{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2019-12-31          0.0        0.0   \n",
       "1      AFG      Asia  Afghanistan  2020-01-01          0.0        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-01-02          0.0        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-01-03          0.0        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-01-04          0.0        0.0   \n",
       "5      AFG      Asia  Afghanistan  2020-01-05          0.0        0.0   \n",
       "\n",
       "   total_deaths  new_deaths  total_cases_per_million  new_cases_per_million  \\\n",
       "0           0.0         0.0                      0.0                    0.0   \n",
       "1           0.0         0.0                      0.0                    0.0   \n",
       "2           0.0         0.0                      0.0                    0.0   \n",
       "3           0.0         0.0                      0.0                    0.0   \n",
       "4           0.0         0.0                      0.0                    0.0   \n",
       "5           0.0         0.0                      0.0                    0.0   \n",
       "\n",
       "   ...  aged_70_older  gdp_per_capita  extreme_poverty  cvd_death_rate  \\\n",
       "0  ...          1.337        1803.987              NaN         597.029   \n",
       "1  ...          1.337        1803.987              NaN         597.029   \n",
       "2  ...          1.337        1803.987              NaN         597.029   \n",
       "3  ...          1.337        1803.987              NaN         597.029   \n",
       "4  ...          1.337        1803.987              NaN         597.029   \n",
       "5  ...          1.337        1803.987              NaN         597.029   \n",
       "\n",
       "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0                 9.59             NaN           NaN                  37.746   \n",
       "1                 9.59             NaN           NaN                  37.746   \n",
       "2                 9.59             NaN           NaN                  37.746   \n",
       "3                 9.59             NaN           NaN                  37.746   \n",
       "4                 9.59             NaN           NaN                  37.746   \n",
       "5                 9.59             NaN           NaN                  37.746   \n",
       "\n",
       "  hospital_beds_per_thousand  life_expectancy  \n",
       "0                        0.5            64.83  \n",
       "1                        0.5            64.83  \n",
       "2                        0.5            64.83  \n",
       "3                        0.5            64.83  \n",
       "4                        0.5            64.83  \n",
       "5                        0.5            64.83  \n",
       "\n",
       "[6 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "dipsi=pd.read_csv('owid-covid-data.csv')\n",
    "dipsi.head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso_code',\n",
       " 'continent',\n",
       " 'location',\n",
       " 'date',\n",
       " 'total_cases',\n",
       " 'new_cases',\n",
       " 'total_deaths',\n",
       " 'new_deaths',\n",
       " 'total_cases_per_million',\n",
       " 'new_cases_per_million',\n",
       " 'total_deaths_per_million',\n",
       " 'new_deaths_per_million',\n",
       " 'total_tests',\n",
       " 'new_tests',\n",
       " 'total_tests_per_thousand',\n",
       " 'new_tests_per_thousand',\n",
       " 'new_tests_smoothed',\n",
       " 'new_tests_smoothed_per_thousand',\n",
       " 'tests_units',\n",
       " 'stringency_index',\n",
       " 'population',\n",
       " 'population_density',\n",
       " 'median_age',\n",
       " 'aged_65_older',\n",
       " 'aged_70_older',\n",
       " 'gdp_per_capita',\n",
       " 'extreme_poverty',\n",
       " 'cvd_death_rate',\n",
       " 'diabetes_prevalence',\n",
       " 'female_smokers',\n",
       " 'male_smokers',\n",
       " 'handwashing_facilities',\n",
       " 'hospital_beds_per_thousand',\n",
       " 'life_expectancy']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To see all features together\n",
    "dipsi.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30221, 34)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                            object\n",
       "continent                           object\n",
       "location                            object\n",
       "date                                object\n",
       "total_cases                        float64\n",
       "new_cases                          float64\n",
       "total_deaths                       float64\n",
       "new_deaths                         float64\n",
       "total_cases_per_million            float64\n",
       "new_cases_per_million              float64\n",
       "total_deaths_per_million           float64\n",
       "new_deaths_per_million             float64\n",
       "total_tests                        float64\n",
       "new_tests                          float64\n",
       "total_tests_per_thousand           float64\n",
       "new_tests_per_thousand             float64\n",
       "new_tests_smoothed                 float64\n",
       "new_tests_smoothed_per_thousand    float64\n",
       "tests_units                         object\n",
       "stringency_index                   float64\n",
       "population                         float64\n",
       "population_density                 float64\n",
       "median_age                         float64\n",
       "aged_65_older                      float64\n",
       "aged_70_older                      float64\n",
       "gdp_per_capita                     float64\n",
       "extreme_poverty                    float64\n",
       "cvd_death_rate                     float64\n",
       "diabetes_prevalence                float64\n",
       "female_smokers                     float64\n",
       "male_smokers                       float64\n",
       "handwashing_facilities             float64\n",
       "hospital_beds_per_thousand         float64\n",
       "life_expectancy                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking whether column is categorical colomn or numerical colomn\n",
    "dipsi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                              64\n",
       "continent                            261\n",
       "location                               0\n",
       "date                                   0\n",
       "total_cases                          311\n",
       "new_cases                            311\n",
       "total_deaths                         311\n",
       "new_deaths                           311\n",
       "total_cases_per_million              375\n",
       "new_cases_per_million                375\n",
       "total_deaths_per_million             375\n",
       "new_deaths_per_million               375\n",
       "total_tests                        20978\n",
       "new_tests                          21377\n",
       "total_tests_per_thousand           20978\n",
       "new_tests_per_thousand             21377\n",
       "new_tests_smoothed                 20180\n",
       "new_tests_smoothed_per_thousand    20180\n",
       "tests_units                        19485\n",
       "stringency_index                    5666\n",
       "population                            64\n",
       "population_density                  1341\n",
       "median_age                          2983\n",
       "aged_65_older                       3373\n",
       "aged_70_older                       3123\n",
       "gdp_per_capita                      3303\n",
       "extreme_poverty                    12211\n",
       "cvd_death_rate                      2960\n",
       "diabetes_prevalence                 2057\n",
       "female_smokers                      8490\n",
       "male_smokers                        8746\n",
       "handwashing_facilities             17837\n",
       "hospital_beds_per_thousand          5374\n",
       "life_expectancy                      421\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12619</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12620</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
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       "    <tr>\n",
       "      <th>12621</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
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       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12623</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent location        date  total_cases  new_cases  \\\n",
       "12619      IND      Asia    India  2019-12-31          0.0        0.0   \n",
       "12620      IND      Asia    India  2020-01-01          0.0        0.0   \n",
       "12621      IND      Asia    India  2020-01-02          0.0        0.0   \n",
       "12622      IND      Asia    India  2020-01-03          0.0        0.0   \n",
       "12623      IND      Asia    India  2020-01-04          0.0        0.0   \n",
       "\n",
       "       total_deaths  new_deaths  total_cases_per_million  \\\n",
       "12619           0.0         0.0                      0.0   \n",
       "12620           0.0         0.0                      0.0   \n",
       "12621           0.0         0.0                      0.0   \n",
       "12622           0.0         0.0                      0.0   \n",
       "12623           0.0         0.0                      0.0   \n",
       "\n",
       "       new_cases_per_million  ...  aged_70_older  gdp_per_capita  \\\n",
       "12619                    0.0  ...          3.414        6426.674   \n",
       "12620                    0.0  ...          3.414        6426.674   \n",
       "12621                    0.0  ...          3.414        6426.674   \n",
       "12622                    0.0  ...          3.414        6426.674   \n",
       "12623                    0.0  ...          3.414        6426.674   \n",
       "\n",
       "       extreme_poverty  cvd_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "12619             21.2          282.28                10.39             1.9   \n",
       "12620             21.2          282.28                10.39             1.9   \n",
       "12621             21.2          282.28                10.39             1.9   \n",
       "12622             21.2          282.28                10.39             1.9   \n",
       "12623             21.2          282.28                10.39             1.9   \n",
       "\n",
       "       male_smokers  handwashing_facilities hospital_beds_per_thousand  \\\n",
       "12619          20.6                   59.55                       0.53   \n",
       "12620          20.6                   59.55                       0.53   \n",
       "12621          20.6                   59.55                       0.53   \n",
       "12622          20.6                   59.55                       0.53   \n",
       "12623          20.6                   59.55                       0.53   \n",
       "\n",
       "       life_expectancy  \n",
       "12619            69.66  \n",
       "12620            69.66  \n",
       "12621            69.66  \n",
       "12622            69.66  \n",
       "12623            69.66  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To subset only those rows that have “India” in the “location” column\n",
    "dipsi = dipsi[dipsi.location == 'India']\n",
    "dipsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196, 34)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                            0\n",
       "continent                           0\n",
       "location                            0\n",
       "date                                0\n",
       "total_cases                         0\n",
       "new_cases                           0\n",
       "total_deaths                        0\n",
       "new_deaths                          0\n",
       "total_cases_per_million             0\n",
       "new_cases_per_million               0\n",
       "total_deaths_per_million            0\n",
       "new_deaths_per_million              0\n",
       "total_tests                        85\n",
       "new_tests                          91\n",
       "total_tests_per_thousand           85\n",
       "new_tests_per_thousand             91\n",
       "new_tests_smoothed                 80\n",
       "new_tests_smoothed_per_thousand    80\n",
       "tests_units                        73\n",
       "stringency_index                    5\n",
       "population                          0\n",
       "population_density                  0\n",
       "median_age                          0\n",
       "aged_65_older                       0\n",
       "aged_70_older                       0\n",
       "gdp_per_capita                      0\n",
       "extreme_poverty                     0\n",
       "cvd_death_rate                      0\n",
       "diabetes_prevalence                 0\n",
       "female_smokers                      0\n",
       "male_smokers                        0\n",
       "handwashing_facilities              0\n",
       "hospital_beds_per_thousand          0\n",
       "life_expectancy                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12619</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12620</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12621</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12623</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent location    date  total_cases  new_cases  \\\n",
       "12619      IND      Asia    India  737424          0.0        0.0   \n",
       "12620      IND      Asia    India  737425          0.0        0.0   \n",
       "12621      IND      Asia    India  737426          0.0        0.0   \n",
       "12622      IND      Asia    India  737427          0.0        0.0   \n",
       "12623      IND      Asia    India  737428          0.0        0.0   \n",
       "\n",
       "       total_deaths  new_deaths  total_cases_per_million  \\\n",
       "12619           0.0         0.0                      0.0   \n",
       "12620           0.0         0.0                      0.0   \n",
       "12621           0.0         0.0                      0.0   \n",
       "12622           0.0         0.0                      0.0   \n",
       "12623           0.0         0.0                      0.0   \n",
       "\n",
       "       new_cases_per_million  ...  aged_70_older  gdp_per_capita  \\\n",
       "12619                    0.0  ...          3.414        6426.674   \n",
       "12620                    0.0  ...          3.414        6426.674   \n",
       "12621                    0.0  ...          3.414        6426.674   \n",
       "12622                    0.0  ...          3.414        6426.674   \n",
       "12623                    0.0  ...          3.414        6426.674   \n",
       "\n",
       "       extreme_poverty  cvd_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "12619             21.2          282.28                10.39             1.9   \n",
       "12620             21.2          282.28                10.39             1.9   \n",
       "12621             21.2          282.28                10.39             1.9   \n",
       "12622             21.2          282.28                10.39             1.9   \n",
       "12623             21.2          282.28                10.39             1.9   \n",
       "\n",
       "       male_smokers  handwashing_facilities hospital_beds_per_thousand  \\\n",
       "12619          20.6                   59.55                       0.53   \n",
       "12620          20.6                   59.55                       0.53   \n",
       "12621          20.6                   59.55                       0.53   \n",
       "12622          20.6                   59.55                       0.53   \n",
       "12623          20.6                   59.55                       0.53   \n",
       "\n",
       "       life_expectancy  \n",
       "12619            69.66  \n",
       "12620            69.66  \n",
       "12621            69.66  \n",
       "12622            69.66  \n",
       "12623            69.66  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting date coloumn to ordinal coloumn\n",
    "import datetime as dt\n",
    "dipsi[\"date\"]=pd.to_datetime(dipsi[\"date\"])\n",
    "dipsi[\"date\"]=dipsi[\"date\"].map(dt.datetime.toordinal)\n",
    "dipsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If more than 50% values in a column are null, then drop that entire column\n",
    "dipsi = dipsi.drop(dipsi.columns[dipsi.apply(lambda col : col.isnull().sum()/len(dipsi) > 0.5)],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso_code',\n",
       " 'continent',\n",
       " 'location',\n",
       " 'date',\n",
       " 'total_cases',\n",
       " 'new_cases',\n",
       " 'total_deaths',\n",
       " 'new_deaths',\n",
       " 'total_cases_per_million',\n",
       " 'new_cases_per_million',\n",
       " 'total_deaths_per_million',\n",
       " 'new_deaths_per_million',\n",
       " 'total_tests',\n",
       " 'new_tests',\n",
       " 'total_tests_per_thousand',\n",
       " 'new_tests_per_thousand',\n",
       " 'new_tests_smoothed',\n",
       " 'new_tests_smoothed_per_thousand',\n",
       " 'tests_units',\n",
       " 'stringency_index',\n",
       " 'population',\n",
       " 'population_density',\n",
       " 'median_age',\n",
       " 'aged_65_older',\n",
       " 'aged_70_older',\n",
       " 'gdp_per_capita',\n",
       " 'extreme_poverty',\n",
       " 'cvd_death_rate',\n",
       " 'diabetes_prevalence',\n",
       " 'female_smokers',\n",
       " 'male_smokers',\n",
       " 'handwashing_facilities',\n",
       " 'hospital_beds_per_thousand',\n",
       " 'life_expectancy']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To see all remaining features together after dropping columns with more than 50% null values\n",
    "dipsi.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196, 34)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop for replacing the Nan values in numerical coloumns by mean and in categorical coloumns by mode of that column\n",
    "for i in dipsi.columns :\n",
    "    if dipsi[i].dtype != 'O' :\n",
    "        dipsi[i] = dipsi[i].replace(np.NaN,dipsi[i].mean())\n",
    "dipsi = dipsi.fillna(dipsi.mode().iloc[0])        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                           0\n",
       "continent                          0\n",
       "location                           0\n",
       "date                               0\n",
       "total_cases                        0\n",
       "new_cases                          0\n",
       "total_deaths                       0\n",
       "new_deaths                         0\n",
       "total_cases_per_million            0\n",
       "new_cases_per_million              0\n",
       "total_deaths_per_million           0\n",
       "new_deaths_per_million             0\n",
       "total_tests                        0\n",
       "new_tests                          0\n",
       "total_tests_per_thousand           0\n",
       "new_tests_per_thousand             0\n",
       "new_tests_smoothed                 0\n",
       "new_tests_smoothed_per_thousand    0\n",
       "tests_units                        0\n",
       "stringency_index                   0\n",
       "population                         0\n",
       "population_density                 0\n",
       "median_age                         0\n",
       "aged_65_older                      0\n",
       "aged_70_older                      0\n",
       "gdp_per_capita                     0\n",
       "extreme_poverty                    0\n",
       "cvd_death_rate                     0\n",
       "diabetes_prevalence                0\n",
       "female_smokers                     0\n",
       "male_smokers                       0\n",
       "handwashing_facilities             0\n",
       "hospital_beds_per_thousand         0\n",
       "life_expectancy                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values present  \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univarite Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dropping categorical columns in data frame bd\n",
    "mody = dipsi.drop(['iso_code','continent','location','tests_units'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                                 int64\n",
       "total_cases                        float64\n",
       "new_cases                          float64\n",
       "total_deaths                       float64\n",
       "new_deaths                         float64\n",
       "total_cases_per_million            float64\n",
       "new_cases_per_million              float64\n",
       "total_deaths_per_million           float64\n",
       "new_deaths_per_million             float64\n",
       "total_tests                        float64\n",
       "new_tests                          float64\n",
       "total_tests_per_thousand           float64\n",
       "new_tests_per_thousand             float64\n",
       "new_tests_smoothed                 float64\n",
       "new_tests_smoothed_per_thousand    float64\n",
       "stringency_index                   float64\n",
       "population                         float64\n",
       "population_density                 float64\n",
       "median_age                         float64\n",
       "aged_65_older                      float64\n",
       "aged_70_older                      float64\n",
       "gdp_per_capita                     float64\n",
       "extreme_poverty                    float64\n",
       "cvd_death_rate                     float64\n",
       "diabetes_prevalence                float64\n",
       "female_smokers                     float64\n",
       "male_smokers                       float64\n",
       "handwashing_facilities             float64\n",
       "hospital_beds_per_thousand         float64\n",
       "life_expectancy                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking whether column is categorical colomn or numerical colomn\n",
    "mody.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
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    {
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     "output_type": "display_data"
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   ],
   "source": [
    "#Loop for plotting histograms\n",
    "for i in mody.columns :\n",
    "    plt.hist(mody[i],bins=10,color='r')\n",
    "    plt.title(\"histogram\")\n",
    "    plt.xlabel(i)\n",
    "    plt.ylabel(\"Frequency\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                               7.375221e+05\n",
       "total_cases                        1.236401e+05\n",
       "new_cases                          4.626286e+03\n",
       "total_deaths                       3.617459e+03\n",
       "new_deaths                         1.210561e+02\n",
       "total_cases_per_million            8.959391e+01\n",
       "new_cases_per_million              3.352378e+00\n",
       "total_deaths_per_million           2.621347e+00\n",
       "new_deaths_per_million             8.770918e-02\n",
       "total_tests                        3.523086e+06\n",
       "new_tests                          1.111559e+05\n",
       "total_tests_per_thousand           2.552982e+00\n",
       "new_tests_per_thousand             8.054286e-02\n",
       "new_tests_smoothed                 9.492413e+04\n",
       "new_tests_smoothed_per_thousand    6.878448e-02\n",
       "stringency_index                   5.530901e+01\n",
       "population                         1.380004e+09\n",
       "population_density                 4.504190e+02\n",
       "median_age                         2.820000e+01\n",
       "aged_65_older                      5.989000e+00\n",
       "aged_70_older                      3.414000e+00\n",
       "gdp_per_capita                     6.426674e+03\n",
       "extreme_poverty                    2.120000e+01\n",
       "cvd_death_rate                     2.822800e+02\n",
       "diabetes_prevalence                1.039000e+01\n",
       "female_smokers                     1.900000e+00\n",
       "male_smokers                       2.060000e+01\n",
       "handwashing_facilities             5.955000e+01\n",
       "hospital_beds_per_thousand         5.300000e-01\n",
       "life_expectancy                    6.966000e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding mean of each column\n",
    "dipsi.mean(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                               7.375225e+05\n",
       "total_cases                        4.807500e+03\n",
       "new_cases                          6.395000e+02\n",
       "total_deaths                       1.315000e+02\n",
       "new_deaths                         2.000000e+01\n",
       "total_cases_per_million            3.484000e+00\n",
       "new_cases_per_million              4.635000e-01\n",
       "total_deaths_per_million           9.550000e-02\n",
       "new_deaths_per_million             1.450000e-02\n",
       "total_tests                        3.523086e+06\n",
       "new_tests                          1.111559e+05\n",
       "total_tests_per_thousand           2.552982e+00\n",
       "new_tests_per_thousand             8.054286e-02\n",
       "new_tests_smoothed                 9.492413e+04\n",
       "new_tests_smoothed_per_thousand    6.878448e-02\n",
       "stringency_index                   7.546000e+01\n",
       "population                         1.380004e+09\n",
       "population_density                 4.504190e+02\n",
       "median_age                         2.820000e+01\n",
       "aged_65_older                      5.989000e+00\n",
       "aged_70_older                      3.414000e+00\n",
       "gdp_per_capita                     6.426674e+03\n",
       "extreme_poverty                    2.120000e+01\n",
       "cvd_death_rate                     2.822800e+02\n",
       "diabetes_prevalence                1.039000e+01\n",
       "female_smokers                     1.900000e+00\n",
       "male_smokers                       2.060000e+01\n",
       "handwashing_facilities             5.955000e+01\n",
       "hospital_beds_per_thousand         5.300000e-01\n",
       "life_expectancy                    6.966000e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding median of each column\n",
    "dipsi.median(numeric_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
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       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>737618</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>737619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>737620</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    iso_code continent location    date  total_cases  new_cases  total_deaths  \\\n",
       "0        IND      Asia    India  737424          0.0        0.0           0.0   \n",
       "1        NaN       NaN      NaN  737425          NaN        NaN           NaN   \n",
       "2        NaN       NaN      NaN  737426          NaN        NaN           NaN   \n",
       "3        NaN       NaN      NaN  737427          NaN        NaN           NaN   \n",
       "4        NaN       NaN      NaN  737428          NaN        NaN           NaN   \n",
       "..       ...       ...      ...     ...          ...        ...           ...   \n",
       "191      NaN       NaN      NaN  737616          NaN        NaN           NaN   \n",
       "192      NaN       NaN      NaN  737617          NaN        NaN           NaN   \n",
       "193      NaN       NaN      NaN  737618          NaN        NaN           NaN   \n",
       "194      NaN       NaN      NaN  737619          NaN        NaN           NaN   \n",
       "195      NaN       NaN      NaN  737620          NaN        NaN           NaN   \n",
       "\n",
       "     new_deaths  total_cases_per_million  new_cases_per_million  ...  \\\n",
       "0           0.0                      0.0                    0.0  ...   \n",
       "1           NaN                      NaN                    NaN  ...   \n",
       "2           NaN                      NaN                    NaN  ...   \n",
       "3           NaN                      NaN                    NaN  ...   \n",
       "4           NaN                      NaN                    NaN  ...   \n",
       "..          ...                      ...                    ...  ...   \n",
       "191         NaN                      NaN                    NaN  ...   \n",
       "192         NaN                      NaN                    NaN  ...   \n",
       "193         NaN                      NaN                    NaN  ...   \n",
       "194         NaN                      NaN                    NaN  ...   \n",
       "195         NaN                      NaN                    NaN  ...   \n",
       "\n",
       "     aged_70_older  gdp_per_capita  extreme_poverty  cvd_death_rate  \\\n",
       "0            3.414        6426.674             21.2          282.28   \n",
       "1              NaN             NaN              NaN             NaN   \n",
       "2              NaN             NaN              NaN             NaN   \n",
       "3              NaN             NaN              NaN             NaN   \n",
       "4              NaN             NaN              NaN             NaN   \n",
       "..             ...             ...              ...             ...   \n",
       "191            NaN             NaN              NaN             NaN   \n",
       "192            NaN             NaN              NaN             NaN   \n",
       "193            NaN             NaN              NaN             NaN   \n",
       "194            NaN             NaN              NaN             NaN   \n",
       "195            NaN             NaN              NaN             NaN   \n",
       "\n",
       "     diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                  10.39             1.9          20.6   \n",
       "1                    NaN             NaN           NaN   \n",
       "2                    NaN             NaN           NaN   \n",
       "3                    NaN             NaN           NaN   \n",
       "4                    NaN             NaN           NaN   \n",
       "..                   ...             ...           ...   \n",
       "191                  NaN             NaN           NaN   \n",
       "192                  NaN             NaN           NaN   \n",
       "193                  NaN             NaN           NaN   \n",
       "194                  NaN             NaN           NaN   \n",
       "195                  NaN             NaN           NaN   \n",
       "\n",
       "     handwashing_facilities hospital_beds_per_thousand  life_expectancy  \n",
       "0                     59.55                       0.53            69.66  \n",
       "1                       NaN                        NaN              NaN  \n",
       "2                       NaN                        NaN              NaN  \n",
       "3                       NaN                        NaN              NaN  \n",
       "4                       NaN                        NaN              NaN  \n",
       "..                      ...                        ...              ...  \n",
       "191                     NaN                        NaN              NaN  \n",
       "192                     NaN                        NaN              NaN  \n",
       "193                     NaN                        NaN              NaN  \n",
       "194                     NaN                        NaN              NaN  \n",
       "195                     NaN                        NaN              NaN  \n",
       "\n",
       "[196 rows x 34 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding mode of each column\n",
    "dipsi.mode(numeric_only=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bi-Variate Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD5CAYAAAAp8/5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAI80lEQVR4nO3cb4ilZ3nH8d+VPzVuE1xk86IvTKYNrTXRxuAEIa0FJahQa7UmpDQqQuui0hSENBaDRW0tSgSlSCybUoS6VCq2kBZsSmlaxX/JrM0fieSFpmtFDUlEFLeKMVdfnGcxWXc3M8mus3Pl84HDWe7znOe5z3nxPffcZ3aquwPAXKdt9wQAOLmEHmA4oQcYTugBhhN6gOHO2O4JHM2ePXt6bW1tu6cBsKMcOHDgwe4+98jxUzL0a2tr2djY2O5pAOwoVXXwaOO2bgCGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhO6AGGE3qA4YQeYDihBxhuy6GvqndW1bXHefxVVXXhk5sWnGL270/W1pLTTlvd79+/3TOCTTsZK/pXJRF65ti/P9m7Nzl4MOle3e/dK/bsGJsKfVVdX1X3VtW/J3n2MvbGqrq9qu6sqk9U1a6quizJK5PcUFV3VNUFy+1fq+pAVX26qn71JL4eOPGuvz45dOixY4cOrcZhB3jc0FfVC5L8XpJLkvxukkuXh/6xuy/t7ouTfDnJH3T3Z5PcnORPuvv53f2VJPuSXNPdL0hybZIbj3GdvVW1UVUbDzzwwJN+YXDCfO1rWxuHU8wZmzjmRUn+qbsPJUlV3byMP7eq/iLJ7iRnJ7nlyCdW1dlJLkvy8ao6PPy0o12ku/dl9aGQ9fX13sJrgJPrvPNW2zVHG4cdYLN79EcL70eS/FF3Py/Ju5KcdYzzf2dZ3R++PeeJTRW2yXvek+za9dixXbtW47ADbCb0n0ry6qp6elWdk+S3l/Fzknyzqs5McvWjjv/e8li6+7tJ7quqK5OkVi4+YbOHn4Wrr0727UvOPz+pWt3v27cahx2guh9/l6Sqrk/y+iQHk3w9yT1Jvp/kumXs7iTndPcbqurXk9yU5IdJrkjySJIPJ/mFJGcm+Vh3v/t411tfX++NjY0n+poAnpKq6kB3r//U+GZC/7Mm9ABbd6zQ+5+xAMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wnNADDCf0AMMJPcBwQg8wXHX3ds/hp1TVA0kObvc84Cj2JHlwuycBx3B+d5975OApGXo4VVXVRnevb/c8YCts3QAMJ/QAwwk9bM2+7Z4AbJU9eoDhrOgBhhN6gOGEHmA4oWdHq6rdVfWWxzlmrap+fxPnWquqL5242cGpQejZ6XYnOW7ok6wledzQw1RCz0733iQXVNUdVXXDcvtSVd1dVVc96pgXLce8dVm5f7qqvrjcLtvMharq9Kp6/3Luu6rqmmX8z6rq9uW6+6qqlvE/rqp7lmM/toz9fFX97XL8f1fV7yzjF1XVbcsc76qqXz7h7xRPWX69kh2tqtaS/Et3P7eqXpPkTUlentXfpLk9yQuTPDvJtd39iuU5u5I80t0/WIL69929/uhzHeNab05yeZKruvvhqnpmd3/78P1yzN8l+Yfu/ueq+kaSX+zuH1bV7u7+TlX9ZZJ7uvujVbU7yW1JLsnqw+jz3b2/qn4uyend/X8n4S3jKciKnkl+I6to/7i770/yX0kuPcpxZya5qaruTvLxJBdu8vyXJ/nr7n44SQ7HPcmLq+oLy/lekuSiZfyuJPur6rVJHl7GXprkT6vqjiT/meSsJOcl+VySt1fV27L6w1QizwlzxnZPAE6g2uRxb01yf5KLs1rs/GAL53/Mj8BVdVaSG5Osd/f/VtU7s4p3kvxWkt9M8sok76iqi5ZzvKa77z3i3F+uqi8sz7mlqv6wu/9jk/OC47KiZ6f7XpJzln9/KslVy176uVlF9rYjjkmSZyT5Znc/kuR1SU7f5LX+LcmbquqMJKmqZ+YnUX+wqs5OcsXy2GlJntXdtya5Lqsvjc9OckuSax61j3/Jcv9LSb7a3X+V5OYkv7aldwGOw4qeHa27H6qqzyy/FvnJrLZL7sxq5X1dd3+rqh5K8nBV3ZnkI1mtwD9RVVcmuTXJ9zd5ub9J8itJ7qqqHyW5qbs/VFU3Jbk7yf9k9b1Asvrw+GhVPSOrVfwHlj36P0/yweUctTznFUmuSvLa5bzfSvLuJ/ymwBF8GQswnK0bgOFs3cARquplSd53xPB93f3q7ZgPPFm2bgCGs3UDMJzQAwwn9ADDCT3AcP8PAWihsmJ33skAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4556 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXoAAAD5CAYAAAAp8/5SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJDElEQVR4nO3cbajedR3H8c/XtsqlKWaBEXooRLuxDI9CdyAl9SjTtBuYlRCuJwYF1pMFLe3Gbh4VFK2QChZBWGJaWll0Zzee4dJlWZQsiqD5IKxWkfrrwfWXTts5ek5unp3vXi/4c/33u/7X//qeDd7n4rez1RgjAPR11FoPAMChJfQAzQk9QHNCD9Cc0AM0t2GtB1jKiSeeOObm5tZ6DIB1ZefOnfeOMZ66//phGfq5ubksLCys9RgA60pV7Vlq3dYNQHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAzQk9QHNCD9Cc0AM0J/QAza069FW1raqueJjnL6iq5zy6seAws2NHMjeXHHXU7HHHjrWeCFbsUHyivyCJ0NPHjh3Jli3Jnj3JGLPHLVvEnnVjRaGvqq1VdXdVfTvJadPaZVV1W1X9vKqurapNVfXiJOcn+WhV7aqqZ03HTVW1s6p+UFWnH8KvBw6+rVuTffv+d23fvtk6rAOPGPqqOivJG5O8MMlrk5w9PfWVMcbZY4wXJPllkreOMW5Ncn2Sd40xzhxj/DbJ9iRvH2OcleSKJJ9c5n22VNVCVS3s3bv3UX9hcND8/verW4fDzIYVXPOyJF8dY+xLkqq6flp/XlW9P8nxSY5JcvP+L6yqY5K8OMmXq+qh5Scs9SZjjO2ZfVPI/Pz8WMXXAIfWySfPtmuWWod1YKV79EuF93NJLh9jnJHkfUmeuMz9/zJ9un/oePb/NyqskQ98INm06X/XNm2arcM6sJLQfz/JhVV1dFUdm+TV0/qxSf5UVRuTbF50/V+n5zLGuC/JPVX1uiSpmRcctOnhsbB5c7J9e3LKKUnV7HH79tk6rAM1xiPvklTV1iRvTrInyR+S3JXk70nePa3dmeTYMcalVfWSJJ9J8q8kFyd5MMmnkpyUZGOSL40xrny495ufnx8LCwv/79cEcESqqp1jjPkD1lcS+sea0AOs3nKh9y9jAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6gOaEHaE7oAZoTeoDmhB6guRpjrPUMB6iqvUn2rPUcsIQTk9y71kPAMk4ZYzx1/8XDMvRwuKqqhTHG/FrPAath6wagOaEHaE7oYXW2r/UAsFr26AGa84keoDmhB2hO6AGaE3paq6pzq+qGtZ4jSapqW1VdsdZzcOQRelgHqupxaz0D65fQc9irqrmq+lVVfbaqdlfVjqo6r6p+VFW/qapzpuPWqrp9ejxtifs8qaquqarbpute8zDv+dyq+llV7aqqO6rq1JXMMb32hKq6bnrdT6rq+Uvc/7Kq+kZVHV1Vlyx6r08/FPWq+ltVXVlVP03yoqq6uqrumu77sYP4W0x3YwyH47A+kswluT/JGZl9ONmZ5JokleQ1Sa5L8uQkG6brz0ty7XR+bpIbpvMPJrlkOj8+ya+TPGmZ9/xEks3T+eOTHL2SORa99r3T+cuT7JrOtyW5IsnlSa5P8oQkz07ytSQbp2s+meTN0/lI8vrp/IQkd+e/PxJ9/Fr/uTjWz7HhUXyPgMfSPWOMO5Okqn6R5JYxxqiqOzML8HFJPl9Vp2YWyI1L3OOVSc5ftE/+xCQnJ/nlEtf+OMnWqnpGkq+MMX5TVSuZI0lemuSiJBljfKeqnlJVx03PvSnJH5JcMMb4d1W9IslZSW6b7n90kj9P1z6Q5Nrp/L4k/0zy2aq6Mclh8fcOrA+2blgv/rXo/MFFv34wyYYkVyX57hjjeUlenVnE91dJLhpjnDkdJ48xlop8xhhfTHJ+kn8kubmqXr7COR56nwNuOT3uzuwbwjMWXfv5RTOdNsbYNj33zzHGA9M89yc5J7PwX5DkpqXmhqUIPV0cl+SP0/mly1xzc5K31/TRuapeuNzNquqZSX43xvh4ZtssB+yzP4zvJ9k83efcJPeOMe6bnrs9yduSXF9VT09yS5KLq+pp0/UnVNUpS8xzTJLjxhhfT/KOJGeuYh6OcEJPFx9J8qGq+lGS5X5C5arMtnTuqKrd06+X84Yku6tqV5LTk3xhFbNsSzJfVXckuTrJWxY/Ocb4YWZ79Tdmtk3zniTfnK7/VpKTlrjnsUlumK75XpJ3rmIejnD+rxuA5nyiB2jOT91wRKuqVyX58H7L94wxLlyLeeBQsHUD0JytG4DmhB6gOaEHaE7oAZr7D/qQZEFagQ7zAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "image/png": "[encoded data, 4420 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
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     "output_type": "display_data"
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    {
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      "image/png": "[encoded data, 4368 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4380 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
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     "output_type": "display_data"
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    {
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      "image/png": "[encoded data, 4396 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
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     "output_type": "display_data"
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     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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       "<Figure size 432x288 with 1 Axes>"
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    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAD5CAYAAAATD4NkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKkklEQVR4nO3df+yudV3H8dcbkBBoEB1qbgrffliWhhLH0iKXhvTLdAyMTWhtbZ1pG2xujEpaWYxWs/XDflgH52p5CmWtZq5EMwl1IZyDcECqtQW4lRqQbdYxE867P+6L+QXP+Z5zOPD+ntN5PLbv7vt87uu+rvfu7Xyfu677OlDdHQB4uh232QMAcGwQHABGCA4AIwQHgBGCA8CIEzZ7gCPVli1bem1tbbPHADiq7Nq166HuPnNfrwnOfqytrWXnzp2bPQbAUaWqHtjfay6pATBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4MDRZMeOZG0tOe641eOOHZs9ERy0DYNTVadX1U8dYJu1qnrdgQ60bHfPoQ4ILHbsSLZtSx54IOlePW7bJjocNQ50hnN6kg2Dk2QtyQGDAxyma65J9ux5/NqePat1OAocKDi/kuQbqurOqnrL8nNPVd1dVZeu2+Z7lm3euJzJfLiq7lh+vutgBqmq46vq15Z9766qK5b1n6+q25fjbq+qWtavrKp7l21vWNZOqap3LNt/vKpes6w/v6puW2bcXVXP3c8M26pqZ1XtfPDBBw9mbJjzyU8e2jocYaq79/9i1VqS93b3C6rq4iSvT/IDSbYkuT3Jdyb55iRXdferlvecnGRvd//P8ov9T7t76/p97edYb0hyQZJLu/uRqjqju//jscdlmz9O8u7u/suq+rckX9fdX6iq07v7P6vql5Pc293vrKrTk9yW5Nysonhrd++oqhOTHN/dn9/og9m6dWvv3LnzwJ8gTFlbW11Ge6Kzz07uv396GtinqtrV3Vv39dqh3DRwflbxeLS7P5Pk75K8eB/bPSPJ9VV1d5Ibk3zrQe7/giS/392PJMljkUny8qr62LK/VyR5/rK+O8mOqro8ySPL2oVJfqaq7kxyc5KTkpyV5O+TvKmqfjrJ2QeKDRyRrrsuOfnkx6+dfPJqHY4ChxKcOsjt3pjkM0lemGRrkhMPYf+PO92qqpOS/F6SS7r725Jcn1VEkuSHk/xukvOS7KqqE5Z9XNzdL1p+zuruf+juP0ny6iSfT3JTVb3iIGeCI8dllyXbt6/OaKpWj9u3r9bhKHCg4HwuyVcuz29JcunyXcuZSV6W1SWr9dskyWlJPtXde5P8WJLjD3KW9yd5/RKOVNUZ+VJcHqqqU5Ncsrx2XJLndPeHklyd1c0Npya5KckV677nOXd5/Pok/9Ldb03yniTnHORMcGS57LLV5bO9e1ePYsNR5ISNXuzuh6vqo8vtzH+d1WWsu7I6E7m6uz9dVQ8neaSq7kryh1mdkfxZVb02yYeS/PdBzvL2JN+UZHdVfTHJ9d39O1V1fZK7k9yf1fdGySpi76yq07I6q/mN5Tuca5P85rKPWt7zqiSXJrl82e+nk/zSQc4EwFNkw5sGjmVuGgA4dE/VTQMA8KRteEnt6VBV35/kV5+wfF93XzQ9CwBzxoPT3Tdl9eU+AMcQl9QAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGVHdv9gxHpKp6MMkDmz0H7MeWJA9t9hCwD2d395n7ekFw4ChUVTu7e+tmzwGHwiU1AEYIDgAjBAeOTts3ewA4VL7DAWCEMxwARggOACMEB4ARggNHiKq6uaq2Ls//qqpO3+yZ4Kl0wmYPAHy57v6hzZ4BnmrOcOAwVNVaVf1jVb29qu6pqh1VdUFVfbSq/rmqvqOqTqmqd1TV7VX18ap6zfLeZ1bVDVW1u6releSZ6/Z7f1VtWZ7/RVXtqqpPVNW2ddv8V1VdV1V3VdWtVfW1G8z5I1X1seX4f/PYtlV1ZlV9oKruqKo/qKoH1h338qq6raruXF47/mn6GDlGCA4cvm9M8ltJzknyvCSvS3J+kquSvCnJNUn+trtfnOTlSd5SVackeUOSPd19TpLrkpy3n/3/RHefl2Rrkiur6quX9VOS3NrdL0xyS5Kf3GDGjyR5SXefm+SGJFcv67+wzPbtSf48yVlJUlXfkuTSJN/d3S9K8miSyw7+I4Ev55IaHL77uvvuJKmqTyT5YHd3Vd2dZC3Js5O8uqquWrY/Katf7C9L8tYk6e7dVbV7P/u/sqouWp4/J8lzkzyc5H+TvHdZ35XklRvM+Owk76qqZyU5Mcl9y/r5SS5aZnhfVX12Wf++rAJ4e1Ulq7Ovfz/A5wAbEhw4fF9Y93zvuj/vzerv2KNJLu7uf1r/puUX+Yb/8rqqvjfJBUle2t17qurmrIKVJF/sL/3L7Uez8d/n307y6939nmWfb37sEPs7dJI/6u6f3Wg+OBQuqcHT76YkV9RSmKo6d1m/Jctlqqp6QVaX5J7otCSfXWLzvCQveZIznJbkX5fnP75u/SNJfnSZ4cIkX7WsfzDJJVX1NctrZ1TV2U/y2JBEcGDCtUmekWR3Vd2z/DlJ3pbk1OVS2tVJbtvHe9+X5IRlm2uT3PokZ3hzkhur6sN5/P9H5xeTXFhVdyT5wSSfSvK57r43yc8lef9y7A8kedaTPDYk8d9Sg2NaVX1Fkke7+5GqemmSty03CcBTznc4cGw7K8m7q+q4rG5C2OhONzgsznDg/5GquibJa5+wfGN3X7cZ88B6ggPACDcNADBCcAAYITgAjBAcAEb8HyWvCbcH+Ur2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZwAAAD5CAYAAAATD4NkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALQElEQVR4nO3deayld13H8c+3LaSWQie1DdEIvahYQ7FsgwpBsVi3gCuDjQxG1GQCKEUSgsa6REyJRomkLtWpqSUwoOLGkkAlpAxCQJgpTNkkJthiwpJSsRZal9Kvf5xnYDqZuwyF753JvF7JzTn5Pb/nd373JnPfec55cqe6OwDw1Xbadm8AgFOD4AAwQnAAGCE4AIwQHABGnLHdGzhRnXfeeb22trbd2wA4qRw8ePAz3X3+sY4JzjrW1tZy4MCB7d4GwEmlqm5Z75i31AAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnDgZLJvX7K2lpx22upx377t3hFs2YbBqaodVfW8TeasVdUzN3uhZd4Hj3eDwGLfvmTPnuSWW5Lu1eOePaLDSWOzK5wdSTYMTpK1JJsGB7iPrrgiufPOe4/deedqHE4CmwXnd5J8U1W9v6p+b/n6YFV9oKouO2LOdy1zXrhcyfxTVd24fD1xKxupqtOr6veXtW+qqucv479RVe9dXndvVdUyfnlVfXiZ+5fL2AOq6tpl/vuq6keX8Yuq6j3LHm+qqoevs4c9VXWgqg7ceuutW9k2zPn4x49vHE4w1d3rH6xaS/LG7n5kVT09yXOS/GCS85K8N8l3JLkwyYu6+2nLOWcluae7/3v5xf6a7t555FrrvNZzk1ya5LLuvruqzu3u/zj8uMx5ZZK/7u43VNUnkjysu/+nqnZ0939W1UuTfLi7X1VVO5K8J8ljsoriu7t7X1XdP8np3X3XRj+YnTt39oEDBzb/CcKUtbXV22hHu+CC5Oabp3cDx1RVB7t757GOHc9NA0/KKh5f6O5PJ9mf5PHHmHe/JNdU1QeSvDbJI7a4/qVJ/rS7706Sw5FJcklV/fOy3lOSXLSM35RkX1U9K8ndy9j3J/mVqnp/krclOTPJQ5O8K8mvVtUvJ7lgs9jACenKK5Ozzrr32FlnrcbhJHA8waktznthkk8neVSSnUnufxzr3+tyq6rOTPInSXZ197cluSariCTJU5P8cZLHJTlYVWcsazy9ux+9fD20uz/S3a9O8iNJ7kpyfVU9ZYt7ghPH7t3J3r2rK5qq1ePevatxOAlsFpw7kjxwef72JJctn7Wcn+S7s3rL6sg5SXJOkk929z1JfjrJ6Vvcyz8mec4SjlTVuflSXD5TVWcn2bUcOy3JQ7r7hiQvzurmhrOTXJ/k+Ud8zvOY5fEbk3ysu69K8vokF29xT3Bi2b179fbZPfesHsWGk8gZGx3s7tuq6p3L7cxvyuptrENZXYm8uLs/VVW3Jbm7qg4luS6rK5K/rapnJLkhyee3uJc/T/ItSW6qqv9Lck13/1FVXZPkA0luzupzo2QVsVdV1TlZXdX8wfIZzm8nefmyRi3nPC3JZUmetaz7qSQv2eKeAPgK2fCmgVOZmwYAjt9X6qYBAPiybfiW2ldDVf1Akt89avjfuvvHp/cCwJzx4HT39Vl9uA/AKcRbagCMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACMEB4ARggPACMEBYITgADBCcAAYITgAjBAcAEYIDgAjBAeAEYIDwAjBAWCE4AAwQnAAGCE4AIwQHABGCA4AIwQHgBGCA8AIwQFghOAAMEJwABghOACMEBwARggOACOqu7d7Dyekqro1yS3bvQ9Yx3lJPrPdm4BjuKC7zz/WAcGBk1BVHejundu9Dzge3lIDYITgADBCcODktHe7NwDHy2c4AIxwhQPACMEBYITgADBCcOA+qqq1qvrgdu/jK62qXlJVly7Pf6mqztruPXFyc9MA3EdVtZbkjd39yOHXPaO77x56rZuT7Oxuf92AL5srHNhEVf16Vf1LVb2lql5TVS+qqsdV1aGqeleSXzhi7rOr6nVV9eaq+mhV/eYG664t676iqm6qqr85fBWxrL+/qg5W1fVV9XXL+Nuq6qVVtT/JC9ZZ98FV9ffL/g5V1ROX8X9Y1vtQVe05Yv7nquplVXVjVb21qs5fxq+rql1VdXmSr09yQ1XdsBy7uqoOLGv91n39GXNqEBzYQFXtTPL0JI9J8hNJDv85mb9Icnl3P+EYp317kt1JHp3kGcsa67kwyd7uvjjJfyV5XlXdL8kfJtnV3Y9Lcm2SK484Z0d3P7m7X7bOmlcl2d/dj0ry2CQfWsZ/bllvZ5LLq+prl/EHJLmxux+bZH+Se0Wyu69K8okkl3T3JcvwFcuf1rk4yZOr6uINvkdIIjiwmScleV1339XddyR5Q1a/oHd09/5lziuPOuct3X1bd9+V5O+WNdbz7939zuX5q5a5FyZ5ZJK3VNX7k/xakm844py/2mTPT0lydZJ09xe6+/Zl/PKqOpTk3UkekuThy/g9R6x5eA+b+cmqujHJ+5JclOQRWziHU9wZ270BOMHVMcY+n2SjDz+PPna8cyvJh9a5ejr8+selqr4nyaVJntDdd1bV25KcucU9Hb3Ww5K8KMnju/uzVXXdBmvBF7nCgY29I8kPV9WZVXV2kqcu47dX1eErgd1HnfN9VXVuVX1Nkh9L8s6s76FVdTgsP7W83keTnH94vKruV1UXHcee35rkucu5p1fVg5Kck+SzS2y+Ncl3HjH/tCS7lufPXPZwtDuSPHB5/qCsond7VT04yQ8dx944hbnCgQ1093ur6vVJDmX1/yMdSHJ7kp9Ncm1V3Znk+qNOe0dWb7N9c5JXd/eBDV7iI0l+pqr+LMm/Jrm6u/+3qnYluaqqzsnq3+nL86XPYjbzgiR7q+rnk3whq/i8OclzquqmrIL27iPmfz7JRVV1cPneLjvGmnuTvKmqPtndl1TV+5b9fCwbBxW+yG3RsImqOru7P7fcQfb2JHu6+8Z15j47q9uHf3EL665lG26nPsY+PtfdZ2/nHjg1uMKBze2tqkdk9TnFK9aLDbAxVzjwVbbcfvzWYxz63u6+7T6se0WSZxw1/NruvvJY82G7CQ4AI9ylBsAIwQFghOAAMEJwABjx/3yHY1XPY8wAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4248 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4252 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4460 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAD4CAYAAADVTSCGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKIUlEQVR4nO3cX8jedf3H8ddbhVKmzVAiinmLBVPIdjCKCu3vSUcVUhQ7ktIEoww6kw6CIoMoOilYkUc7KexIzYQO6m6geA+nywUVmeXZLyqLTKH2/h1c3/G7HdvadO/du/s9HnBxXdfn+93n+7ku2J58v9d1rbo7ADDloq1eAAD/3YQGgFFCA8AooQFglNAAMOqSrV7Aheaqq67qtbW1rV4GwLZy6NChP3X31SfbJjQnWFtby8bGxlYvA2BbqapnTrXNpTMARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIauNAdOJCsrSUXXbS6P3Bgq1cEZ+WSrV4AcBoHDiS33548//zq+TPPrJ4nyb59W7cuOAv/8Yymqtaq6ldV9d2qeqqqHq6qS6vquqp6qKoOVdV6Ve2uqour6ne1srOqjlXVzcs861X1plMcY0dV3VtVR6rqyaq6ZRn/TlVtLMf90qb976mqo8u+X1/Grq6q+6rqseX2rmX83VV1eLk9XlWXn4s3Ds6Lu+/+v8gc9/zzq3HYJs70jObNST7R3bdV1Q+S3JLk1iR3dPdvqurtSb7d3e+rql8nuSHJtUkOJbmpqh5N8sbu/u0p5v9ikue6+y1JUlVXLuN3d/efq+riJD+tqhuTPJvkI0l2d3dX1c5l328l+WZ3/6KqdiX5SZLrk3whyZ3dfbCqdiR54cSDV9XtSW5Pkl27dp3hWwLnwR/+cHbjcAE609A83d2Hl8eHkqwleWeSH1bV8X1etdyvJ7k5q9B8NcltSX6W5LHTzP+BJB8//qS7/7I8/NgSgUuSvD6rgB3NKhbfq6oHkty/aY4bNq3niuXs5WCSb1TVgSQ/6u5nTzx4d+9Psj9J9u7d26d7I+C82rVrdbnsZOOwTZzplwFe3PT430lem+Sv3b1n0+36Zft6kpuSvC3Jg0l2JnlPkp+fZv5K8pJ/4Kvq2qzORt7f3TcmeSDJq7v7X8vc9yX5cJKHNr2Wd2xazxu6++/dfU+STyW5NMkjVbX7DF8zbL2vfCW57LKXjl122WoctomX+62zvyV5uqo+miTLZzJvXbY9mtXZzrHufiHJ4SSfzipAp/Jwks8cf7JcOrsiyT+SPFdVr0vywWXbjiSv6e4Hk9yVZM8p5tiz3F/X3Ue6+2tJNpIIDdvHvn3J/v3JNdckVav7/ft9EYBt5ZV8vXlfkk9W1RNJnkryoSTp7heT/DHJI8t+60kuT3LkNHN9OcmVVfXLZb73dvcTSR5f5v5+VpfAssx1f1U9mdUluc8v459Nsnf5gsDRJHcs43dtmvefSX78Cl4znH/79iW//31y7NjqXmTYZqrbRxKb7d27tzc2NrZ6GQDbSlUd6u69J9vmB5sAjDqvP9isqluTfO6E4YPdfef5XAcA5895DU1335vk3vN5TAC2lktnAIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFHV3Vu9hgtKVf1Pkme2eh1wElcl+dNWLwJO4ZruvvpkG4QGtomq2ujuvVu9DjhbLp0BMEpoABglNLB97N/qBcDL4TMaAEY5owFglNAAMEpoABglNACMEhp4hapqrap+VVXfraqnqurhqrq0qq6rqoeq6lBVrVfV7qq6uKp+Vys7q+pYVd28zLNeVW86xTF2VNW9VXWkqp6sqluW8e9U1cZy3C9t2v+eqjq67Pv1Zezqqrqvqh5bbu9axt9dVYeX2+NVdfn8u8b/J5ds9QLgv8Sbk3yiu2+rqh8kuSXJrUnu6O7fVNXbk3y7u99XVb9OckOSa5McSnJTVT2a5I3d/dtTzP/FJM9191uSpKquXMbv7u4/V9XFSX5aVTcmeTbJR5Ls7u6uqp3Lvt9K8s3u/kVV7UrykyTXJ/lCkju7+2BV7Ujywrl8Y0Bo4Nx4ursPL48PJVlL8s4kP6yq4/u8arlfT3JzVqH5apLbkvwsyWOnmf8DST5+/El3/2V5+LGquj2rv8uvzypgR7OKxfeq6oEk92+a44ZN67liOXs5mOQbVXUgyY+6+9mzeuXwH7h0BufGi5se/zvJa5P8tbv3bLpdv2xfT3JTkrcleTDJziTvSfLz08xfSV7yo7equjars5H3d/eNSR5I8uru/tcy931JPpzkoeWPXJTkHZvW84bu/nt335PkU0kuTfJIVe1+eW8BnJzQwIy/JXm6qj6aJMtnMm9dtj2a1dnOse5+IcnhJJ/OKkCn8nCSzxx/slw6uyLJP5I8V1WvS/LBZduOJK/p7geT3JVkzynm2LPcX9fdR7r7a0k2kggN55TQwJx9ST5ZVU8keSrJh5Kku19M8sckjyz7rSe5PMmR08z15SRXVtUvl/ne291PJHl8mfv7WV0CyzLX/VX1ZFaX5D6/jH82yd7lCwJHk9yxjN+1ad5/JvnxK3zd8BL+CxoARjmjAWCUb53BBaSqbk3yuROGD3b3nVuxHjgXXDoDYJRLZwCMEhoARgkNAKOEBoBR/ws6mAJ9HTK4wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAD5CAYAAAAeEfMjAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALLElEQVR4nO3dX6xlZ1nH8d/TltjWUgq2ISgOhwChtFgqjCgolEK9IMaItkVxLqQiFS0pNeldJQZjFRPxDyY2joZCcDSKoCh/WpoGSttY0pn+pUXEAAWiF4K1QFsw0MeLvQYOk3OmMy3PnI79fJKTvfe71n7Xu08y5ztr7zVnqrsDAFOO2OoFAPD/m9AAMEpoABglNACMEhoARh211Qt4pDnxxBN7bW1tq5cBcFjZs2fPF7v7pI22Cc0+1tbWsnv37q1eBsBhparu2mybt84AGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpo4JFu165kbS054ojV7a5dW70iOChHbfUCgP3YtSs5//zkvvtWj++6a/U4SXbs2Lp1wUF40DOaqlqrqk9U1V9U1R1V9aGqOqaqnlZVV1TVnqq6tqpOrqojq+rTtXJCVT1QVS9e5rm2qp6+yTGOq6rLq+r2qrqtqs5exi+rqt3Lcd+0bv83V9Wdy75/sIydVFXvrqobl68fX8bPqKpblq+bq+qx341vHBwSl1zy7cjsdd99q3E4TBzoGc0zkryqu19bVX+X5Owk5yV5XXd/qqp+NMmfdfdLq+rfkpyS5KlJ9iR5UVV9LMmTu/vfN5n/jUnu6e4fSpKqevwyfkl3/3dVHZnk6qo6LckXkvxskpO7u6vqhGXfP0nyR919XVVtS3JlkmcluTjJBd19fVUdl+Rr+x68qs5Pcn6SbNu27QC/JXAIfO5zBzcOj0AHGprPdPcty/09SdaSvDDJu6pq7z7fs9xem+TFWYXm95K8Nsk1SW7cz/xnJfmFvQ+6++7l7iuXCByV5ElZBezOrGLxl1X1/iTvWzfHKevWc/xy9nJ9kj+sql1J3tPdX9j34N29M8nOJNm+fXvv7xsBh9S2bau3yzYah8PEgV4M8PV197+Z5AlJ/qe7T1/39axl+7VJXpTk+Uk+kOSEJC9J8tH9zF9JvuMHfFU9NauzkZd192lJ3p/k6O7+xjL3u5O8IskV617LC9at5we6+yvd/eYkv5LkmCQ3VNXJB/iaYetdemly7LHfOXbssatxOEw81KvOvpzkM1V1bpIsn8k8Z9n2sazOdh7o7q8luSXJr2YVoM18KMnr9z5Y3jo7Psm9Se6pqicmefmy7bgkj+vuDyS5KMnpm8xx+nL7tO6+vbt/P8nuJELD4WPHjmTnzuQpT0mqVrc7d7oQgMPKw7m8eUeS11TVrUnuSPIzSdLdX0/y+SQ3LPtdm+SxSW7fz1y/k+TxVfXxZb4zu/vWJDcvc78tq7fAssz1vqq6Lau35H5jGb8wyfblAoE7k7xuGb9o3bz3J/ngw3jNcOjt2JF89rPJAw+sbkWGw0x1+0hive3bt/fu3bu3ehkAh5Wq2tPd2zfa5h9sAjDqkP6Dzao6L8kb9hm+vrsvOJTrAODQOaSh6e7Lk1x+KI8JwNby1hkAo4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFglNAAMEpoABglNACMEhoARgkNAKOEBoBRQgPAKKEBYJTQADBKaAAYJTQAjBIaAEYJDQCjhAaAUUIDwCihAWCU0AAwSmgAGCU0AIwSGgBGCQ0Ao4QGgFFCA8AooQFgVHX3Vq/hEaWq/ivJXVu9DtjAiUm+uNWLgE08pbtP2miD0MBhoqp2d/f2rV4HHCxvnQEwSmgAGCU0cPjYudULgIfCZzQAjHJGA8AooQFglNAAMEpo4CGqqrWq+vhWr+O7rap+u6rOWu5fVFXHbvWaOLy5GAAeoqpaS/K+7n72IT7uUd39jUN0rM8m2d7dfiMBD5kzGthEVb2xqv61qq6qqr+pqour6nlVdWtV/UuSC9bt++qqem9VXVFVn6yq39rPvGvLvO+oqtuq6u/3njUs819TVXuq6sqqetIy/pGq+t2quibJGzaZ94lV9Q/L+m6tqhcu4/+4zHdHVZ2/bv+vVtVbquqmqrq6qk5axt9eVedU1YVJvj/Jh6vqw8u2y6pq9zLXmx7u95hHB6GBDVTV9iRnJ/nhJD+XZO+vfrk8yYXd/YINnvb8JDuSnJ7k3GWOzTwzyc7uPi3Jl5P8elU9JsmfJjmnu5+X5G1JLl33nBO6+4zufssmc741yTXd/Zwkz01yxzL+y8t825NcWFXft4x/b5Kbuvu5Sa5J8h1x7O63JvmPJGd295nL8CXLr8E5LckZVXXafl4jJBEa2MxPJHlvd9/f3V9J8s9Z/WA+obuvWfZ55z7Puaq7v9Td9yd5zzLHZj7f3dcv9/9q2feZSZ6d5KqquiXJbyZ58rrn/O2DrPmlSS5Lku7+Znffs4xfWFW3JrkhyQ8mecYy/sC6Ofeu4cG8sqpuSnJzklOTnHIAz+FR7qitXgA8QtUGY/cm2d+HmvtuO9h9K8kdm5wt7T3+QamqlyQ5K8kLuvu+qvpIkqMPcE37zvXUJBcn+ZHuvruq3r6fueBbnNHAxq5L8tNVdXRVHZfkp5bxe6pq79/8d+zznJ+sqidU1TFJXpHk+mxuW1XtDcqrluN9MslJe8er6jFVdepBrPnqJL+2PPfIqjo+yeOS3L1E5uQkP7Zu/yOSnLPc/8VlDfv6SpLHLvePzyp291TVE5O8/CDWxqOYMxrYQHffWFX/lOTWrP5/ot1J7klyXpK3VdV9Sa7c52nXZfV22tOT/HV3797PIT6R5Jeq6s+TfCrJZd39v1V1TpK3VtXjsvrz+cf59mctD+YNSXZW1WuSfDOr6FyR5HVVdVtWIbth3f73Jjm1qvYsr+3nN5hzZ5IPVtV/dveZVXXzsp5PZ/8hhW9xeTNsoqqO6+6vLleEfTTJ+d190yb7vjqry4BffwDzrmULLoveYB1f7e7jtnINPDo4o4HN7ayqU7L6HOIdm0UG2D9nNDBkuYz46g02vay7v/Qw5r0kybn7DL+ruy/daH/YakIDwChXnQEwSmgAGCU0AIwSGgBG/R+hu0W+ICvxCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4220 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4212 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4432 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4460 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4408 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    },
    {
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      "image/png": "[encoded data, 4412 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4428 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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    {
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      "image/png": "[encoded data, 4652 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 5132 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4292 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4096 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAD5CAYAAABlNTHvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALMklEQVR4nO3cb8hed33H8c+3tdhmikWTB+uD9h46EVtqsSn43ylVYTg2oa7ToFbGOjqpW6UMoUV8UtYiTBSx0sIsm6GWqgPZkE42bUux2rSmSTeGMGztA1cTx8SZKmv73YPrdE1Dct93Ysm3aV8vCFfyO7/zO78rkLxzznXfqe4OAEw5aXoDADy/CREAo4QIgFFCBMAoIQJg1AumN3Ci2bp1a6+trU1vA+CEcu+99+7v7m2HOyZER2ltbS27du2a3gbACaWqHjrSMY/mABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiOBEt3NnsraWnHTS6nXnzukdwVFZN0RVdXpV/dkGc9aq6v0bXWiZ98BmN1ZV366q7Zudf8i5l1TVGQf9+sGq2nosa8Gz2s6dyaWXJg89lHSvXi+9VIw4oWx0R3R6knVDlGQtyYYhOs4uSXLGRpPghHfVVcmBA08fO3BgNQ4niI1CdG2Sl1fV7qr61PLjgaraW1UXHzTnzcucK5Y7nzur6r7lxxs2s5GqOq2qvlxVe6rqliSnHXTsnVX1nWW9W6vqRcv4J6rqnmVPN9TKRUm2J9m57OnJdS5fzt9bVa9azn/rMmd3VX2/ql58hL1dWlW7qmrXvn37NvN24Pj40Y+ObhyehTYK0ceT/Ed3n5fk7iTnJXlNkguTfKqqfnOZc2d3n9fdn07ykyTv6O7XJrk4yWc3uZfLkhzo7nOTXJPk/CRZHqldneTCZc1dST62nPO57r6gu8/JKlzv7u6vLHN2LHt6dJm7fzn/+iRXLmNXJvnI8v7enOTJuU/T3Td09/bu3r5t27ZNvh04Ds488+jG4VnoaL5Y4U1Jbu7ux7v7kSS3J7ngMPNOSXJjVe1NcmuSV29y/bck+VKSdPeeJHuW8dcta9xVVbuTfCjJWcuxt1XVd5drvT3J2eus/7Xl9d6sHicmyV1J/rqqPprk9O5+bJN7hWeHa65Jtmx5+tiWLatxOEG84Cjm1ibnXZHkkazunE5K8sujuEYf4brf7O73PW2w6tQkn0+yvbsfrqpPJjl1nbV/tbw+nuV9d/e1VfWPSX43yd1VdWF3//tR7Bdm7dixer3qqtXjuDPPXEXoyXE4AWx0R/TzJE9+bnJHkour6uSq2pbVHcz3DpmTJC9J8uPufiLJB5KcvMm93JFkR5JU1TlJzl3G707yxqp6xXJsS1W9Mk9FZ//ymdFFR9j3EVXVy7t7b3dfl9XjvFdtcq/w7LFjR/Lgg8kTT6xeRYgTzLp3RN3906q6a/my629k9bjs/qzuXP6yu/+zqn6a5LGquj/JTVndpXy1qt6b5FtJfrHJvVyf5ItVtSfJ7qwil+7eV1WXJLm5ql64zL26u39QVTcm2ZvkwST3HLTWTUm+UFWPJnn9Otf8i6p6W1Z3Sf+2vEcAjqPqPtzTMI5k+/btvWvXrultAJxQqure7j7s94b6nxUAGHU0X6zwjKiqdyW57pDhH3b3e473XgCYd9xD1N23JbnteF8XgGcnj+YAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGVXdP7+GEUlX7kjw0vQ84jK1J9k9vAo7grO7edrgDQgTPEVW1q7u3T+8DjpZHcwCMEiIARgkRPHfcML0BOBY+IwJglDsiAEYJEQCjhAiAUUIEx1FVrVXVA0cx/9tVdUzfG1RVl1TVGQf9+sGq2nosa61zjbWqev8zuSbPP0IEz12XJDljo0kbqaoXrHN4LYkQ8WsRIjhGVfXBqtpTVfdX1d8vdxwnLce2VNXDVXVKVZ2/zPlOko9ssOZpVfXlZd1bkpx20LF3VtV3quq+qrq1ql60jH+iqu6pqgeq6oZauSjJ9iQ7q2p3VT25zuXL+Xur6lXr7OOTy1r/lORvlzufO5dz76uqNyxTr03y5uUaV1TVyVX1qWU/e6rqT4/195fnDyGCY1BVZye5Ksnbu/s1Sf44yf1J3rpM+b0kt3X3/yb5YpKPdvfrN7H0ZUkOdPe5Sa5Jcv5yva1Jrk5yYXe/NsmuJB9bzvlcd1/Q3edkFa53d/dXljk7uvu87n50mbt/Of/6JFdusJfzk/x+d78/yU+SvGM59+Ikn13mfDzJncs1Pr38Pvysuy9IckGSP6mq39rE++Z5TIjg2Lw9yVe6e3+SdPd/Jbklq7+kk+SPktxSVS9Jcnp3376M/90G674lyZeWNfck2bOMvy7Jq5PcVVW7k3woyVnLsbdV1Xerau+yr7PXWf9ry+u9WT1WW8/XDwrYKUluXK5x67KXw3lnkg8ue/xukpcl+e0NrsPz3HrPfoEjqySHfjf415P8VVW9NKu7iX9J8uLDzNvI4eZXkm929/ueNlh1apLPJ9ne3Q9X1SeTnLrO2r9aXh/Pxn/+f3HQz69I8kiS12T1D9hfHuGcSnJ5d9+2wdrw/9wRwbH55yR/WFUvS5Kqeml3/0+S7yX5TJJ/6O7Hu/u/k/ysqt60nLdjg3XveHJOVZ2T5Nxl/O4kb6yqVyzHtlTVK/NUdPYvnxlddNBaP88qhM+ElyT5cXc/keQDSU4+wjVuS3JZVZ2y7POVVfUbz9AeeI5yRwTHoLv/taquSXJ7VT2e5PtZfZXaLVk9uvqdg6Z/OMnfVNWBrP6iXs/1Sb5YVXuS7M4qbOnufVV1SZKbq+qFy9yru/sHVXVjkr1JHkxyz0Fr3ZTkC1X1aJLNfD61ns8n+WpVvTfJt/LU3dKeJI9V1f3L9T6T1SO/+6qqkuxL8ge/5rV5jvN/zQEwyqM5AEZ5NAcDqupdSa47ZPiH3f2e47yPDyf580OG7+rudb/fCZ5JHs0BMMqjOQBGCREAo4QIgFFCBMCo/wOzkTkEv8am5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4284 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaIAAAD5CAYAAABlNTHvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKwUlEQVR4nO3cb6yed13H8c93G3F/ICO4mrCQrhGQxS2zSIcif2Q4ITGIW5hZpBL2xPkXw8xijF3IFKebMxqIER1EgVjRIMQQJk4CyHAyWOdKW+ci0W2oUdh4grqNuO3rg/tq7OZpew7Mfnu21ys5uc+57t/9u76nD/budd33Wt0dAJhywvQAADy1CREAo4QIgFFCBMAoIQJg1EnTA2w2Z5xxRm/btm16DIBN5fbbb7+/u7es9ZwQbdC2bduyZ8+e6TEANpWquvdwz7k1B8AoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgSb3e7dybZtyQknrB53756eCDbkiCGqqmdW1U8dZc22qnrD0U60rDuw3sGq6q+qasd61z/utZdV1ZmH/HxPVZ3x9ewFx7Xdu5PLL0/uvTfpXj1efrkYsakc7YromUmOGKIk25IcNUTH2GVJzjzaItj0du1KHnjgscceeGB1HDaJo4Xo2iTPraq9VXX98nWgqvZX1aWHrHn5suaK5crn01X1t8vX96xnkKo6par+uKr2VdWfJDnlkOdeXVWfWfb7QFU9fTn+1qq6bZnphlq5JMmOJLuXmQ7u8+bl9fur6uzl9d+7rNlbVXdU1TMOM9vlVbWnqvbcd9996/l14Nj44hc3dhyOQ0cL0S8k+cfu3p7k1iTbk3xHkguTXF9Vz17WfLq7t3f3byX5cpLv7+7vTHJpknesc5afTPJAd5+X5JokL0qS5ZbaVUkuXPbck+Tnltf8dnef393nZhWu13b3ny5rdi4zPbisvX95/TuTXLkcuzLJTy+/38uTHFz7GN19Q3fv6O4dW7ZsWeevA8fA1q0bOw7HoY18WOFlSd7f3Y9095eSfCrJ+Wuse1qSd1XV/iQfSPLt69z/FUn+MEm6e1+Sfcvx7172uKWq9iZ5U5KzlucuqKrPLud6VZJzjrD/h5bH27O6nZgktyT5zar62STP7O6H1zkrHB+uuSY59dTHHjv11NVx2CRO2sDaWue6K5J8KasrpxOSPLSBc/Rhzvux7v6RxxysOjnJ7yTZ0d3/XFVXJzn5CHt/bXl8JMvv3d3XVtWNSX4gya1VdWF337WBeWHWzp2rx127Vrfjtm5dRejgcdgEjnZF9B9JDr5vcnOSS6vqxKraktUVzOcetyZJTk/yb939aJI3JjlxnbPcnGRnklTVuUnOW47fmuSlVfW85blTq+rb8r/RuX95z+iSw8x9WFX13O7e393XZXU77+x1zgrHj507k3vuSR59dPUoQmwyR7wi6u6vVNUty8euP5rV7bLPZ3Xl8vPd/e9V9ZUkD1fV55O8J6urlA9W1Q8n+WSS/1rnLO9M8gdVtS/J3qwil+6+r6ouS/L+qvqmZe1V3f0PVfWuJPuT3JPktkP2ek+S362qB5O85AjnfEtVXZDVVdKdy+8IwDFU3WvdDeNwduzY0Xv27JkeA2BTqarbu3vN/zfUv6wAwKiNfFjhCVFVr0ly3eMO393dFx/rWQCYd8xD1N03JbnpWJ8XgOOTW3MAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjhAiAUUIEwCghAmCUEAEwSogAGCVEAIwSIgBGCREAo4QIgFFCBMAoIQJglBABMEqIABglRACMEiIARgkRAKOECIBRQgTAKCECYJQQATBKiAAYJUQAjBIiAEYJEQCjqrunZ9hUquq+JPdOzwFrOCPJ/dNDwGGc1d1b1npCiOBJoqr2dPeO6Tlgo9yaA2CUEAEwSojgyeOG6QHg6+E9IgBGuSICYJQQATBKiAAYJUQwqKpeWVUfmZ4jSarq6qq6cnoOnnqECPiGVdWJ0zOweQkRfIOqaltV3VVV766qA1W1u6ourKpbquoLVfXi5etvquqO5fEFa+xzWlX9flXdtqz7oSOc85yq+lxV7a2qfVX1/PXMsbz2WVX1Z8vrbq2q89bY/8eq6qNVdUpV/egh5/q9g9Gpqv+sql+uqs8meUlVXVtVdy77/sYT+EfMk5wQwRPjeUnenuS8JGcneUOSlyW5MskvJrkrySu6+4VJ3prkV9fYY1eST3T3+UkuSHJ9VZ12mPP9RJK3d/f2JDuS/Ms650iSX0pyR3eftxx736EbV9XPJPnBJBcl2Zbk0iQvXc71SJKdy9LTkhzo7u9KcmeSi5Ocs+z7K0f4s4LHOGl6AHiSuLu79ydJVf1dko93d1fV/qz+Y356kvdW1fOTdJKnrbHHq5O87pD3aU5OsjXJ36+x9jNJdlXVc5J8qLu/UFXrmSNZhen1SdLdn6iqb66q05fn3phV1C7q7v+uqu9L8qIkty37n5Lky8vaR5J8cPn+q0keSvLuqroxyXHxvhebgysieGJ87ZDvHz3k50ez+gvf25J8srvPzepq4+Q19qgkr+/u7cvX1u5eK0Lp7j9K8rokDya5qapetc45Dp7n/2y5PB7IKljPOWTtew+Z6QXdffXy3EPd/cgyz8NJXpxVmC5K8hdrzQ1rESI4Nk5P8q/L95cdZs1NSd5cy6VHVb3wcJtV1bcm+afufkeSD2d1K269bs5ye62qXpnk/u7+6vLcHUl+PMmHq+rMJB9PcklVfcuy/llVddYa8zw9yend/edJ3pJk+wbm4SlOiODY+PUkv1ZVtyQ53CfM3pbVLbt9VXVg+flwLk1yoKr2ZvVe0PuOsPbxrk6yo6r2Jbk2yZsOfbK7/zqr95RuzOo23FVJ/nJZ/7Ekz15jz2ck+ciy5lNJrtjAPDzF+bfmABjligiAUT41B8exqnpNkused/ju7r54Yh74/+DWHACj3JoDYJQQATBKiAAYJUQAjPofH/MrAFcc1bkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4284 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4500 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ8AAAD4CAYAAAAzZOvCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJKElEQVR4nO3bb6ied33H8c+3rVOjHf5JJpUZz6bC1GErjRT8U//BhoxtFa0+yDYUaSyMTR/oYGTUP6z4YM8czrWCzWCBgYOBdGw6RGgUqp5oslo3ptimDkVSsdUZrNJ89+C+ytKYk5y29nvOca8XhPvkd133dX/v8+TN/buvU90dAJh00VYPAMD/P+IDwDjxAWCc+AAwTnwAGHfJVg+wU+zevbvX1ta2egyAHeXo0aP3dvees9fFZ5PW1tayvr6+1WMA7ChVdeJc67bdABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfGCnOnw4WVtLLrpo9Xj48FZPBJu2LeNTVXdX1e5H+dx3V9WuM/7/Pz+/yWCbOHw4OXAgOXEi6V49HjggQOwY2zI+j9G7k+y64Fmwkx08mJw69fC1U6dW67ADXDA+VbVWVf9RVR+rqjur6tNV9eSqel5V/WtVHa2qI1X1G1V1cVV9s1aeVlWnq+rq5TpHqur5G7zGM5frfqWqbkpSZxz7g6r6YlUdq6qbquriZf2jVbW+zPSBZe1Pkzw7yWer6rNnXOPGqjpeVbdX1bOWtWur6qvL+m0bzHVgeY31kydPbvqXCo+7e+55ZOuwzWz2k88Lknyku1+c5L4kb0pyc5I/6e4rk7wnyd9094NJ/ivJi5K8MsnRJK+qqicm+dXu/sYG139fks9190uTfDLJ3iSpqhcmeWuSV3T3FUkeTLJ/ec7B7t6X5CVJXl1VL+nuDyf5dpLXdvdrl/OekuT27r48yW1JrlvWb0jy28v6751rqO6+ubv3dfe+PXv2bPJXBQP27n1k67DNbDY+d3X3seXno0nWkrw8ySeq6liSm5Jcthw/kuTq5d+HsorQy5J86TzXvzrJ3ydJd/9zku8v669PcmWSLy2v8/okv74ce0tVfTnJV5K8OKvgnctPktx61uxJ8vkkh6rquiQXn2c22H5uvDHZddbu8q5dq3XYAS7Z5HkPnPHzg0meleS+5dPI2Y4kuT6r7a8bkrw3yWuy+tRxPn2OtUryd9395w9brPq1rD5tvay7v19Vh5I8aYPr/rS7H7r2g1nec3dfX1VXJfmdJMeq6oru/t4FZoTtYf+yAXDw4Gqrbe/eVXgeWodt7tHecPCDJHdV1bVJsnzHc/ly7AtZfSo63d0/TnIsyTuzitJGbsuynVZVb0jy9GX9M0neXFW/shx7RlU9N8kvJ/lRkvuX73DecMa1fpjk0gu9gap6Xnd/obtvSHJvkudc+G3DNrJ/f3L33cnp06tH4WEHeSx3u+1P8o6qOp7kziS/nyTd/UCSbyW5fTnvSFYxuOM81/pAkquXbbTfSnLPcq2vJfmLJJ+uqn9P8m9JLuvu41ltt92Z5ONZbaE95OYk/3LmDQcb+KuquqOqvppV/I5v6l0D8JjV/+1IcT779u3r9fX1rR4DYEepqqPLzWEP84v4dz4AbHObveHg56Kq3p7kXWctf767/3hyDgC21mh8uvuWJLdMviYA249tNwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHiA8A48QFgnPgAME58ABgnPgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADBOfAAYJz4AjBMfAMaJDwDjxAeAceIDwDjxAWCc+AAwTnwAGCc+AIwTHwDGiQ8A48QHgHHV3Vs9w45QVSeTnNjqOeAcdie5d6uHgA08t7v3nL0oPrDDVdV6d+/b6jngkbDtBsA48QFgnPjAznfzVg8Aj5TvfAAY55MPAOPEB4Bx4gM7RFW9v6rec57j11TViyZngkdLfOAXxzVJxIcdwQ0HsI1V1cEkf5TkW0lOJjma5P4kB5L8UpJvJPnDJFckuXU5dn+SNy2X+EiSPUlOJbmuu/9zcn7YiPjANlVVVyY5lOSqJJck+XKSv01yS3d/bznnL5N8t7v/uqoOJbm1u/9xOfaZJNd399er6qokH+ru182/E/hZl2z1AMCGXpXkn7r7VJJU1SeX9d9covO0JE9N8qmzn1hVT03y8iSfqKqHlp/4uE8MmyQ+sL2da2viUJJruvt4Vb0tyWvOcc5FSe7r7isev9Hg0XPDAWxftyV5Y1U9uaouTfK7y/qlSb5TVU9Isv+M83+4HEt3/yDJXVV1bZLUyuVzo8P5+c4HtrEzbjg4keS/k3wtyY+S/NmydkeSS7v7bVX1iiQfS/JAkjcnOZ3ko0kuS/KEJP/Q3R8cfxNwDuIDwDjbbgCMEx8AxokPAOPEB4Bx4gPAOPEBYJz4ADDufwE/7pYtabOafwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4464 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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     "output_type": "display_data"
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    {
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      "image/png": "[encoded data, 4400 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4420 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4420 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
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     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4292 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4096 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4284 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4280 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4496 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "image/png": "[encoded data, 4288 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "image/png": "[encoded data, 4456 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
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     "output_type": "display_data"
    },
    {
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      "image/png": "[encoded data, 4524 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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    {
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      "image/png": "[encoded data, 4236 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4168 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAD5CAYAAAAa5CMeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAK+0lEQVR4nO3de6xlZ1nH8d/TFlKHW6NtCEbp8YKQFkuBQYWAXMT73RYbaRTQZIKIRSJRYzUqpERDSLQmNgyxgjAQg1FBEmkIQiuEhk6vFJGYIMUoYiGkFlpjoI9/7DV4ejzX0mfODP18kpO9593vetc7J+n5ztpr57S6OwBwfztlvzcAwFcngQFghMAAMEJgABghMACMOG2/N3CiOPPMM3ttbW2/twFwUrn++us/091nbfaawCzW1tZy9OjR/d4GwEmlqm7b6jVvkQEwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMDAiezIkWRtLTnllNXjkSP7vSPYtW0DU1VnVNVLdpizVlXP3+lEy7xbt3n9/Kr6oZ3Wua/7hJPOkSPJoUPJbbcl3avHQ4dEhpPGTlcwZyTZ6Qf3WpIdA7ML5ye5T4HJ7vYJJ5dLL03uuuveY3fdtRqHk8BOgfn9JN9SVTdV1WuWr1ur6sNVddG6Oc9Y5rx8uVL5h6q6Yfl62k6bqKoHJ3llkouWdS6qqodU1ZVVdV1V3VhVP77MPbeqPrTMu6WqHrPJPh9VVdcsf761qp6xxXkPVdXRqjp6++237/Z7BsfHJz+5t3E4wVR3b/1i1VqSd3b346vqgiQvTvIDSc5Mcl2S70zy2CSv6O4fWY45kOSe7v7v5Yf/W7v74Pq1tjjXC5Mc7O6XLn9+dZJ/7O43V9UZST6U5IlZxeTa7j6yhOnUJI9cv3ZV/WqS07v7sqo6NcmB7r5zu2/EwYMH++jRo9t/t+B4WltbvS220dlnJ5/4xPHeDWyqqq7v7oObvbaXm/xPzyoWX+ruTye5OslTNpn3oCSvr6oPJ3lbknP2uuHF9yX5jaq6Kcn7kpye5NFJPpjkN6vq15Oc3d13b3LsdUleVFW/m+Tbd4oLnJAuuyw5cODeYwcOrMbhJLCXwNQu5708yaeTPCHJwSQP3uum1p3vgu4+f/l6dHd/tLvfkuTHktyd5Kqqes7GA7v7miTfneTfkrypqn7uPu4B9s/FFyeHD6+uWKpWj4cPr8bhJLBTYO5M8rDl+TVZ3SM5tarOyuoH+Ic2zEmSRyT5VHffk+Rns3oLazc2rnNVkl+uqkqSqnri8vjNST7e3ZcneUeS8zYeW1VnJ/nP7n59kj9N8qRd7gFOLBdfvHo77J57Vo/iwklk28B092eTfGD5ePFTk9yS5OYkf5/k17r7P5axL1bVzVX18iR/kuQFVXVtkm9L8oVd7uW9Sc45dpM/yauyervtluX8r1rmXZTk1uWts8cl+fP1+6yq1yR5VpKbqurGJBck+aNd7gGA+8m2N/kfSNzkB9i7++smPwDs2mnH+4RV9f1J/mDD8L90908e770AMOe4B6a7r8rqBj4AX8W8RQbACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADBCYAAYITAAjBAYAEYIDAAjBAaAEQIDwAiBAWCEwAAwQmAAGCEwAIwQGABGCAwAIwQGgBECA8AIgQFghMAAMEJgABghMACMEBgARggMACMEBoARAgPACIEBYITAADCiunu/93BCqKrbk9y23/uALZyZ5DP7vQnYxNndfdZmLwgMnASq6mh3H9zvfcBeeIsMgBECA8AIgYGTw+H93gDslXswAIxwBQPACIEBYITAADBCYGCPqmqtqm7d733c36rqlVX13OX5r1TVgf3eEyc3N/lhj6pqLck7u/vxx/m8p3X3F4/TuT6R5GB3++0B3GeuYGCDqvrtqvqnqnp3Vb21ql5RVU+uqpur6oNJfmnd3BdW1dur6l1V9bGq+p1t1l1b1n1jVd1SVX957CphWf/qqrq+qq6qqkct4++rqldX1dVJXrbFuo+sqr9e9ndzVT1tGf+bZb2PVNWhdfM/X1Wvraobquo9VXXWMv6Gqrqwqi5J8vVJ3ltV711eu6Kqji5r/d5X+j3mgUFgYJ2qOpjkgiRPTPJTSY79epY/S3JJdz91k8O+I8nFSc5P8rxlja08Nsnh7j4vyX8leUlVPSjJHye5sLufnOTKJJetO+aM7n5md792izUvT3J1dz8hyZOSfGQZ//llvYNJLqmqr1vGH5Lkhu5+UpKrk9writ19eZJ/T/Ls7n72Mnzp8qtqzkvyzKo6b5u/IyQRGNjo6Une3t13d/edSf42qx/IZ3T31cucN2045t3d/dnuvjvJXy1rbOVfu/sDy/M3L3Mfm+TxSd5dVTcl+a0k37DumL/YYc/PSXJFknT3l7r7jmX8kqq6Ocm1Sb4xyWOW8XvWrXlsDzv56aq6IcmNSc5Ncs4ujuEB7rT93gCcYGqTsS8k2e5m5cbX9jq3knxki6ujY+ffk6p6VpLnJnlqd99VVe9Lcvou97RxrW9K8ookT+nuz1XVG7ZZC77MFQzc2/uT/GhVnV5VD03yw8v4HVV17F/6F2845nur6mur6muS/ESSD2Rrj66qYyH5meV8H0ty1rHxqnpQVZ27hz2/J8kvLseeWlUPT/KIJJ9b4vK4JN+1bv4pSS5cnj9/2cNGdyZ52PL84VlF7o6qemSSH9zD3ngAcwUD63T3dVX1jiQ3Z/X/Bzqa5I4kL0pyZVXdleSqDYe9P6u3zb41yVu6++g2p/hokhdU1euS/HOSK7r7f6rqwiSXV9Ujsvrv8g/zf/dSdvKyJIer6heSfCmr2LwryYur6pasAnbtuvlfSHJuVV2//N0u2mTNw0n+rqo+1d3Prqobl/18PNsHFL7Mx5Rhg6p6aHd/fvmE1zVJDnX3DVvMfWFWH+d96S7WXcs+fLx5k318vrsfup974IHBFQz8f4er6pys7jO8cau4ANtzBQP3s+XjwO/Z5KXv6e7PfgXrXprkeRuG39bdl202H/abwAAwwqfIABghMACMEBgARggMACP+F1dMRSoR+732AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4152 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4148 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4364 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4188 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4176 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4392 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4100 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAD5CAYAAAD1JkggAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKfUlEQVR4nO3cXaxl9VnH8d8D0xdHKynMtCFGOBfa0sZWlGMVWxEzYtMba2sN0rHBaEI0GrReVasp1dTQq/rSNDKYtqOOjUGENjQxtCh9MfIyAzgglhJlBmMIBW1EHCUp/r3Y69jd4zmHfWie2XPg80lO9t5r/ddez56L+c5ae2ZqjBEA6HDasgcA4LlLZABoIzIAtBEZANqIDABtdi17gFPJnj17xsrKyrLHANhRjhw58vgYY+9G+0RmzsrKSg4fPrzsMQB2lKo6vtk+t8sAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0Gbpkamqlaq6b4E1b597vVpVv9c/HZwCDh1KVlaS006bPR46tOyJYGG7lj3AglaSvD3JnybJGONwksPLHAhOikOHkiuuSE6cmL0+fnz2Okn271/eXLCgZ7ySma4ivlBVB6vqaFX9eVXtrqp9VXV3Vd1bVR+uqhdN649V1fur6o7p59um7R+tqrfNve+Tm5zrc1V11/Tz/dOuq5P8QFXdU1XvrKqLq+qm6Zgzq+rGabbbquq10/arprlurap/qqorv/5fLjjJ3v3urwZmzYkTs+2wAyx6u+yVSQ6MMV6b5Ikkv5Lko0kuHWO8JrMrop+fW//EGON1ST6Y5He2Mc+XklwyxvjuJJcmWbsl9q4knxtjnD/G+MC6Y96b5O5ptl9L8kdz+85L8sYkr0vynqp6wfoTVtUVVXW4qg4/9thj2xgVToKHH97edjjFLBqZfx5j/M30/E+S7Evy0Bjji9O2g0kumlv/sbnHC7cxzwuSXFtV9ya5LsmrFzjmDUn+OEnGGH+V5KyqOmPa98kxxlNjjMczC9jL1x88xjgwxlgdY6zu3bt3G6PCSXDOOdvbDqeYRSMztvm+Y4PnX1k7X1VVkhducNw7kzya5DuTrG6yZr3a4vxPzW17OjvnOyiYed/7kt27v3bb7t2z7bADLBqZc6pq7YrksiSfTrKy9n1Lknck+czc+kvnHv92en4syQXT8zdndtWy3hlJHhlj/M/0nqdP2/8jyUs2me2zSfYnSVVdnOTxMcYTC30qONXt358cOJCce25SNXs8cMCX/uwYi/7J/h+SXF5V1yR5MMkvJbktyXVVtSvJnUn+YG79i6rq9swidtm07dokH6+qO5LckuQ/NzjPh5JcX1U/keSv59YcTfKVqvq7zL4LunvumKuSfKSqjiY5keTyBT8T7Az794sKO1aNsfWdsKpaSXLTGOM7FnrDqmNJVqfvQXaU1dXVcfiwvxkNsB1VdWSMsbrRvqX/Y0wAnrue8XbZGONYkoWuYqb1K1/HPAA8h7iSAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDaiAwAbUQGgDYiA0AbkQGgjcgA0EZkAGgjMgC0ERkA2ogMAG1EBoA2IgNAG5EBoI3IANBGZABoIzIAtBEZANqIDABtRAaANiIDQBuRAaCNyADQRmQAaCMyALQRGQDa1Bhj2TOcMqrqsSTHlz0HbGJPkseXPQRs4Nwxxt6NdogM7BBVdXiMsbrsOWA73C4DoI3IANBGZGDnOLDsAWC7fCcDQBtXMgC0ERkA2ogMAG1EBk6yqjpWVXs22ffKqrpn7ueJqvrlad+ZVfWpqnpwenzpszj3xVV103bngmdLZOAUMsZ4YIxx/hjj/CQXJDmR5IZp97uS3DLG+PYkt0yvl6aqTl/m+dkZRAa2UFU3VtWRqvr7qrpi2vazVfXFqrq1qq6tqg9O2/dW1fVVdef08/pp+1lVdXNV3V1V1ySpBU+/L8k/jjHW/qujNyc5OD0/mOTHtpj7xVX1kaq6dzrvD22wZtO5quqnquqO6WrqmrWgVNWTVfWbVXV7kgsX/Bw8j4kMbO1nxhgXJFlNcmVVfUuS30jyfUkuSXLe3NrfTfKBMcb3JPnxJH84bX9Pks+PMb4rySeSnLPguX8yycfmXr98jPFIkkyPL9vi2F+Y1r0myWVJDlbVi9et2XCuqnpVkkuTvH66ono6yf7pmG9Mct8Y43vHGJ9f8HPwPLZr2QPAKe7KqnrL9Pxbk7wjyWfGGP+WJFV1XZJXTPt/OMmrq/7vguCbq+olSS5K8tYkGWN8sqq+/EwnraoXJvnRJL/6LOd+Q5Lfn875hao6Pjfnms3m2pfZrbo7p8/yDUm+NO17Osn1z3ImnodEBjZRVRdnFo4LxxgnqurWJA8kedUmh5w2rf2vde+TJNv9V89vSnLXGOPRuW2PVtXZY4xHqursfPU3/g3HX/A8G81VSQ6OMTYK3H+PMZ5e8L3B7TLYwhlJvjwF5rzMbpHtTvKDVfXSqtqV2W2xNTcn+cW1F1V1/vT0s5luN1XVm5Is8rfCLsvX3ipLZre0Lp+eX57k41scP3/OV2R2K+yBLdbMz3VLkrdV1cumfWdW1bkLzAz/j8jA5v4yya6qOprkt5LcluRfkvx2ktuTfDrJ/Un+fVp/ZZLVqjpaVfcn+blp+3uTXFRVdyX5kSQPb3XSqtqd2fc9f7Fu19VJLqmqB6f9V2/xNh9KcnpV3Zvkz5L89BjjqXVrNpxrjHF/kl9PcvP02T+V5OytZobN+L/LYJuq6pvGGE9OVzI3JPnwGOOGZzoOno9cycD2XVVV9yS5L8lDSW5c8jxwynIlA0tQVWdl9t3HevvGGP+6jfd5Y5L3r9v80BjjLRuth5NNZABo43YZAG1EBoA2IgNAG5EBoM3/Au5v7oMV3xMtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4096 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4304 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "image/png": "[encoded data, 4412 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 4420 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4096 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4272 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4272 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4480 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4644 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4584 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4600 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4608 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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      "image/png": "[encoded data, 4128 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbEAAAD5CAYAAACprAsEAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALR0lEQVR4nO3cf8yvdV3H8debH4VHHAzhH7fwtpKaMMg4rDK1ZKRtYebUKFDDVaQ1bDRnLsy5GqWzzXQOHTRxyZkSWpu5NWz5izkFDggHstUfCbqldo5OZx51Au/++F7Mw9k5930fft3nzXk8trPv4XN9rs/14btxnlzX93uf6u4AwERHbfUGAOChEjEAxhIxAMYSMQDGEjEAxjpmqzdwJDn55JN7bW1tq7cBMMqtt966p7tPOdAxEXsMra2tZefOnVu9DYBRquqegx3zOBGAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDICxRAyAsUQMgLFEDI5kO3Yka2vJUUetXnfs2OodwSF51CJWVWtVddchzP9kVW1/iNe6uKqess8/311VJz+Utda5xlpVXfhIrglbaseO5JJLknvuSbpXr5dcImSM8ni5E7s4yVM2mrSRqjpmncNrSUSMx4/LL0/27n3w2N69q3EYYlMRq6pXVtWuqrqjqv5pudM5ajm2raq+XFXHVtXZy5zPJvmjDdZ8QlV9cFn3uiRP2OfY86vqs1V1W1VdX1XHL+NvqqpbququqrqqVl6aZHuSHVV1e1U9sM6ly/l3VtVPr7OPNy9rfSzJ3y93XDcu595WVc9apr4lyXOWa1xWVUdX1duW/eyqqj84yPqXVNXOqtq5e/fuzbzd8Nj40pcObRwOQxtGrKpOT3J5knO7+6wkv5vkjiS/tEx5YZIbuvsHSa5J8tru/oVNXPs1SfZ295lJrkhy9nK9k5O8Mcl53f2zSXYm+ZPlnHd19zndfUZW0Tu/uz+0zLmou3+mu7+7zN2znP/uJK/bYC9nJ3lRd1+Y5H+T/Mpy7gVJ3rnMeUOSG5drvH15H77V3eckOSfJ71fV0/ZfuLuv6u7t3b39lFNO2cTbAo+RU089tHE4DG3mTuzcJB/q7j1J0t3fSHJdVn/AJ8lvJbmuqk5IcmJ3f2oZf/8G6z43ybXLmruS7FrGfz7JM5J8pqpuT/I7SZ66HHteVd1UVXcu+zp9nfX/cXm9NatHgev5yD7xOzbJ1cs1rl/2ciDPT/LKZY83JXlykqdvcB04fFxxRbJt24PHtm1bjcMQ630G9IBK0vuNfSTJX1fVSVndxXw8yZMOMG8jB5pfSf61u3/7QYNVxyW5Msn27v5yVb05yXHrrP395fW+bPzv+Z19fn9Zkq8lOSuryH/vIOdUkku7+4YN1obD00UXrV4vv3z1CPHUU1cBe2AcBtjMndi/JfnNqnpyklTVSd39f0luTvKOJB/t7vu6+5tJvlVVz17O2+i/hE8/MKeqzkhy5jL+uSS/WFU/uRzbVlWn5YfB2rN8RvbSfdb6dlYRfSSckOQr3X1/klckOfog17ghyWuq6thln6dV1RMfoT3AY+Oii5K7707uv3/1KmAMs+GdWHf/e1VdkeRTVXVfks9n9W3A67J63PbL+0x/VZL3VtXerP6QX8+7k1xTVbuS3J5VFNPdu6vq4iQfqKofXea+sbv/q6quTnJnkruT3LLPWu9L8p6q+m6SzXwet54rk3y4ql6W5BP54V3ariT3VtUdy/XekdVjytuqqpLsTvIbD/PaAByC6j7UJ4A8VNu3b++dO3du9TYARqmqW7v7gD9H/Hj5OTEAjkCb+WLHw1JVL0jy1v2Gv9jdL360r73fPl6V5I/3G/5Md6/782wAHL4e9Ygt397b8m/wdfc1Wf0cGwCPEx4nAjCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMJaIATCWiAEwlogBMFZ191bv4YhRVbuT3LPV+4ADODnJnq3eBBzEU7v7lAMdEDEgVbWzu7dv9T7gUHmcCMBYIgbAWCIGJMlVW70BeCh8JgbAWO7EABhLxAAYS8QAGEvE4DBVVSdW1R9uMGetqi7cxFprVXXXI7c7ODyIGBy+TkyybsSSrCXZMGLweCVicPh6S5KfqKrbq+pty6+7qurOqrpgnznPWeZcttxx3VhVty2/nrWZC1XV0VX1N8vau6rq0mX8TVV1y3Ldq6qqlvHXVtUXlrkfXMaeWFXvXeZ/vqpetIyfXlU3L3vcVVVPf8TfKY5YvmIPh6mqWkvy0e4+o6pekuTVSX41q7/n8JYkP5fkp5K8rrvPX87ZluT+7v7eEosPdPf2fdc6yLVek+S8JBd0971VdVJ3f+OB12XO+5P8Q3f/c1X9T5Kndff3q+rE7v5mVf1Vki9097VVdWKSm5M8M6vQfq67d1TVjyQ5uru/+yi8ZRyB3InBDM/OKkj3dffXknwqyTkHmHdskqur6s4k1yd5xibXPy/Je7r73iR5IFxJnldVNy3rnZvk9GV8V5IdVfXyJPcuY89P8oaquj3JJ5Mcl+TUJJ9N8mdV9adZ/UWuAsYj5pit3gCwKbXJeZcl+VqSs7L6n9TvHcL6D3osU1XHJbkyyfbu/nJVvTmrMCXJryV5bpJfT/LnVXX6ssZLuvs/91v7P6rqpuWcG6rq97r745vcF6zLnRgcvr6d5EnL7z+d5ILls6tTsgrIzfvNSZITknylu+9P8ookR2/yWh9L8uqqOiZJquqk/DBYe6rq+CQvXY4dleTHuvsTSV6f1RdQjk9yQ5JL9/nc7JnL648n+e/ufmeSjyQ585DeBViHOzE4THX316vqM8tX4/8lq0d4d2R1x/T67v5qVX09yb1VdUeS92V15/ThqnpZkk8k+c4mL/d3SU5LsquqfpDk6u5+V1VdneTOJHdn9TlcsgrjtVV1QlZ3X29fPhP7yyR/u6xRyznnJ7kgycuXdb+a5C8e8psC+/HFDgDG8jgRgLE8ToQjSFW9IMlb9xv+Yne/eCv2Aw+Xx4kAjOVxIgBjiRgAY4kYAGOJGABj/T82QkJ6RvszNwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "image/png": "[encoded data, 4688 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 4652 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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    {
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      "image/png": "[encoded data, 4648 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
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    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 4632 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": "[encoded data, 4632 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": "[encoded data, 4632 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 4480 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "image/png": "[encoded data, 4484 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4348 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "image/png": "[encoded data, 4212 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": "[encoded data, 4112 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     "metadata": {
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     "output_type": "display_data"
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    {
     "data": {
      "image/png": "[encoded data, 4336 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4108 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
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     "metadata": {
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       "<Figure size 432x288 with 1 Axes>"
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       "<Figure size 432x288 with 1 Axes>"
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     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4576 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
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    },
    {
     "data": {
      "image/png": "[encoded data, 4132 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
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    {
     "data": {
      "image/png": "[encoded data, 4304 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4256 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
      "needs_background": "light"
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4376 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4408 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     "metadata": {
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     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4332 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
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     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4560 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4328 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4164 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4552 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4768 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 4308 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Loop for plotting Scatter plots\n",
    "for k in range(len(mody.columns)) :\n",
    "    for i in range(len(mody.columns)) :\n",
    "        for j in range(1) : \n",
    "            plt.scatter(x=mody.columns[i:i+1],y=mody.columns[j+k:j+k+1],color='r')    \n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "[encoded data, 9376 characters]\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Loop for plotting Line plots\n",
    "flag=0\n",
    "col = mody.columns\n",
    "for k in range(len(mody.columns)) :\n",
    "    if flag==1 :\n",
    "        col = col.drop(col[0])\n",
    "    for i in mody.columns :\n",
    "        for j in col :\n",
    "            sns.lineplot(x=mody[i],y=mody[j],data=mody,color='r')\n",
    "            plt.show()\n",
    "            flag=1\n",
    "            break       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.337</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2019-12-31          0.0        0.0   \n",
       "1      AFG      Asia  Afghanistan  2020-01-01          0.0        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-01-02          0.0        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-01-03          0.0        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-01-04          0.0        0.0   \n",
       "\n",
       "   total_deaths  new_deaths  total_cases_per_million  new_cases_per_million  \\\n",
       "0           0.0         0.0                      0.0                    0.0   \n",
       "1           0.0         0.0                      0.0                    0.0   \n",
       "2           0.0         0.0                      0.0                    0.0   \n",
       "3           0.0         0.0                      0.0                    0.0   \n",
       "4           0.0         0.0                      0.0                    0.0   \n",
       "\n",
       "   ...  aged_70_older  gdp_per_capita  extreme_poverty  cvd_death_rate  \\\n",
       "0  ...          1.337        1803.987              NaN         597.029   \n",
       "1  ...          1.337        1803.987              NaN         597.029   \n",
       "2  ...          1.337        1803.987              NaN         597.029   \n",
       "3  ...          1.337        1803.987              NaN         597.029   \n",
       "4  ...          1.337        1803.987              NaN         597.029   \n",
       "\n",
       "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0                 9.59             NaN           NaN                  37.746   \n",
       "1                 9.59             NaN           NaN                  37.746   \n",
       "2                 9.59             NaN           NaN                  37.746   \n",
       "3                 9.59             NaN           NaN                  37.746   \n",
       "4                 9.59             NaN           NaN                  37.746   \n",
       "\n",
       "  hospital_beds_per_thousand  life_expectancy  \n",
       "0                        0.5            64.83  \n",
       "1                        0.5            64.83  \n",
       "2                        0.5            64.83  \n",
       "3                        0.5            64.83  \n",
       "4                        0.5            64.83  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Importing the dataset\n",
    "dipsi=pd.read_csv('owid-covid-data.csv')\n",
    "dipsi.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso_code',\n",
       " 'continent',\n",
       " 'location',\n",
       " 'date',\n",
       " 'total_cases',\n",
       " 'new_cases',\n",
       " 'total_deaths',\n",
       " 'new_deaths',\n",
       " 'total_cases_per_million',\n",
       " 'new_cases_per_million',\n",
       " 'total_deaths_per_million',\n",
       " 'new_deaths_per_million',\n",
       " 'total_tests',\n",
       " 'new_tests',\n",
       " 'total_tests_per_thousand',\n",
       " 'new_tests_per_thousand',\n",
       " 'new_tests_smoothed',\n",
       " 'new_tests_smoothed_per_thousand',\n",
       " 'tests_units',\n",
       " 'stringency_index',\n",
       " 'population',\n",
       " 'population_density',\n",
       " 'median_age',\n",
       " 'aged_65_older',\n",
       " 'aged_70_older',\n",
       " 'gdp_per_capita',\n",
       " 'extreme_poverty',\n",
       " 'cvd_death_rate',\n",
       " 'diabetes_prevalence',\n",
       " 'female_smokers',\n",
       " 'male_smokers',\n",
       " 'handwashing_facilities',\n",
       " 'hospital_beds_per_thousand',\n",
       " 'life_expectancy']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To see all features together\n",
    "dipsi.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30221, 34)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                            object\n",
       "continent                           object\n",
       "location                            object\n",
       "date                                object\n",
       "total_cases                        float64\n",
       "new_cases                          float64\n",
       "total_deaths                       float64\n",
       "new_deaths                         float64\n",
       "total_cases_per_million            float64\n",
       "new_cases_per_million              float64\n",
       "total_deaths_per_million           float64\n",
       "new_deaths_per_million             float64\n",
       "total_tests                        float64\n",
       "new_tests                          float64\n",
       "total_tests_per_thousand           float64\n",
       "new_tests_per_thousand             float64\n",
       "new_tests_smoothed                 float64\n",
       "new_tests_smoothed_per_thousand    float64\n",
       "tests_units                         object\n",
       "stringency_index                   float64\n",
       "population                         float64\n",
       "population_density                 float64\n",
       "median_age                         float64\n",
       "aged_65_older                      float64\n",
       "aged_70_older                      float64\n",
       "gdp_per_capita                     float64\n",
       "extreme_poverty                    float64\n",
       "cvd_death_rate                     float64\n",
       "diabetes_prevalence                float64\n",
       "female_smokers                     float64\n",
       "male_smokers                       float64\n",
       "handwashing_facilities             float64\n",
       "hospital_beds_per_thousand         float64\n",
       "life_expectancy                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking whether column is categorical colomn or numerical colomn\n",
    "dipsi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                              64\n",
       "continent                            261\n",
       "location                               0\n",
       "date                                   0\n",
       "total_cases                          311\n",
       "new_cases                            311\n",
       "total_deaths                         311\n",
       "new_deaths                           311\n",
       "total_cases_per_million              375\n",
       "new_cases_per_million                375\n",
       "total_deaths_per_million             375\n",
       "new_deaths_per_million               375\n",
       "total_tests                        20978\n",
       "new_tests                          21377\n",
       "total_tests_per_thousand           20978\n",
       "new_tests_per_thousand             21377\n",
       "new_tests_smoothed                 20180\n",
       "new_tests_smoothed_per_thousand    20180\n",
       "tests_units                        19485\n",
       "stringency_index                    5666\n",
       "population                            64\n",
       "population_density                  1341\n",
       "median_age                          2983\n",
       "aged_65_older                       3373\n",
       "aged_70_older                       3123\n",
       "gdp_per_capita                      3303\n",
       "extreme_poverty                    12211\n",
       "cvd_death_rate                      2960\n",
       "diabetes_prevalence                 2057\n",
       "female_smokers                      8490\n",
       "male_smokers                        8746\n",
       "handwashing_facilities             17837\n",
       "hospital_beds_per_thousand          5374\n",
       "life_expectancy                      421\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12619</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12620</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12621</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12623</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent location        date  total_cases  new_cases  \\\n",
       "12619      IND      Asia    India  2019-12-31          0.0        0.0   \n",
       "12620      IND      Asia    India  2020-01-01          0.0        0.0   \n",
       "12621      IND      Asia    India  2020-01-02          0.0        0.0   \n",
       "12622      IND      Asia    India  2020-01-03          0.0        0.0   \n",
       "12623      IND      Asia    India  2020-01-04          0.0        0.0   \n",
       "\n",
       "       total_deaths  new_deaths  total_cases_per_million  \\\n",
       "12619           0.0         0.0                      0.0   \n",
       "12620           0.0         0.0                      0.0   \n",
       "12621           0.0         0.0                      0.0   \n",
       "12622           0.0         0.0                      0.0   \n",
       "12623           0.0         0.0                      0.0   \n",
       "\n",
       "       new_cases_per_million  ...  aged_70_older  gdp_per_capita  \\\n",
       "12619                    0.0  ...          3.414        6426.674   \n",
       "12620                    0.0  ...          3.414        6426.674   \n",
       "12621                    0.0  ...          3.414        6426.674   \n",
       "12622                    0.0  ...          3.414        6426.674   \n",
       "12623                    0.0  ...          3.414        6426.674   \n",
       "\n",
       "       extreme_poverty  cvd_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "12619             21.2          282.28                10.39             1.9   \n",
       "12620             21.2          282.28                10.39             1.9   \n",
       "12621             21.2          282.28                10.39             1.9   \n",
       "12622             21.2          282.28                10.39             1.9   \n",
       "12623             21.2          282.28                10.39             1.9   \n",
       "\n",
       "       male_smokers  handwashing_facilities hospital_beds_per_thousand  \\\n",
       "12619          20.6                   59.55                       0.53   \n",
       "12620          20.6                   59.55                       0.53   \n",
       "12621          20.6                   59.55                       0.53   \n",
       "12622          20.6                   59.55                       0.53   \n",
       "12623          20.6                   59.55                       0.53   \n",
       "\n",
       "       life_expectancy  \n",
       "12619            69.66  \n",
       "12620            69.66  \n",
       "12621            69.66  \n",
       "12622            69.66  \n",
       "12623            69.66  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To subset only those rows that have “India” in the “location” column\n",
    "dipsi = dipsi[dipsi.location == 'India']\n",
    "dipsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196, 34)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                            0\n",
       "continent                           0\n",
       "location                            0\n",
       "date                                0\n",
       "total_cases                         0\n",
       "new_cases                           0\n",
       "total_deaths                        0\n",
       "new_deaths                          0\n",
       "total_cases_per_million             0\n",
       "new_cases_per_million               0\n",
       "total_deaths_per_million            0\n",
       "new_deaths_per_million              0\n",
       "total_tests                        85\n",
       "new_tests                          91\n",
       "total_tests_per_thousand           85\n",
       "new_tests_per_thousand             91\n",
       "new_tests_smoothed                 80\n",
       "new_tests_smoothed_per_thousand    80\n",
       "tests_units                        73\n",
       "stringency_index                    5\n",
       "population                          0\n",
       "population_density                  0\n",
       "median_age                          0\n",
       "aged_65_older                       0\n",
       "aged_70_older                       0\n",
       "gdp_per_capita                      0\n",
       "extreme_poverty                     0\n",
       "cvd_death_rate                      0\n",
       "diabetes_prevalence                 0\n",
       "female_smokers                      0\n",
       "male_smokers                        0\n",
       "handwashing_facilities              0\n",
       "hospital_beds_per_thousand          0\n",
       "life_expectancy                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cvd_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12619</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12620</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737425</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12621</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12622</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737427</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12623</th>\n",
       "      <td>IND</td>\n",
       "      <td>Asia</td>\n",
       "      <td>India</td>\n",
       "      <td>737428</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.414</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      iso_code continent location    date  total_cases  new_cases  \\\n",
       "12619      IND      Asia    India  737424          0.0        0.0   \n",
       "12620      IND      Asia    India  737425          0.0        0.0   \n",
       "12621      IND      Asia    India  737426          0.0        0.0   \n",
       "12622      IND      Asia    India  737427          0.0        0.0   \n",
       "12623      IND      Asia    India  737428          0.0        0.0   \n",
       "\n",
       "       total_deaths  new_deaths  total_cases_per_million  \\\n",
       "12619           0.0         0.0                      0.0   \n",
       "12620           0.0         0.0                      0.0   \n",
       "12621           0.0         0.0                      0.0   \n",
       "12622           0.0         0.0                      0.0   \n",
       "12623           0.0         0.0                      0.0   \n",
       "\n",
       "       new_cases_per_million  ...  aged_70_older  gdp_per_capita  \\\n",
       "12619                    0.0  ...          3.414        6426.674   \n",
       "12620                    0.0  ...          3.414        6426.674   \n",
       "12621                    0.0  ...          3.414        6426.674   \n",
       "12622                    0.0  ...          3.414        6426.674   \n",
       "12623                    0.0  ...          3.414        6426.674   \n",
       "\n",
       "       extreme_poverty  cvd_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "12619             21.2          282.28                10.39             1.9   \n",
       "12620             21.2          282.28                10.39             1.9   \n",
       "12621             21.2          282.28                10.39             1.9   \n",
       "12622             21.2          282.28                10.39             1.9   \n",
       "12623             21.2          282.28                10.39             1.9   \n",
       "\n",
       "       male_smokers  handwashing_facilities hospital_beds_per_thousand  \\\n",
       "12619          20.6                   59.55                       0.53   \n",
       "12620          20.6                   59.55                       0.53   \n",
       "12621          20.6                   59.55                       0.53   \n",
       "12622          20.6                   59.55                       0.53   \n",
       "12623          20.6                   59.55                       0.53   \n",
       "\n",
       "       life_expectancy  \n",
       "12619            69.66  \n",
       "12620            69.66  \n",
       "12621            69.66  \n",
       "12622            69.66  \n",
       "12623            69.66  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting date coloumn to ordinal coloumn\n",
    "import datetime as dt\n",
    "dipsi[\"date\"]=pd.to_datetime(dipsi[\"date\"])\n",
    "dipsi[\"date\"]=dipsi[\"date\"].map(dt.datetime.toordinal)\n",
    "dipsi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#If more than 50% values in a column are null, then drop that entire column\n",
    "dipsi = dipsi.drop(dipsi.columns[dipsi.apply(lambda col : col.isnull().sum()/len(dipsi) > 0.5)],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['iso_code',\n",
       " 'continent',\n",
       " 'location',\n",
       " 'date',\n",
       " 'total_cases',\n",
       " 'new_cases',\n",
       " 'total_deaths',\n",
       " 'new_deaths',\n",
       " 'total_cases_per_million',\n",
       " 'new_cases_per_million',\n",
       " 'total_deaths_per_million',\n",
       " 'new_deaths_per_million',\n",
       " 'total_tests',\n",
       " 'new_tests',\n",
       " 'total_tests_per_thousand',\n",
       " 'new_tests_per_thousand',\n",
       " 'new_tests_smoothed',\n",
       " 'new_tests_smoothed_per_thousand',\n",
       " 'tests_units',\n",
       " 'stringency_index',\n",
       " 'population',\n",
       " 'population_density',\n",
       " 'median_age',\n",
       " 'aged_65_older',\n",
       " 'aged_70_older',\n",
       " 'gdp_per_capita',\n",
       " 'extreme_poverty',\n",
       " 'cvd_death_rate',\n",
       " 'diabetes_prevalence',\n",
       " 'female_smokers',\n",
       " 'male_smokers',\n",
       " 'handwashing_facilities',\n",
       " 'hospital_beds_per_thousand',\n",
       " 'life_expectancy']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To see all remaining features together after dropping columns with more than 50% null values\n",
    "dipsi.columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(196, 34)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For detemining no. of rows and coloumns\n",
    "dipsi.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loop for replacing the Nan values in numerical coloumns by mean and in categorical coloumns by mode of that column\n",
    "for i in dipsi.columns :\n",
    "    if dipsi[i].dtype != 'O' :\n",
    "        dipsi[i] = dipsi[i].replace(np.NaN,dipsi[i].mean())\n",
    "dipsi = dipsi.fillna(dipsi.mode().iloc[0])        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                           0\n",
       "continent                          0\n",
       "location                           0\n",
       "date                               0\n",
       "total_cases                        0\n",
       "new_cases                          0\n",
       "total_deaths                       0\n",
       "new_deaths                         0\n",
       "total_cases_per_million            0\n",
       "new_cases_per_million              0\n",
       "total_deaths_per_million           0\n",
       "new_deaths_per_million             0\n",
       "total_tests                        0\n",
       "new_tests                          0\n",
       "total_tests_per_thousand           0\n",
       "new_tests_per_thousand             0\n",
       "new_tests_smoothed                 0\n",
       "new_tests_smoothed_per_thousand    0\n",
       "tests_units                        0\n",
       "stringency_index                   0\n",
       "population                         0\n",
       "population_density                 0\n",
       "median_age                         0\n",
       "aged_65_older                      0\n",
       "aged_70_older                      0\n",
       "gdp_per_capita                     0\n",
       "extreme_poverty                    0\n",
       "cvd_death_rate                     0\n",
       "diabetes_prevalence                0\n",
       "female_smokers                     0\n",
       "male_smokers                       0\n",
       "handwashing_facilities             0\n",
       "hospital_beds_per_thousand         0\n",
       "life_expectancy                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To check if there are any Nan values present  \n",
    "dipsi.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error as MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X is our feature coloumn and Y is our target coloumn\n",
    "features = mody.columns  #input values\n",
    "features = features.drop('total_cases')\n",
    "X = mody[features].values\n",
    "y = mody['total_cases'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (176, 29)\n",
      "Shape of X_test:   (20, 29)\n",
      "Shape of y_train:  (176, 1)\n",
      "Shape of y_test:   (20, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train: \",X_train.shape)\n",
    "print(\"Shape of X_test:  \", X_test.shape)\n",
    "print(\"Shape of y_train: \",y_train.shape)\n",
    "print(\"Shape of y_test:  \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Regressor LinReg\n",
    "LinReg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the regressor to the training data\n",
    "LinReg.fit(X_train,y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on the test data: y_pred\n",
    "y_pred = LinReg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9999999999980906"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the accuracy\n",
    "LinReg.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Random Forest Regressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate rf\n",
    "rf = RandomForestRegressor(n_estimators=40,random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=40, random_state=2)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit rf to the training set    \n",
    "rf.fit(X_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict the test set labels\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set RMSE of rf: 5320.86\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the test set RMSE\n",
    "rmse_test = MSE(y_test,y_pred)**(1/2)\n",
    "print('Test set RMSE of rf: {:.2f}'.format(rmse_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999349200670565"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute the accuracy\n",
    "rf.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting total case for a new date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Formation of features for the date 13/08/2020\n",
    "new = {\n",
    "0  :{0:737650},\n",
    "1  :{0:75296},\n",
    "2  :{0:62070},\n",
    "3  :{0:1040},\n",
    "4  :{0:1910},\n",
    "5  :{0:54},\n",
    "6  :{0:43.4},\n",
    "7  :{0:0.75},\n",
    "8  :{0:25310165},\n",
    "9  :{0:333985},\n",
    "10 :{0:18.3},\n",
    "11 :{0:0.243},\n",
    "12 :{0:490275},\n",
    "13 :{0:0.35},\n",
    "14 :{0:71.4},\n",
    "15 :{0:1380000000},\n",
    "16 :{0:450.419},\n",
    "17 :{0:28.2},\n",
    "18 :{0:6},\n",
    "19 :{0:3.4},\n",
    "20 :{0:6426},\n",
    "21 :{0:21.2},\n",
    "22 :{0:282.3},\n",
    "23 :{0:10.4},\n",
    "24 :{0:1.9},\n",
    "25 :{0:20.6},\n",
    "26 :{0:59.55},\n",
    "27 :{0:0.53},\n",
    "28 :{0:69.66}\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>737650</td>\n",
       "      <td>75296</td>\n",
       "      <td>62070</td>\n",
       "      <td>1040</td>\n",
       "      <td>1910</td>\n",
       "      <td>54</td>\n",
       "      <td>43.4</td>\n",
       "      <td>0.75</td>\n",
       "      <td>25310165</td>\n",
       "      <td>333985</td>\n",
       "      <td>...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>6426</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.3</td>\n",
       "      <td>10.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1      2     3     4   5     6     7         8       9   ...  \\\n",
       "0  737650  75296  62070  1040  1910  54  43.4  0.75  25310165  333985  ...   \n",
       "\n",
       "    19    20    21     22    23   24    25     26    27     28  \n",
       "0  3.4  6426  21.2  282.3  10.4  1.9  20.6  59.55  0.53  69.66  \n",
       "\n",
       "[1 rows x 29 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting it to 2-D array\n",
    "data = pd.DataFrame.from_dict(new)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([825761.925])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting total cases using Random Forest Regressor Model\n",
    "rf.predict([data])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2635594.37949139])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Predicting total cases using Linear Regression Model\n",
    "LinReg.predict(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error as MSE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "iso_code                            object\n",
       "continent                           object\n",
       "location                            object\n",
       "date                                 int64\n",
       "total_cases                        float64\n",
       "new_cases                          float64\n",
       "total_deaths                       float64\n",
       "new_deaths                         float64\n",
       "total_cases_per_million            float64\n",
       "new_cases_per_million              float64\n",
       "total_deaths_per_million           float64\n",
       "new_deaths_per_million             float64\n",
       "total_tests                        float64\n",
       "new_tests                          float64\n",
       "total_tests_per_thousand           float64\n",
       "new_tests_per_thousand             float64\n",
       "new_tests_smoothed                 float64\n",
       "new_tests_smoothed_per_thousand    float64\n",
       "tests_units                         object\n",
       "stringency_index                   float64\n",
       "population                         float64\n",
       "population_density                 float64\n",
       "median_age                         float64\n",
       "aged_65_older                      float64\n",
       "aged_70_older                      float64\n",
       "gdp_per_capita                     float64\n",
       "extreme_poverty                    float64\n",
       "cvd_death_rate                     float64\n",
       "diabetes_prevalence                float64\n",
       "female_smokers                     float64\n",
       "male_smokers                       float64\n",
       "handwashing_facilities             float64\n",
       "hospital_beds_per_thousand         float64\n",
       "life_expectancy                    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dipsi.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X is our feature coloumn and Y is our target coloumn\n",
    "dipsi = dipsi.drop(['location','continent','iso_code','tests_units'],axis=1)\n",
    "features = dipsi.columns  #input values\n",
    "X = dipsi[features].values\n",
    "y = dipsi['total_cases'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting the dataset into the Training set and Test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train:  (176, 30)\n",
      "Shape of X_test:   (20, 30)\n",
      "Shape of y_train:  (176, 1)\n",
      "Shape of y_test:   (20, 1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train: \",X_train.shape)\n",
    "print(\"Shape of X_test:  \", X_test.shape)\n",
    "print(\"Shape of y_train: \",y_train.shape)\n",
    "print(\"Shape of y_test:  \",y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn = KNeighborsClassifier (n_neighbors=7,p=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=7, p=1)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the classifier to the training data\n",
    "knn.fit(X_train,y_train.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predict on the test data: y_pred\n",
    "y_pred=knn.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV:  0.2784090909090909\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANIL\\anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:670: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=8.\n",
      "  warnings.warn((\"The least populated class in y has only %d\"\n"
     ]
    }
   ],
   "source": [
    "# Predicting Cross Validation Score\n",
    "cv_knn = cross_val_score(estimator = knn, X = X_train, y = y_train.ravel(), cv = 8)\n",
    "print(\"CV: \", cv_knn.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy:  0.45\n"
     ]
    }
   ],
   "source": [
    "#Compute accuracy on the testing set\n",
    "accuracy_knn_test = accuracy_score(y_test, y_pred)\n",
    "print(\"Test set accuracy: \", accuracy_knn_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        0, 0],\n",
       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "        1, 0]], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the confusion matrix\n",
    "matrix = confusion_matrix(y_test, y_pred)\n",
    "matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Super Vector Classifier\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()\n",
    "svc.fit(X_train, y_train.ravel())\n",
    "pred_svc = svc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.00      0.00      0.00         6\n",
      "         3.0       0.15      1.00      0.26         3\n",
      "        50.0       0.00      0.00      0.00         1\n",
      "       165.0       0.00      0.00      0.00         1\n",
      "     10363.0       0.00      0.00      0.00         1\n",
      "     12380.0       0.00      0.00      0.00         1\n",
      "     46433.0       0.00      0.00      0.00         1\n",
      "     59662.0       0.00      0.00      0.00         1\n",
      "    106750.0       0.00      0.00      0.00         1\n",
      "    118447.0       0.00      0.00      0.00         1\n",
      "    190535.0       0.00      0.00      0.00         1\n",
      "    256611.0       0.00      0.00      0.00         1\n",
      "    490401.0       0.00      0.00      0.00         1\n",
      "\n",
      "    accuracy                           0.15        20\n",
      "   macro avg       0.01      0.08      0.02        20\n",
      "weighted avg       0.02      0.15      0.04        20\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ANIL\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# Computing the classification report\n",
    "print(classification_report(y_test.ravel(), pred_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = DecisionTreeClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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       "       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
       "        0]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the confusion matrix \n",
    "cfm = confusion_matrix(y_test, predict)\n",
    "cfm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.45"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Computing the accuracy\n",
    "accuracy = accuracy_score(y_test, predict)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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