8/aXLsWP5R/HMbbMwOZmEpX1vTdjY0ahmAmu3r13UgosbRxXUhEb9sjrh/DRrgv7AtgMZc5htbGY2lN2lXEzBItrFj/batstHYnXF/x0i+jGAbQBSSydmftoWKQTPYmaaKWRFkuspIZcZKBDw4cL6KmxaMgMxhRHwEeqrQgiFsl/GRk8Qoyr8ONHdj1HhADYtmQEGUGHRAWm2oh4/qhJjR1Za+nLmKoxnNBeT6qvx8yUzcKwzgq7IYMq0BCDN5JNt7FKEWeYqj2F2vsPdEeykj8Sq4r8JwEVINExXTT0MQBT/ECaXaSbfip7ZlE6uYykKozMygONdA1lLLZih/RINDsbxZlsPvqQZ5+FFLbiowVrim9FNb9WCZnz3V6/jzisnW16ZmX2xzeaiIuDD2x19hj2G9SYfs7HdrmFjpTyGF8o7DDeshnP+mZk/4II8hkg4Z2mwO+0+30bq+pIN777Xb6j0tKUWrHCssw/f/dXrWNDSmIps2dJ6BCvnTYHP57N0I1MUxrun+/FOVyRnVE++mM37ynlT8MC2AxmhrVZDULWyu7W6LrY8hkQFFUex4Zw7iaiJmffZLJfgYbSmmamNNamCZdFYZiSJFfJtpK6agVTfwH2fvThnqQUrECEjsuXB66biZE8US5+wVpDM5yMwc0bOgB0hd2ZzMSLkx+4jXbj3uf14/ObpaOseyDD5FNqboBiy3Ujy6Wug30fKOziHVcX/IQA3ENEhJGz8BAnnHPKophk7O0yZKZ1sZiBVMZiVSAj4rcYoJMspM1AR8GHF3KbUSr2zdzDtacJKeKpT9nKzcfuiCUW4+0gX3mrrMSwQ53ZJhFxmGrPIqYBBX4NSn8twwuo35ioAkwDMAfBpAHOTv00hokYi+h0RvUFErxPRl5Pb7yaiY0T0x+TPJ4s5AcE5VHvwHVdMyggBNIvGMSNXmYBskSiqYliz/WBGiYQ1i1rQUF1h6Riqkrp23U4sXPMHrNy6D1/7+GRMbazBiJA/r1WnWo//iVsuw3/+00fwq9tn4dEbL8XGm6dnlEY42tmHY519eKerD+90RRCLZT6hxGIK3umK4O2OXjAz1i/OnIvz6kaktm1pPZIW4ZSPrT7fUhHZyBWppY/E0n9mgJR3KAWWyzLnPTDReADjmfk1IhoJoBXAPwC4BkAPM99rdSyx8ZcORWEc7erDh+/ZnvG/Hcs/inNqR1gaI5fzzmqZgfrqCtxxxSRMHFOFyoAP9ckIESvHMLMlr5jbhJDfZ+g/MFrxmyVwbXjpUMq5q5VZv88dV1yY5tw0coA+dtOlOKsyiMG4klbmIZ++wYV+FvlwrLMPs1b9LmO79tpQo3picQUBiepxlaLKMhcCMx9n5teSr7sBvAHgHKeOJziDz0cIBwN5x+xrsRK/r5YZuOmxV3HtuqJAtc8AACAASURBVJ246bFXcf0jr6CjN5rmG3jwuqmYcs4onDd6BMbXhFMKxMoxssXfn1c3wnDV6fchY2VslsC1oKUxdcxs+yx7ohVtmoSytp6BjNIRNz76KmIKp+Ux6HMb9PkKVhRlPrkUVrCSzxEI+HB2TRjn1lXhbM1npsXusuJ2PtUMRaza+IuCiCYCmArgZQCzANxORNcD2AXgq8zcafCeJQCWAMC5557rhpiCCcWGAFpx3uXaJ5dD0soxzGzJZ9eEMe6sSgBIOZ6DAR96+mO4+sEdGStjs2Op9fnVY2bbR+vctOIAtQu7Hal1VSFsvHl6xpNaKc00Eh6aG8dW/CpEVA1gC4CvMPNpAA8DuADAJQCOA7jP6H3MvI6ZpzHztPr6eqfFFLJQbBah2aowHPKnVmVEhDlNDRn7WH2qsLLyNLMljzurMmNFTaC0QmfalbHZsVTncyjgz7mP1rlp1kQ+H6e1VZxoQj4QU7Di2b24dt1OrHh2LwYMfBhuYvdTzVDEMRs/ABBREMBWAM8ZlW5OPglsZeYp2cYRG395Y7QC23jzdAzElLRtaxa1IBKNQ2FOs/HnKnGsJnjpj7FmUQsaRlZg9IhQWrJQR28UA7E4CInQTuaEEzIYPKP8stmu1Z672mPd99mL8ZPf/zWrjf/hL3wQ/YMKaquCmDi6ytDGf8aHMQJVoQDGVNvbTc3u1bAXY/Ct+B2GC8XG8RdyQALwEwBvaJU+EY1n5uPJP+cjUfhNGOJUBHxYOW9KyhwQ9PsyVtTLnmjFynlTcNNjr6YUkpZcSmtSfTU2L52JwbiCwThj3X8dxEt/7UjLFPX5CDWVAbzZ1m+QuVudUv65Qgz151NXHcIPFjSjJnzGKTl57Eg8fdvl6BuI49DJXvzLs6+jvWcA6xdPyyhFcdHYkXj6S5fjdP8gjpyKoO30APqifYY3v2IoJOM6G16MwdeGIau5J33ReFoXtuGOkzb+WQAWA/gzEf0xue2fAXyeiC5BouTDYQBLHZRBKBI7oi2M6sObtVMckfxyqo/nv7x9FuJKQsEQEe5/Yb9hrH1dVQgH2nsyomgOtPVg2ROtaZmibT0DKaWvjvOlJ1qxacmM1Iowm18jW717fZVRAqUawKjc+nhmfkAg4EPAT2jvHkhFF02oTbSNrBkRxOgq+1bPdiZweTEGX/U7nDjdn1a5df3109JuzMMZxxQ/M/8eiUQvPb9x6piCvWhX2KoJ4vwxVRhR4ceYKusmCKNVoVnDlK7IYOrv+uoKHO/qT8umXbWgGe3d0VSmqj67Vx9FozZV0TpKY4pxhcqYJvKj0CxjK+dutm8kGjdsG7lpyQygymBiPYDbtX+s4PMRqisDuP6RzNwTKQORwJWoHqE8UZVpsZm7RqtCo4Ypqxc2457f7k/tc8cVk1JKH8hU5kBmdq8WNYpG7ygN+Mg4m1R3LoVkGeufkMIh6yviuEnJ5LiHIxHtNh3ZxWDMOFJKykAkEMUvmKIq0xVzmwwzd62unsw6amkbpqjhk2rDlAm1YZw/psrwy/v++mpsWjIjLXTQ7AmirroCj910aVqmaEN1BR5e1JJh49fuA+Rfkro2HDT0Qfzs1svwl7Zew3BH7TECPh/mNDXg+X1taedQGcyvJIXbiVC5TEelkMkNE1Q5J52J4hdMUb88auy5lnxWT9lWharCUBRGT38szWHqN1mZ/+1UX4YD2KxU8j2/fQN3fmxy2hcyGPTjoobqtLr++qieQkpSm4UR/vSWy9Ls9msWtaQSiowikQDg+X1tqWOOsWjf92L8eqlkctoE5cW5zgdHwzntQsI5S4N6cZuVQ7bTXmoUFjinqQFf/tiFhuYg1cavlcPOUsmFhCmahRE+tWxmWhXPCbWJssRBv8/wGJuXzgQz572K9GJoZSllcnJF7sW5NsL1cE6h/FFXtmPPqsDaRS0ZJYvtdOAZ2eif39eGlfOmpFbWAHD7k7tTSh/IzO4tplSyVlEACeeyVqZc45iZF/SJQ2pWrlkbxJjC8Begn7wYWllKmZzsYOXFuc4HUfxCVnw+wuiqRN9aJx14ZkrT5/Olvrzt3QOGTdO1dttCbbtGj+5GTxfZxjEyL6xd1IIfbXsrQ+aAPxG+aSTrwbaeNFNWMU70UodWelEmOyj383K8ZIMwNLC7iJYeK6V57drHCCP7/F1P7cEdV0yyPI5RaYsLG6pxxxUXpsmzZlEL6qtC6OmPYfXC9DLT919zMR7YdiAlQz6lBrxY3tiLMtlBuZ+X2PgFz2DFJhuNxtDeG005ZceMCOF0NJ72HgCm42iPQUTwE1LtFo3s8y/eNRsAiionbFSWuDMyiPkP7cjILq0M+vD59S+nvV8tNWDlWHbtYyflHP2SjXI4L7HxC54nl012cDCO/e29qTDMOU0N+McrLkwLy1RNI1abmKs18r/9qSZjs0t7r6nZxWpkh1qWWItqIz7aGUnlJAAJR7AWbX6AlWNZCa10OxrFSVt7KSnn8xJTj5A3hdQ6t6M+ur7UwoKWxozSC9lMIx29Udz/wn6smNuETUtmYMXcJmx46RAWtDTipzsPY62uU9Tqhc1ZzS7FVIE0q5LZMLLC0HxgV8VJqVwpALLiF/KkkBWjXatMfamFfPMLFEXJaLK+akEzGkaGUF0RwI+2vYUVc5tQVxVC/cgK/Pv//YtpBBFQXGSHWZz52aPCRZeJyEa5R6MI9iArfiEvClkx2rXKVEstqKi17bVMqA0jaNDhCQDijIwM5OVb9qAiGMDyLXvw/L42LH28FQvX/AFf+PHL+MQHxmeMbRRBlG0fM8x6HJh11bKrjr4T9fiF8kMUv5AXhawYrb4nlzlILbWgKi6jhuOrFzajJxJD2+l+vN3Rm9bc3CxuPq4Y13U5f0yVLRFEZueVT6SUXVEk+YyjbQBv1iReKE/E1CPkRSHxy1beY8UcZFRqoTJ4pi5+V2QQ9/x2P9p7BtLq+qv1+E3lSHbA0m/3+yithESFwZOEvi5/dYUfJ3sHMBhTUg3R9eWiCzFz2VUMzeo4Rg3gtX0NhPJGwjmFvHDKxl9oCrxZmYRNS2bg2nU7U+NsXjoT486qNJRjUn11hnJeu6gFv/zjUaz9f4dN5dHKPLWxBl+dcyHG14Txt44+PLDtANp7BrB2UQtAiScItYTEnKYG3H31lILKMujn1alwwne6Irhm7R8My0noI5QE7yLhnIItFLLytPKeQp2OZqt4bV1/tURCNjkm1VfjyVsuQ1v3ADp6o/jRtrdww+Xn45XDXRm1//UyT22syShbvWpBM+59bj+WPtGKFXObsHLrPqxa0Ixndx/DvKnnpJRqoU8ATodlutkAXnAfeWYTXCGXPbtQp6ORzXr1wmas2X4wbRy1Hr+ZHJ2RQVz345excM0fsGb7QSxoaURFwId7FjZjamONoTyqzMtmX2DoNF42+wIc7Yykoo82vHQIt18xCRUBH1bMbcLUxpqiwjL1oan3v7DftrBMNxvAC+7jZM/dRgAbAYwDoABYx8w/IqLRADYBmIhE68VrmLnTKTkEeyl0palmrw7GFQQNsmDVdnlvd/QZ1q03Q7+KD/p9ON0/mFbXf41BrX09A1lW72qT9LrqUIZzd+PN06EwDFfHahOYrsggpjbW4IbLz8d163dmPBXsPtJl6OjOZsYxC01VlNwrcismoobqCqxZ1JJh4881j0J54JiNn4jGAxjPzK8R0UgArQD+AcCNAE4x8w+I6BsAapl5ebaxxMbvHQqxxVtxFNppujAqkZCt1EIw4EM0puBz63amzDL681s5bwrGjao0zNw1K1u9ct4UhAI+3PvcfiybfYHhuOrxtPNnZS4KtcFbnWdFYRzr6sNAjOEjQGGgIkA4p2aE58oSCOaY2fgde25j5uPM/FrydTeANwCcA2AegA3J3TYgcTMQyoRCbPFtPQMppa/uv+yJVrRpKm3amVGqlkg4t64KZ9eEDZX+/hPdmP/QDsxa9Tt85qGXwMxYtaAZdVUhw/MbEfKbZu4+sO0AVi1IL7a2dlELLhpXjQ0vHcLuI12m46qmKu2ThJW5UExCU3Mt5KzOc0dvFJ9f/zI+9sP/wt/f91/42A//C59f/7Jk+A4RXHHuEtFEAFMBvAxgLDMfBxI3ByJqMHnPEgBLAODcc891Q0zBArlCM43MCFYchW5mlBopv7+09eJnr7yNuz5+kamz2My5e7QzgnufS9jbVfPO+FGJm8D35zfjO59OFIQzGnf8qEqMHVmZt6PbZzIeUfbVuNV5lgzfoY3jnhoiqgawBcBXmPm01fcx8zpmnsbM0+rr650TUMiLbAlAisI43NGLvcfew9HOCPYeew+HO3oRDho7brWOQjszSnMlghkptQe2HcA//v0krH7uzYzV+6oFCWdxtszd3Ue6sPTxVnz1P/6EUMAPn4/SHMnjzqrMmLdVC5rx3V+9jgPtPWkyWpkLP8FQzlwNXIzGntPUACJKmy/J8B3aOBrHT0RBAFsBPMfMP0xu2w9gdnK1Px7AdmaenG0csfF7C/2qvjYcRGdkEIqi4K8ne3HXU2ccjqsXNuPCsdU4/t6AKzb+YnIG7r/mEpzqSzRtHxUOIq4w/trem4rJX794GiaPy786pzpfiqIgpjCOv9eftTWkegPVO7on1lWlncO3ntmDBS2NqAkH0RUZxJbWI/je/A+AQBmfjfZvbc7CnKYG3HHFhWmfjVlug5d7ypZDieRSYGbjd9K5S0jY8E8x81c021cD6NA4d0cz89ezjSWK37told/jN0/H4kdeyVCom5bMgMKc01Fox5e3rbsfn3nopQwZnr7tcjSMrASQqOmvLe88oTacKAVRU4G+aCLjtqYygLc7+3DkVCSlfBtHhzFxdFWazyB39E36zUHff1dFrblv9B4jpWu2T0XAh+uTn0E2pa7eDIjI0En8zG2zUlVBva5My73xuZOUIoFrFoDFAP5MRH9MbvtnAD8AsJmIvgjgbwA+66AMgsNo7eVx01o4jOt+/HLOSCA76pv3DxrbpvsHz/gT2nuj+LdkJU51tfxv297Cdz7931LK952uCG589NWcUTO5ZNb7Ezp6oznLV5g5YLXzZZSM5vcBVz945klmQUtjhlNdP86xzj5TW3651Ju3Ml9COo4pfmb+PQCz2+0VTh1XcJdoLI766gqsmNsEAuHRGy/FA9sOpPWpDfjINUeh38TpqbV9xxTG8/va8Py+trT3futTTanXRg7p+uoKKMw41tlneQWs9yes2X4QqxY0p8Xf66N6rDpW9YpZr8QbRlaYjtPePZBa8edbe8lriCM6f6Rkg1AU4ZAfX79qcoZdXy2Wtv76aagMudeYOhzyY/XC5gx5wqEzx1LLO+vlCWiUeFBXuG1qYw2+ftVkfG7dzjSFncucoI+C2n2kCxteOoTNS2ea1uoptJG3/n3VFQHDcRhI+TjmNDVkJGqVU+9YoPwbn5cCKdJW5pTaqWVmU9+0ZEZaD1y3bLBax2jNiCBGVgYxssKPOCOlaEdV+A1t/Bc1VCMYTCgLfdLZozdeapiklcuc4GbjGv37fn3Hh9DVN5iR3VszIohPPfD71PvsKhpXKsTGb47rzl07EcWfjjZK5GRvFEsfz+w5a+WCt+OmYVYdU+usBBL9ctt6BlLllBuqK1JKNh+sjBOLKTjVF0Vb9wAeSBZb05tWzq8dgY5IetP2ior0B2BtY3cA+Mjq7Rny/H75R0FAVnkKmedCPxt9M/m7f7k3I/JnQUtjWp9fIPPzcptir8VSL4C8ilTnHCJoVzf68gL5OLXsWiVZSeg62TOA3mgMh0+eKVdcSG33wcE43mzrybpSVxTGgfaeVBmFFXObMgqo3bpxF356y2X4QtLhbBReGospeKu9N23Fb3SecYXTxtHLAxTmtC7Usap9n6Iw7rxycnq56cUt+NF/vpX2ngm15l3L3MCOa7FcHNFeQUrtlRnaCIZ8e86ajaO+zyh1P1cylJWErtffOY2204nyDN+5ugmXv68uMeZ7kbwar+ubrR/tjOBLJqUfRoT8WeeovXsgbRx9CQl9mYkHth3A6oVnEqbmNDXgp7dchq6+QayY24RrWiZgxdwmDCSfSAppJm9EMU3qjdo7nlNTiZtmnZ9RzTRQwtWxNIB3H1nxlxnaCAa152whTi0rkRBWV2L6LlRqp6quSBQnTp8pYJZIkroYX/zv5+OLG/Jf3embrasyxzTKUD0vdW7M5kivVI52ppeQ0Ef17D7ShXt+ux8/XzIDQR/hZG80tdKf09SA2/9+Em776Wu22pidWAkf6+zDPb/dnxbKes9v9+PB66YCVQWLWhQSleM+suIvM7Sp9GpooNFqO59xVKzGk+sLlV3/yCu46bFXce26nbjpsVdx/SOvoKM3ikg0noquUd9/5+Y/4Z2u/oJWd/pm66rMAYOIGHVutrQeMSygtqX1SOY4mhISRvXo23sG4COCz+dL+VWARLy8qvTzPadsOLESDgX8aO8ZwNLHW3Htup1Y+ngr2nsGShoBI+Uh3EdW/B7CioNKNa3cunFXKjTwyVsug99Hae/JNZZ2nGLiybPtY5bQ1Tg6EfXTFRlMlS2wsrpTm63rbfxqjXhFYTAYT3zxMhw62Ytndx/D56efh4l1I1Lhk8GAD34CvvnJv8Pnp5+X5nPQ1prPVo/+RHd/2nkVY3LLhhMr4UL7HjiJlWtRsBdR/B7B6mO9lTaGVsayMo6V+Ohs+zDY8H9HTkVSjdBXLWjGhpcOWVrdGTVbV6NojM557aIWjK+pRE34zM1Qv4/aZGVEyJ927oGADxeNHYnNS2dm1PXXn3MxJrdsOBWfPhBT0sxv66/PCPpwHTNzoeAMEs7pEQptNl7sWNmeDAqpGTOnqQHf/lQT/D5CMOBDT38sVTtGm9ylzex98pbLMKG2uAYfVs7ZbB+jZijZMDpnfU0c/U2nEJyIT7fzOrMLL8o0VJBwTo9j52O91bFyKRYrTwXafdS8gus04Y3rr5+GX94+C5Fo4ti3P7k7pfRVufzJYxVDMWYp1VRjda6N5qU2HMTTt12OvoE4Dp3sxbd/sTeVuVyooi6ksX0uvOhI9aJMQx15nvIIdjq4rI5lxXmYq0m6dh+901MdL64A59SOSDkW7TjHQs7ZbB/VVBMO+S2HTurnJRDwgUBY9JOXcdNjr2L3kS5bnLFW5j8fvOhI9aJMQx1R/DZQTKy1SrZ4+HyPqTrwHr3xUmxaMgOP3ngpNt48PWMsu1Zaqix90VjW8XKdo5V5jMUUvNMVwdsdvXinK4JYTLE0ttk+auTPxpun48TpgVQ7xvkP7cD+E915fZbZ5rPY6yPbuWspJu+iVHhRpqGO2PiLxE47rNW081zHtGqbP9rZZ6lcstXzN2tUrm8yYnSOVmTO1bTdyvwpCuNk7wAi0TgG44xINIaTPVG8v6Ean1+/s6i5MLNVP3nLZRnmr3yvDzsb1nuxvIEXZRoKuN5sfbhgZ6y11cf6XMe0Gn//vV/vy4xxX9yS10pLeywreQVm52hF5lxN262apQiEL/w40Uj80w/uwE2PvYoTp/uLfvoxWrmuXdyC7/06s6xGvteHnQ3r7TYf2YEXZRrKiHO3SErhmMp2TEVhRGNx3PfZi9Pi5I0cnc/va0N7dxQr5jbh7FGVqAz6URHwoaM3annFpZVl95GuVNPxvxs3EuFQwHTVrW/daCazoiip2vGMRE187bnrM24LnT8rDVJyYeSMVRQlo+5/IdeH1xrWC+WNrPiLpBSOKbNjhkN+7D/RjWvX7cS163Zi5dZ9+NrHJ2NqY42po3P3kS6s2X4Qp/tjuOmxV/Hh1dvzsm/rZdl9pAsrt+5DOBQwXLmp5gitLf3NE9347q9ez5B5TlMDTvZGU/t+bt1OfP2qxP+0563NuC10/ra0HsHaxS1F25kzV66ZGcCFXB9GmcT6cxcnqWAVJ3vuPgJgLoA2Zp6S3HY3gFsBtCd3+2dm/k2usZyy8dthV7RqV43FFLT1DGAwriCoSQYqhFhMwf627oxyzHXVIcPa+CvnTcG4UZWm8ffZbPO14WBWuQuxK1+7LtOWvmJuU6pU8ITaMB67aToqAj5Du/vKeVNSCWD52vizyaztRWuXndkuH1ApbPxu2t3Fxu8MpYjjfwzAgwA26rbfz8z3OnhcS9jplM2VdWjlS5uP3Afae/Cj/0z0jK2rCqFhZAXOHhXOKCUAJB71zx09AqQ7Ja1ZIls0zpsn+nPKnev8tXN932cvNo2l1/7d1RfFmGrj1oEX1Ffhxbtmp2XT5vN5ZouPdyJhyI6s1GyZxCp2ZXXns58dSCMV93Gy5+6LRDTRqfGLxa4GzWqRsmzRIGaOOX3j7nzlVm3H6vHMUvz/0t5jmJmqKrr2bhi+D0BOua2cv1Zms/IGXZHBtL87eqPo7o8Z7quakczmRZU12+fpVv12K/NjlUDAl/N6ybf5u9k8udnAXJqlu08pbPy3E9EeInqEiGrNdiKiJUS0i4h2tbe3m+1WMHY5wqyMY8UxZ8fxzOLU12w/mPXczOKoiWB4LMKZWHG12Xq289fKbBT5s0ZTLVMr8wPbDmDtImt2d686Nr0ml1V53JTba3M0HHA7qudhACsBcPL3fQBuNtqRmdcBWAckbPx2C2JXASwr4+gbd6v75OuUzHU89VF/89KZeKcrgo7eKO59LlEXJ9u5mZkI3j3dn3GsOU0NONU7iKVPnGk6blR/x6yQmxr5s3LeFFzQUI1wMBHVc/fVU7Dkw5kyj6+ptFSywKsNt70ml1V53JTba3M0HHB1xc/MJ5g5zswKgPUAprt5fC12ZQtaGUct8atf5TZU5/8Ym+t4Ph9h3FmVqKoIYOXWfSkFmuvcjOKojeT+1qeasFRn/rnrqT2444pJpuevl7m9ZwDjRlViQk04Ve7ATOaacMhSfLdXsz+9JpdVedyU22tzNBxwNHM3aePfqonqGc/Mx5Ov7wRwGTN/Ltc4hUT1WIkSUCNtzJxlVsexso+2cXfAR6ivCiEUKuyBy8rxrDYlzxVppJ8jhRkfMmiu/uJds8GA6bGsnL+VzyObzPlEhtj1uVrBaxErEtUzfHA9qoeIfgZgNoAxRHQUwHcAzCaiS5Aw9RwGsNSJY1stWXCgvSevksNm0Qa5HGr6xt3FRPVYPd7+tp6sx7MaaaR3KLZ3Dxg+lh9s7zUMsQQSN6H97b2WmqQXU7LBqsO2kHLTxUSaeK0RuFV53JTba3M01HHM1MPMn2fm8cwcZOYJzPwTZl7MzB9g5mZmvlpd/duN1ZIFduxjBSvp9nZi5XiFymT0WL56YTMe2HYg67GsNknPNtd2zaObn70geJEhWbKh2JaB+exjBTujeuw6XqEy6R3BDOAfDWrsa8fJp0m6fh8noqPc/OwFwYsMyZINxdRmz3cfK1hJt7cTK8crRiatI9hHZFhjXztOPk3S9fsYRUcVIrMWNz97QfAiQ7Iss102XDfT7fMhl1PWyvFiMQXtvf2IxYE4M/xECPiB+qrKjJIM2ZxusZiCU5EBRGOMuMKoCCQcwAoDFcn943EFb7b15LTxH+7ozWgCPrGuKs3Gf/hUL46ciqT2aRwdxsTRVXnJ7LaNXxBKhZlzd0gqfsC+qA27og2sRKxYHcfKTSTX8fr7YzjQkelwnVRXhcrKQOrccym/aDSWctzWV1fg61dNxl1P7Unbf1J9NY6fjmAgxvARkjcFwjk1Iyw7blPyvNuNWx/XyLN4GiaPy19hW7k55LoRCYLXGXaKf6jyTlcE16z9Q0ZUTb7lH4519hkWS9u0ZAbOqR0BwFoTbO04axe3GBZ727x0pqHM2nGsnFcxDdXzTf+XBuDCUEAasQwR7HJw2uVw1Y6jNi03ks0Ox62bTllx7gpDGVH8ZYZdDk67HK7acdQCbEay2eG4ddMpK85dYSgjpp4yI18bv5kD2KqN/1hXX1bbvFUb/986+3I6bu2oN2+XU1acu8JQQGz8QwSr0S9Wbg79/TF0RM6UUagLh1JKP59xtOUYKgM+gAixuJJymgLIq1mNGyU0rM61lBEQyhlR/EMEK05HuxzAdo0jjlJBKA3i3B0iuJnh6mamrCAI7iGKv8xwM8PVzUxZQRDcQ0w9ZYadNv5c2DWO3U3ABUGwhtj4hwh2Ze7mc7xix7Fys5IoGkGwH1H8QwS7HK5u4mbGrSAIZxDn7hDB7RLPdiBlkAXBW4jiLzPcLvFsB1IGWRC8hWPagogeIaI2Itqr2TaaiF4gogPJ37VOHX+oYmfjdrew0kxbGm4Lgns4ZuMnog8D6AGwUdNs/R4Ap5j5B0T0DQC1zLw811hi40/HLsetm1iJ2LHrvCQ6SBASuN5snZlfJKKJus3zkGjADgAbAGwHkFPxC+noG6CXA7maaVtptm4FiQ4ShNy4vUwcqzZYT/5ucPn4gkexq7m5NEkXhNx41j5AREuIaBcR7Wpvby+1OILDSB19QXAPtxX/CSIaDwDJ321mOzLzOmaexszT6uvrXRNQKA1SR18Q3MNtxf9LADckX98A4FmXjy94FLuieoZ7dJCiMNq7B3Cssw/t3QNQFO8naAru42RUz8+QcOSOAXACwHcA/ALAZgDnAvgbgM8y86lcY0lUz/BA6ugXhzi2BT1SskEQhjhS9kLQIyUbBGGII45twSqi+AVhiCCObcEqovgFYYgw3B3bgnUcy9wVBMFdfD7C5LEj8cxts4adY1vID1H8gjCEyFUaQxAAMfUIgiAMO0TxC4IgDDNE8QuCIAwzRPELgiAMM0TxC4IgDDPKomQDEbUDeNvgX2MAnHRZnGIpR5mB8pRbZHaPcpR7OMh8HjNnlDcuC8VvBhHtMqpD4WXKUWagPOUWmd2jHOUezjKLqUcQBGGYIYpfEARhmFHuin9dqQUogHKUGShPuUVm9yhHuYetzGVt4xcEQRDyp9xX/IIgCEKeiOIXBEEYZnhe8RPRgFcS3wAABT9JREFUZCL6o+bnNBF9RbfPbCJ6T7PPv5RAzkeIqI2I9mq2jSaiF4joQPJ3rcl7ryKi/UT0FyL6hntSm8q9mojeJKI9RPQMEdWYvPcwEf05Oeeu9cY0kfluIjqmuQY+afLeksy1icybNPIeJqI/mry3VPPcSES/I6I3iOh1Ivpycrtnr+ssMnv9mjaT25nrmpnL5geAH8C7SCQlaLfPBrC1xLJ9GMAHAezVbLsHwDeSr78BYJXJOR0E8D4AIQB/AtBUYrnnAAgkX68ykjv5v8MAxnhkru8G8DUL109J5tpIZt3/7wPwLx6b5/EAPph8PRLAWwCavHxdZ5HZ69e0mdyOXNeeX/HruALAQWY2yuItKcz8IoBTus3zAGxIvt4A4B8M3jodwF+Y+a/MHAXw8+T7XMFIbmZ+npljyT93ApjgljxWMJlrK5RsrrPJTEQE4BoAP3NDFqsw83Fmfi35uhvAGwDOgYevazOZy+CaNptrK+Q91+Wm+D8H8y/HTCL6ExH9HyL6b24KlYWxzHwcSHywABoM9jkHwBHN30dh/QN3g5sB/B+T/zGA54molYiWuCiTGbcnH+UfMTE/eHWu/zuAE8x8wOT/JZ9nIpoIYCqAl1Em17VOZi2evqYN5Lb9ui4bxU9EIQBXA/gPg3+/hoT552IA/wbgF27KViRGffE8EWNLRN8CEAPwU5NdZjHzBwF8AsD/IKIPuyZcJg8DuADAJQCOI2E60ePVuf48sq/2SzrPRFQNYAuArzDzaatvM9jm2lybyez1a9pAbkeu67JR/Eh8EK8x8wn9P5j5NDP3JF//BkCQiMa4LaABJ4hoPAAkf7cZ7HMUQKPm7wkA3nFBtqwQ0Q0A5gL4AicNiXqY+Z3k7zYAzyDxyFkSmPkEM8eZWQGw3kQWz801EQUAfAbAJrN9SjnPRBREQhH9lJmfTm729HVtIrPnr2kjuZ26rstJ8ZuuiohoXNJOCiKajsR5dbgomxm/BHBD8vUNAJ412OdVAJOI6PzkU83nku8rGUR0FYDlAK5m5j6TfaqIaKT6Ggnn2V6jfd1AVURJ5pvI4rm5BvAxAG8y81Gjf5ZynpPfqZ8AeIOZf6j5l2evazOZvX5NZ5Hbmevabe91gR7vEUgo8lGabcsALEu+vh3A60h4s3cCuLwEMv4MiUexQSTuwF8EUAdgG4ADyd+jk/ueDeA3mvd+Egkv/kEA3/KA3H9Bwmb4x+TPGr3cSEQQ/Cn587qbcpvI/DiAPwPYk7zox3tpro1kTm5/TL2ONft6ZZ4/hITJYI/mWvikl6/rLDJ7/Zo2k9uR61pKNgiCIAwzysnUIwiCINiAKH5BEIRhhih+QRCEYYYofkEQhGGGKH5BEIRhhih+QRCEYYYofkEQhGGGKH5ByAER/SJZtOt1tXBXsv75a8nCgNuS26qJ6NFkPfc9RLSgtJILgjGSwCUIOSCi0cx8iojCSKTHXwFgF4APM/Mhzf9XAahg5q8k31fLzJ0lFF0QDAmUWgBBKAPuIKL5ydeNAJYAeJGZDwEAM6t19j+GRJ0UJLeL0hc8iZh6BCELRDQbCYU+kxNlv3cjUcvF6FGZTLYLgqcQxS8I2RkFoJOZ+4joIgAzAFQA+AgRnQ8kTEHJfZ9HomAgktsNe9EKQqkRG78gZIGIKpBo7HMOgP0A6pHogxoG8L+QWDy1MfOVySYa/w6gBUAcwHdZUw9eELyCKH5BEIRhhph6BEEQhhmi+AVBEIYZovgFQRCGGaL4BUEQhhmi+AVBEIYZovgFQRCGGaL4BUEQhhn/H1d7Hvkxq16dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x='acc',y='mpg',data=mpg_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 10. Display the average weight based on no of cylinders present. Note down the insight for the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cyl\n",
       "3      24.105528\n",
       "4    1183.060302\n",
       "5      23.391960\n",
       "6     675.002513\n",
       "8    1064.864322\n",
       "Name: wt, dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_cyl=mpg_df.groupby('cyl')['wt'].sum()\n",
    "arr_cyl_avg=arr_cyl/398\n",
    "arr_cyl_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cyl\n",
       "3    2398.500000\n",
       "4    2308.127451\n",
       "5    3103.333333\n",
       "6    3198.226190\n",
       "8    4114.718447\n",
       "Name: wt, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.groupby('cyl').wt.mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 11. Check for the outliers in the dataset. Note down the insight for the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15220878580>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWAAAAEGCAYAAABbzE8LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKzElEQVR4nO3df6jd913H8dcn906WOUR7o2HcTi56BZWpFcoYbuCYLYQqmyKC80fzx2D7Q9NYBFH/ccN/ZPirjSB0WnejokwnTLqQks6JCGKXuW7pSNGLi2Ls+uMWt5XEys39+Mc50dvknjRpb+77c3MfD7jk3HNvznnnzbnPfu/39pzbeu8BYOftqx4AYK8SYIAiAgxQRIABiggwQJH5G/nkAwcO9KWlpZs0CsCt58CBA3n00Ucf7b0fuvJjNxTgpaWlnD59evsmA9gDWmsHtrreKQiAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGK3NDvhGP7HDt2LKurq9VjXLfz588nSRYXF4sneW2Wl5dz5MiR6jEgiQCXWV1dzRNPns2lN9xWPcp1mbvwlSTJl1/avQ+ZuQsvVI8AL7N7v5puAZfecFsufuc91WNcl/1PnUiSXTPvVi7/G2AUzgEDFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxTZkQAfO3Ysx44d24m7AniZkfszvxN3srq6uhN3A3CVkfvjFARAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxwDWtra7nvvvuytra27bctwADXsLKykjNnzuT48ePbftsCDDDD2tpaTp48md57Tp48ue1HwfPbemsznD9/PhcvXszRo0d34u52hdXV1ez7n149xp6y77+/mtXVr3kc7jGrq6vZv3//q/q7Kysr2djYSJJcunQpx48fz/33379ts73iEXBr7f2ttdOttdPPPffctt0xwOgee+yxrK+vJ0nW19dz6tSpbb39VzwC7r0/lOShJLnzzjtf1SHb4uJikuSBBx54NX/9lnT06NF89l+fqR5jT9l4/Tdk+dsOehzuMa/lO5677rorJ06cyPr6eubn53P33Xdv42TOAQPMdPjw4ezbN8nk3Nxc7r333m29fQEGmGFhYSGHDh1Kay2HDh3KwsLCtt7+jvwQDmC3Onz4cM6dO7ftR7+JAANc08LCQh588MGbcttOQQAUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigyvxN3sry8vBN3A3CVkfuzIwE+cuTITtwNwFVG7o9TEABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigiAADFBFggCICDFBEgAGKCDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoMl89wF42d+GF7H/qRPUY12XuwlqS7Jp5tzJ34YUkB6vHgP8jwEWWl5erR7gh58+vJ0kWF3dzwA7uur1zaxPgIkeOHKkeASjmHDBAEQEGKCLAAEUEGKCIAAMUEWCAIgIMUESAAYoIMEARAQYoIsAARQQYoIgAAxQRYIAiAgxQRIABiggwQBEBBigiwABFBBigSOu9X/8nt/Zckn+7SbMcSPL8Tbrt7TD6fMn4M44+XzL+jKPPl4w/407P93yS9N4PXfmBGwrwzdRaO917v7N6jllGny8Zf8bR50vGn3H0+ZLxZxxpPqcgAIoIMECRkQL8UPUAr2D0+ZLxZxx9vmT8GUefLxl/xmHmG+YcMMBeM9IRMMCeIsAARUoC3Fp7uLX2bGvtyU3X3dZaO9Va+5fpn99UMds15vtga+18a+2J6ds9hfO9ubX26dba2dbaF1trR6fXj7TDWTMOscfW2utba4+31j4/ne9D0+tH2uGsGYfY4aY551prn2utPTJ9f5gdzphvmP1VHQF/NMmV/1PyLyf5VO/9O5J8avp+lY/m6vmS5Hd673dM307s8EybrSf5xd77dyV5W5Kfa619d8ba4awZkzH2+FKSd/Xevy/JHUkOtdbelrF2OGvGZIwdXnY0ydlN74+0w+Tq+ZJB9lcS4N773yV54Yqr35NkZXp5JcmP7uhQm8yYbxi996d77/80vfy1TB5cixlrh7NmHEKfeHH67uumbz1j7XDWjMNord2e5IeT/MGmq4fZ4Yz5hjHSOeCDvfenk8kXb5JvKZ5nKz/fWvvC9BRF6bdVl7XWlpJ8f5J/zKA7vGLGZJA9Tr81fSLJs0lO9d6H2+GMGZNBdpjkd5P8UpKNTdeNtMOt5ksG2d9IAR7d7yf59ky+FXw6yW/VjpO01t6Y5ONJfqH3/tXqebayxYzD7LH3fqn3fkeS25O8tbX2lqpZZpkx4xA7bK39SJJne++frbj/V3KN+YbYXzJWgJ9prb0pSaZ/Pls8z8v03p+ZfjFsJPlIkrdWztNae10mYfvT3vtfTa8eaodbzTjaHqcz/VeSv83kvP9QO7xs84wD7fDtSd7dWjuX5M+TvKu19icZZ4dbzjfQ/oYK8F8nOTy9fDjJJwpnucrlB9TUjyV5ctbn7sAsLckfJjnbe//tTR8aZoezZhxlj621b26tfeP08v4kdyV5KmPtcMsZR9lh7/1Xeu+3996Xkvxkkr/pvf9MBtnhrPlG2V+SzFfcaWvtz5K8M8mB1tp/JPm1JL+R5GOttfcl+fckP1Ex2zXme2dr7Y5MfghyLskHqubL5L/sP5vkzPT8YJL8agbaYWbP+N5B9vimJCuttblMDkQ+1nt/pLX2Dxlnh7Nm/ONBdjjLSI/DrXx4lP15KjJAkZFOQQDsKQIMUESAAYoIMEARAQYoIsAARQQYoEjJEzHgek1fyOdkkr/P5GUtP5/kj5J8KJMXefnpJPdk8tz+xSRvTvLh3vtHWmv7kvxekh9M8qVMDjge7r3/5c7+K2BrAsxusJzJs6nen+QzSX4qyTuSvDuTZ9c9keR7Mwn01yf5XGvtk0l+IMlSku/JJNZnkzy8w7PDTE5BsBt8qfd+ZvriKV/M5MW+e5IzmQQ2ST7Re7/Ye38+yaczeYGVdyT5i977Ru/9y9PrYRgCzG7w0qbLG5ve38j/fxd35XPqe5J2k+eC10SAuVW8Z/o71BYyeSGlz2Ry3vjHW2v7WmsHp9fDMJwD5lbxeJJPJvnWJL/ee//P1trHk/xQJi83+M+Z/EaOr9SNCC/n1dDY9VprH0zyYu/9N7f42Bt77y9Oj4wfT/L26flgKOcImFvdI9MXNf+6TI6MxZdhOAIGKOKHcABFBBigiAADFBFggCICDFDkfwHysjoWmYKn0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(mpg_df['mpg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15220984130>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(mpg_df['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152208271c0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(mpg_df['wt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15221b6cf70>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(mpg_df['disp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15221f050a0>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G4BExOyJ+HBFfiohtEXF9RJwbEd+OiAcj4o0R8amI+NeI+GbV9pfVY18SEV+IiPsj4tbqk9xR5AgR8WcR8aOIuC8ibo6IhyLi6Op3L4uIh/cu6+Ai4paI2FK91pZUbedHxL1V326s2o6PiC9HxNaq39/R3cq7KyI+Wr2vt0XER6r3+1BEfAG4F3hl9Ro8tVr/E1UebIiItRHxsar9hf8Qq/U/XfX91oj4ze79hQeaiiPw1wDvpHl+lruBRcCbgYXAlcAPgfnAWcBxwA8i4jbgbGA2MA84DRgCrh3n2iesiPgtYDnwpsx8PCJOAf4BuBC4heaBXl/LzOeagyEdwp9n5q8i4ljg7ohYB/wz8AeZ+VDVtwCfAJ7MzHkAEXFyl+rtuoh4A/ABmudjCmAz8C3gtcAHMvPyar296/cC7wBeTzMH7wW2HGTzj2fm70TE5cDHgL/o3F9yeKbUCLzyUGZuzcw9wP3AxmzuS7mVZkADrMvMZzPzcWATzTMvvhn498zck5k/r9r1orcCN1Z9Rmb+CvgSzTcV1e2Xu1Rbaf46Iu4DvkfzdBVLgDsz8yF4oW8BzqV5Smeq9ifGu9AJ5M3AzZn5dGYOAzcBvw/8NDO/d5D1977PnwL+4xDbvqm63cKLGTEhTMUA3zni/p4Ry3t48T+S/XeOT1qfvEsvCvbrt8z8NjA7It4CTMvMbV2prCAR0UczmH8vM18H/AC4jxYniqNFn09hB3t/Pn2Y67eyNyOeZ4LNWkzFAG/HRRFxTET8BtBHc6rlLuAd1Vx4T9WuF20E3lX1GSP+zf8XYC2Ovtt1IvBEZj5TzbeeBcwA3hIRr4Z9+vYbwAf3PnAqT6EAdwIXR8RLI+I44I+B/zrE+ncBf1S9z4+nOdVXHAO8te8Dt9H8F/YzmbkD+BrNMzBuA/6J5hzbk12rcIKpzoOzAvhW9e//56pfXQ+cTDPENbrbgaMi4kfAZ2i+Bn9Bcxrlpqpvv1Kt+/fAydWXdvcB/d0oeCLIzHuB62i+dzfTnL476JRSZt5N8+R799GcIrmHAt/PHkq/n4j4FDCcmZ9t8bvjM3O4GmV+n+YXdj8f7xpLUn2bf1Fmvq/btUgjjXg/v5TmCH5J9UFQjAk1n1OAW6uDAKbTHJkb3ocQEf9I85qpo+57K3XB6uo6vscAg6WFNzgCl6RiOQcuSYUywCWpUAa4JBXKAJekQhngklSo/weUiutv1sCvtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.drop([\"wt\",\"disp\",\"yr\"],axis=1).boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15221ffd7f0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"disp\",\"yr\"]].boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15222074fd0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[[\"wt\"]].boxplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 12. Plot the correlations for variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>mpg</th>\n",
       "      <th>cyl</th>\n",
       "      <th>disp</th>\n",
       "      <th>wt</th>\n",
       "      <th>acc</th>\n",
       "      <th>yr</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mpg</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.775396</td>\n",
       "      <td>-0.804203</td>\n",
       "      <td>-0.831741</td>\n",
       "      <td>0.420289</td>\n",
       "      <td>0.579267</td>\n",
       "      <td>0.563450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyl</th>\n",
       "      <td>-0.775396</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.950721</td>\n",
       "      <td>0.896017</td>\n",
       "      <td>-0.505419</td>\n",
       "      <td>-0.348746</td>\n",
       "      <td>-0.562543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disp</th>\n",
       "      <td>-0.804203</td>\n",
       "      <td>0.950721</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.932824</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>-0.370164</td>\n",
       "      <td>-0.609409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wt</th>\n",
       "      <td>-0.831741</td>\n",
       "      <td>0.896017</td>\n",
       "      <td>0.932824</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.417457</td>\n",
       "      <td>-0.306564</td>\n",
       "      <td>-0.581024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acc</th>\n",
       "      <td>0.420289</td>\n",
       "      <td>-0.505419</td>\n",
       "      <td>-0.543684</td>\n",
       "      <td>-0.417457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.288137</td>\n",
       "      <td>0.205873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yr</th>\n",
       "      <td>0.579267</td>\n",
       "      <td>-0.348746</td>\n",
       "      <td>-0.370164</td>\n",
       "      <td>-0.306564</td>\n",
       "      <td>0.288137</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.180662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>origin</th>\n",
       "      <td>0.563450</td>\n",
       "      <td>-0.562543</td>\n",
       "      <td>-0.609409</td>\n",
       "      <td>-0.581024</td>\n",
       "      <td>0.205873</td>\n",
       "      <td>0.180662</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             mpg       cyl      disp        wt       acc        yr    origin\n",
       "mpg     1.000000 -0.775396 -0.804203 -0.831741  0.420289  0.579267  0.563450\n",
       "cyl    -0.775396  1.000000  0.950721  0.896017 -0.505419 -0.348746 -0.562543\n",
       "disp   -0.804203  0.950721  1.000000  0.932824 -0.543684 -0.370164 -0.609409\n",
       "wt     -0.831741  0.896017  0.932824  1.000000 -0.417457 -0.306564 -0.581024\n",
       "acc     0.420289 -0.505419 -0.543684 -0.417457  1.000000  0.288137  0.205873\n",
       "yr      0.579267 -0.348746 -0.370164 -0.306564  0.288137  1.000000  0.180662\n",
       "origin  0.563450 -0.562543 -0.609409 -0.581024  0.205873  0.180662  1.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mpg_df.corr()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.3"
  }
 },
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 "nbformat_minor": 1
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