machine_Learning_mastering_04_03_2023.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "548db8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import polars\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5c976ecb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18c1a7ee",
   "metadata": {},
   "outputs": [
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       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
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       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
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       "      <th>4</th>\n",
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       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "082bf034",
   "metadata": {},
   "outputs": [
    {
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       "      <th>BloodPressure</th>\n",
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       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
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       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
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       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4ea4466b",
   "metadata": {},
   "outputs": [
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       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
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       "      <td>60</td>\n",
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       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
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       "      <th>767</th>\n",
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       "      <td>93</td>\n",
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       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
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      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "763           10      101             76             48      180  32.9   \n",
       "764            2      122             70             27        0  36.8   \n",
       "765            5      121             72             23      112  26.2   \n",
       "766            1      126             60              0        0  30.1   \n",
       "767            1       93             70             31        0  30.4   \n",
       "\n",
       "     DiabetesPedigreeFunction  Age  Outcome  \n",
       "763                     0.171   63        0  \n",
       "764                     0.340   27        0  \n",
       "765                     0.245   30        0  \n",
       "766                     0.349   47        1  \n",
       "767                     0.315   23        0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34c7b767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "199"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Glucose'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e7b4be1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "768"
      ]
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['Glucose'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c6be6b73",
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ae015944",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 768\n",
       "Glucose                     768\n",
       "BloodPressure               768\n",
       "SkinThickness               768\n",
       "Insulin                     768\n",
       "BMI                         768\n",
       "DiabetesPedigreeFunction    768\n",
       "Age                         768\n",
       "Outcome                     768\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3e26e6c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Pregnancies               768 non-null    int64  \n",
      " 1   Glucose                   768 non-null    int64  \n",
      " 2   BloodPressure             768 non-null    int64  \n",
      " 3   SkinThickness             768 non-null    int64  \n",
      " 4   Insulin                   768 non-null    int64  \n",
      " 5   BMI                       768 non-null    float64\n",
      " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
      " 7   Age                       768 non-null    int64  \n",
      " 8   Outcome                   768 non-null    int64  \n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a64588d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "12d634ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 9)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74b95d80",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df5a1461",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "590167d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=d.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7bec0f6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<property at 0x15f9ac9e6d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "31a892ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import set_option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f12d1d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_option('display.width', 100)\n",
    "description=df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46a96617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin         BMI  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   31.992578   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002    7.884160   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   27.300000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   32.000000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   36.600000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   67.100000   \n",
       "\n",
       "       DiabetesPedigreeFunction         Age     Outcome  \n",
       "count                768.000000  768.000000  768.000000  \n",
       "mean                   0.471876   33.240885    0.348958  \n",
       "std                    0.331329   11.760232    0.476951  \n",
       "min                    0.078000   21.000000    0.000000  \n",
       "25%                    0.243750   24.000000    0.000000  \n",
       "50%                    0.372500   29.000000    0.000000  \n",
       "75%                    0.626250   41.000000    1.000000  \n",
       "max                    2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b16c4885",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_counts=df.groupby('Outcome').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ffb69229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Outcome\n",
       "0    500\n",
       "1    268\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46bf2648",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c51be2a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Outcome': 'class'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e7d5e2f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>3</th>\n",
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      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  \\\n",
       "0            6      148             72             35        0  33.6                     0.627   \n",
       "1            1       85             66             29        0  26.6                     0.351   \n",
       "2            8      183             64              0        0  23.3                     0.672   \n",
       "3            1       89             66             23       94  28.1                     0.167   \n",
       "4            0      137             40             35      168  43.1                     2.288   \n",
       "\n",
       "   Age  class  \n",
       "0   50      1  \n",
       "1   31      0  \n",
       "2   32      1  \n",
       "3   21      0  \n",
       "4   33      1  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a81a27c7",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  \\\n",
       "0              6      148             72             35        0  33.6                     0.627   \n",
       "1              1       85             66             29        0  26.6                     0.351   \n",
       "2              8      183             64              0        0  23.3                     0.672   \n",
       "3              1       89             66             23       94  28.1                     0.167   \n",
       "4              0      137             40             35      168  43.1                     2.288   \n",
       "..           ...      ...            ...            ...      ...   ...                       ...   \n",
       "763           10      101             76             48      180  32.9                     0.171   \n",
       "764            2      122             70             27        0  36.8                     0.340   \n",
       "765            5      121             72             23      112  26.2                     0.245   \n",
       "766            1      126             60              0        0  30.1                     0.349   \n",
       "767            1       93             70             31        0  30.4                     0.315   \n",
       "\n",
       "     Age  class  \n",
       "0     50      1  \n",
       "1     31      0  \n",
       "2     32      1  \n",
       "3     21      0  \n",
       "4     33      1  \n",
       "..   ...    ...  \n",
       "763   63      0  \n",
       "764   27      0  \n",
       "765   30      0  \n",
       "766   47      1  \n",
       "767   23      0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5fd7e17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6588a9f7",
   "metadata": {},
   "source": [
    "# Correlations Between Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "306c5910",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlations=df.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f92d3b84",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672 -0.073535  0.017683   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328  0.331357  0.221071   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371  0.088933  0.281805   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000  0.436783  0.392573   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783  1.000000  0.197859   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573  0.197859  1.000000   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928  0.185071  0.140647   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970 -0.042163  0.036242   \n",
       "class                        0.221898  0.466581       0.065068       0.074752  0.130548  0.292695   \n",
       "\n",
       "                          DiabetesPedigreeFunction       Age     class  \n",
       "Pregnancies                              -0.033523  0.544341  0.221898  \n",
       "Glucose                                   0.137337  0.263514  0.466581  \n",
       "BloodPressure                             0.041265  0.239528  0.065068  \n",
       "SkinThickness                             0.183928 -0.113970  0.074752  \n",
       "Insulin                                   0.185071 -0.042163  0.130548  \n",
       "BMI                                       0.140647  0.036242  0.292695  \n",
       "DiabetesPedigreeFunction                  1.000000  0.033561  0.173844  \n",
       "Age                                       0.033561  1.000000  0.238356  \n",
       "class                                     0.173844  0.238356  1.000000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c81b548",
   "metadata": {},
   "source": [
    "# Skew of Univariate Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9f257d6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "mySkew=df.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "506622ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                 0.901674\n",
       "Glucose                     0.173754\n",
       "BloodPressure              -1.843608\n",
       "SkinThickness               0.109372\n",
       "Insulin                     2.272251\n",
       "BMI                        -0.428982\n",
       "DiabetesPedigreeFunction    1.919911\n",
       "Age                         1.129597\n",
       "class                       0.635017\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mySkew"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a909c6f0",
   "metadata": {},
   "source": [
    "# Histogram of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "05ecd2fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "359fa5ae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ee6aec49",
   "metadata": {},
   "source": [
    "# Density Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1aa2ddc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>],\n",
       "       [<AxesSubplot:ylabel='Density'>, <AxesSubplot:ylabel='Density'>,\n",
       "        <AxesSubplot:ylabel='Density'>]], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='density', subplots=True, layout=(3, 3), sharex=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e5e5f8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6e9b3469",
   "metadata": {},
   "source": [
    "# Box and Whisker Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f8147ed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='box', subplots=True, layout=(3, 3), sharex=False, sharey=False)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba70116e",
   "metadata": {},
   "source": [
    "# Correlation Matrix Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9293c399",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ba1d1f6a",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pregnancies</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129459</td>\n",
       "      <td>0.141282</td>\n",
       "      <td>-0.081672</td>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.017683</td>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.221898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Glucose</th>\n",
       "      <td>0.129459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.466581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BloodPressure</th>\n",
       "      <td>0.141282</td>\n",
       "      <td>0.152590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>0.065068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SkinThickness</th>\n",
       "      <td>-0.081672</td>\n",
       "      <td>0.057328</td>\n",
       "      <td>0.207371</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>0.074752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Insulin</th>\n",
       "      <td>-0.073535</td>\n",
       "      <td>0.331357</td>\n",
       "      <td>0.088933</td>\n",
       "      <td>0.436783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.130548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.017683</td>\n",
       "      <td>0.221071</td>\n",
       "      <td>0.281805</td>\n",
       "      <td>0.392573</td>\n",
       "      <td>0.197859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.292695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <td>-0.033523</td>\n",
       "      <td>0.137337</td>\n",
       "      <td>0.041265</td>\n",
       "      <td>0.183928</td>\n",
       "      <td>0.185071</td>\n",
       "      <td>0.140647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>0.173844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.544341</td>\n",
       "      <td>0.263514</td>\n",
       "      <td>0.239528</td>\n",
       "      <td>-0.113970</td>\n",
       "      <td>-0.042163</td>\n",
       "      <td>0.036242</td>\n",
       "      <td>0.033561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.238356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>0.221898</td>\n",
       "      <td>0.466581</td>\n",
       "      <td>0.065068</td>\n",
       "      <td>0.074752</td>\n",
       "      <td>0.130548</td>\n",
       "      <td>0.292695</td>\n",
       "      <td>0.173844</td>\n",
       "      <td>0.238356</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
       "Pregnancies                  1.000000  0.129459       0.141282      -0.081672 -0.073535  0.017683   \n",
       "Glucose                      0.129459  1.000000       0.152590       0.057328  0.331357  0.221071   \n",
       "BloodPressure                0.141282  0.152590       1.000000       0.207371  0.088933  0.281805   \n",
       "SkinThickness               -0.081672  0.057328       0.207371       1.000000  0.436783  0.392573   \n",
       "Insulin                     -0.073535  0.331357       0.088933       0.436783  1.000000  0.197859   \n",
       "BMI                          0.017683  0.221071       0.281805       0.392573  0.197859  1.000000   \n",
       "DiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928  0.185071  0.140647   \n",
       "Age                          0.544341  0.263514       0.239528      -0.113970 -0.042163  0.036242   \n",
       "class                        0.221898  0.466581       0.065068       0.074752  0.130548  0.292695   \n",
       "\n",
       "                          DiabetesPedigreeFunction       Age     class  \n",
       "Pregnancies                              -0.033523  0.544341  0.221898  \n",
       "Glucose                                   0.137337  0.263514  0.466581  \n",
       "BloodPressure                             0.041265  0.239528  0.065068  \n",
       "SkinThickness                             0.183928 -0.113970  0.074752  \n",
       "Insulin                                   0.185071 -0.042163  0.130548  \n",
       "BMI                                       0.140647  0.036242  0.292695  \n",
       "DiabetesPedigreeFunction                  1.000000  0.033561  0.173844  \n",
       "Age                                       0.033561  1.000000  0.238356  \n",
       "class                                     0.173844  0.238356  1.000000  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "182134f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zdysp0PUYBzIzsybR1v1xFWskBzIzsyaQbivuQGZmZnXMLTJbiaRWYBbpNXwMODUiqv/FZR+RdCSwc0Rc0td1MbPeE8CyBr1tl4ffr74lETEqInYFllJ2W/c8p9gaJyImOIiZNZ8gaK3yUW8cyHrG3cB2kvaXdIek64BZklokXSppWp4B+jMAktaS9CNJsyXdIulWScfldc9K+oakhyTNkrRjTh8j6V5JD+e/O+T0sZJuknSbpKckfadUqTzL9EOSHpE0uZD/h3l5E0k35vpNk7RfTv+gpBn58bCkDXrzxTSzGghorfJRb9y12E2S+pFmcr4tJ40Bdo2IZ/KtGl6LiL0lrQPcI2kSsBcwAtgN2JTUNXl1odhFEbGnpH8CvgR8Cngc+ED+HcZBwH8Ax+b8o4D3AG8DT0j6AWmetJ/kbZ6RtHGF6n8f+F5E/EnSu0m/7dgp7/OzEXGPpIGsPOda6bjPJM/g/e4t/DEyW9OlmT0ak89Aq29dSTPy8t3A/wDvA6ZGxDM5/RBg91JrCxhEuq3B+4FfRUQb8KKkO8rKvin/fRD4WGHbaySNJH0mi7P0To6I1wAkzQG2AgYDd5XqEhGVZqo+CNhZemf6sw1z6+se4L8kXQvcFBHzyjfME4iOA7o0AbCZ9RXRWnGqw/rnQLb6lkTEqGJCDgiLi0nAuRExsSzfRzop++38t5UV79HFwB0RcYykEcCUCvmL24jOJ+NcC9g3IpaUpV8i6XekX+bfL+mgiHi8k7LMbA2WBns0ZiDzNbLamgicLak/gKTtJa0P/Ak4Nl8rG0aa6qUzg4AX8vLYKvLfB3xQ0tZ535W6FicB55SeSBqV/24bEbMi4tvAdGDHKvZnZmuw9DsyVfWoNw5ktXUVMAd4KN8S/Mek1tKNpNmhS2kPAK91UtZ3gG9Juoc0R1mHImIh6RrWTZIeAW6okO1zwOg8EGUOK0Zenifp0bzdEuD3ne3PzNZ8baGqHvXGXYurKSIGVkibQqHLL18D+1p+rETSlyLiTUlDgKmk36QRESMK208nt9Yi4j5g+0IR/5LTx5NuO17a5ojC8u8pC0LF/BGxCDihwnGcu+oRm1k9K7XIGpEDWd+5RdJGpPvzXBwRL/ZxfcysgQWitUE74RzI+khE7N/XdTCz5lKP3YbVcCAzM2sCgVgaa+SEQ93mQGZm1gTSD6LdtWhmZnXMgz3MzKxuRYjWcIvMrKJZrw9l69s+VbPynzn0qpqVDbDN7afXtHyApYNba1r+sBGVZiDrOa++sV5Ny1/6Vv/OM3XTlOe3q2n5e2zxQueZVtOi/kt7pJy2HmqRSTqUNFdrC3BV+R01JH0ZODk/7Ueaw3WTiPirpGeBN0izEC2PiNHdrY8DmZlZE0iDPbp/ys+3qLocOJg0scM0SRMiYs47+4q4FLg05/8o8IWy+V4PyL9j7RGN2c40M7OVlAZ7VPPoxBjg6YiYGxFLgeuBozrIfxLwi545isocyMzMmkRrqKpHJ7YAni88n5fTViFpPeBQ0rR8JQFMkvRgvh1Ut7lr0cysCXRxZo+hkqYXno/Lt24CKl5oa+9OGx8F7inrVtwvIuZL2hS4XdLjEXFXtRWrxIHMzKxJtFU/anFRB4Mw5gHDC8+3BOa3k/dEyroVI2J+/vuypN+Quiq7FcjctWhm1gTSpMFrVfXoxDRgpKStJa1NClYTyjNJGgR8ELi5kLZ+vnkv+ZZWh5DuAtItbpGZmTWBQCzrgSmqImK5pHNI91tsAa6OiNmSzsrrr8xZjwEmRUTxZsPDgN/kmxD3A66LiNu6WycHMjOzJhBBj/0gOiJuBW4tS7uy7Pl4CreYymlzgT16pBIF7lpcg0gaJuk6SXPziJ77JB0jaX9Jt/R1/cysnom2Kh/1xi2yNYRSW/u3wDUR8Y85bSvgSODVPqyamTWAoOdaZGuaxjyq+nQgsLTYPI+I5yLiB8VMki6S9KXC80cljcjLp0iaKekRST/PaVtJmpzTJ0t6d04/Pm/7iKS7clqLpEslTcv5P1P7wzaz3tJDgz3WOG6RrTl2AR5a3Y0l7QJcQPqNxiJJG+dVPwR+FhHXSDoduAw4GrgQ+HBEvJDvVA1wBvBaROwtaR3gHkmTIuKZ1a2Xma0ZAjXsjTXrL/Q2CUmX59bStCo3ORD4dWn+ssIPEPcFrsvLPwfen5fvAcZL+jRp5BGkobCnSJoBPAAMAUa2U78zJU2XNL31jcWVspjZGiSAZdGvqke9qb8aN67ZwLGlJxHxWUlDgell+Zaz8heQAfmvaP/X9UWRyz9L0j7AR4AZkkblMs6NiImdFpJ+5T8OYJ2tt6xmv2bWp9Sw9yNzi2zN8UdggKSzC2mV7p3xLLAngKQ9ga1z+mTg45KG5HWlrsV7ST9YhHRbhT/l9dtGxAMRcSGwiPRL/YnA2ZL65zzb5x8tmlmdC9LMHtU86o1bZGuIiAhJRwPfk/T/gIXAYuArZVlvZEX33zTgybz9bEnfBO6U1Ao8DIwFPgdcne8PtBA4LZdzqaSRpFbYZOARYCYwAngoj6JcSLqeZmYNoFFbZA5ka5CIWMCK1lO5KTnPEtK1rErbXwNcU5b2LOn6WXnej1UqAvhafphZA4lQXba2quFAZmbWBNJgj+5PUbUmciAzM2sKatgfRDuQmZk1gTTYw9fIzMysjtXjrB3VcCAzM2sCjTyzhwOZmVmTaHOLzKx9Wqt2k3tsc/vpNSsbYO7BV9e0fIDt7zy1puW/9Pzgmpa/4bA3a1r+soXr1rR8gNaBtT2JT5u1bc3KfmvJOt0uIwKWtTmQmZlZnUpdiw5kZmZWxzyzh5mZ1a1GHn7fmO1MMzMrox6bNFjSoZKekPS0pPMrrN9f0muSZuTHhdVuuzrcIjMzaxJtPdC1KKkFuBw4GJgHTJM0ISLmlGW9OyKOWM1tu8SBzMysCaRRiz0y1+IY4OmImAsg6XrgKKCaYNSdbdvlrkUzsyZQ+kF0NQ9gaOkO8PlxZqGoLYDnC8/n5bRy++a73P9e0i5d3LZL3CIzM2sSXehaXBQRo9tZV6mQ8h+SPgRsFRFvSjoc+C1Quv9hZ9t2mVtkZmZNoDRqscoWWUfmke4oX7IlMH+lfUW8HhFv5uVbgf6Shlaz7epwICsjqTWPsnlE0kOS3pfTR0h6tIf2MUXS6Lz8rKRZeX+TJG3WE/swMyvXQ6MWpwEjJW0taW3SzYAnFDNI2izfZR5JY0ix5pVqtl0d7lpc1ZKIGAUg6cPAt4AP1nifB0TEIkn/Qbo78+dKK/KHQRHRVuM6IKklIlprvR8z630RYnkPzOwREcslnQNMBFqAqyNitqSz8vorgeOAsyUtB5YAJ0ZEABW37W6d3CLr2IbAq+WJkgZI+mluST0s6YBO0teVdL2kmZJuANqbWO4uYLvc+ntM0o9Ifc3DJX1Z0rRcxjdyuetL+l1uzT0q6YScfomkOTnvd3PaeEnHFY7hzfx3f0l3SLoOmCWpRdKlhX19podeSzPrYz3UtUhE3BoR20fEthHxzZx2ZQ5iRMQPI2KXiNgjIt4bEfd2tG13uUW2qnUlzQAGAJsDB1bI81mAiNhN0o7AJEnbd5B+NvBWROwuaXdScKrkCGBWXt4BOC0i/knSIaQLpWNIF0snSPoAsAkwPyI+AiBpkKSNgWOAHSMiJG1UxTGPAXaNiGfy6KTXImJvSesA90iaFBHPFDfI+c4EaBlSzS7MrC95Zo/msiQiRkXEjsChwM9Kfb0F7wd+DhARjwPPAdt3kP4B4H9z+kxgZll5d+TguSGpKxPguYi4Py8fkh8Pk4LgjqTANgs4SNK3Jf1DRLwGvA78HbhK0seAt6o45qmFQHUIcEquzwPAkLyvlUTEuIgYHRGjWzZYv4pdmFlf66kW2ZrGLbIORMR9eaTNJmWr2nunO/oEdDTE9ICIWPROIakVtbis3G9FxI9X2aG0F3A48K3ccvq3fHH1Q6QLqeeQWpXLyV9ccmBeu1BM+b7OjYiJHdTXzOpMI99Y0y2yDuTuwRbSaJuiu4CTc57tgXcDT1SZviuwexerMhE4XdLAXMYWkjaV9C5Sl+X/At8F9sx5BuUhr+cBo3IZzwJ75eWjgP4d7OtsSf1LxyHJTS6zBtCGqnrUG7fIVlW6RgapdXJqRLSW9S7+CLhS0ixSS2dsRLydB2dUSr8C+KmkmcAMYGpXKhQRkyTtBNyX6/Em8AlgO+BSSW3AMtK1uA2AmyUNyPX/Qi7mJzl9KjCZlVthRVcBI4CHcsttIXB0V+prZmueCFjuG2s2h4ioOBlZRDwL7JqX/w6MrZCnvfQlpG6+SuWO6GhfhbTvA98vy/pnUguq3JgKZb4EvLeQ9NWcPgWYUsjXRvoJwNcq1dfM6lejdi06kJmZNYFGvkbmQGZm1iTCgczMzOpZPQ7kqIYDmZlZE4jwNTIzM6trotWjFs3MrJ75GplZO7RM9H9h7c4zrqalg2s7If/2d55a0/IBnvzgNTUt/8inDq1p+UuWt/f7+Z6xePCAmpYPEHM2qO0Ohi2vbfnd1MhzLTqQmZk1g0jXyRqRA5mZWZPwqEUzM6tb4cEeZmZW79y1aGZmda1RRy02ZjvTzMxWEpECWTWPzkg6VNITkp6WdH6F9SdLmpkf90rao7DuWUmzJM2QNL0njs0tMjOzJtETw+8ltQCXAwcD84BpkiZExJxCtmeAD0bEq5IOA8YB+xTWr3Qz4e5yIDMzaxI9dI1sDPB0RMwFkHQ96Wa97wSyiLi3kP9+YMse2XM73LVYgaQLJM3OzeIZkvbJzeGhFfLeW6mMwvrf5DKelvRaXp4h6X0dlHlkpeZ6Yf0ISY+u3tGZWTMKRFvbWlU9gKGSphceZxaK2gJ4vvB8Xk5rzxnA71eqCkyS9GBZuavNLbIykvYFjgD2zHd3Hgq0O21FRLyvo/Ii4phc7v7AlyLiiMK+2ttmAjChq3U3M+tIFxpkiyJidDvrKp24KhYt6QBSIHt/IXm/iJgvaVPgdkmPR8Rd1VdtVW6RrWpz0pv4NkBELIqI+aWVktaVdJukT+fnb+a/+0uaIunXkh6XdK3ai1QrO1fSQ/ni5465rLGSfpiXh+VW3SP5sVLglLSNpIcl7Z23uynX7ylJ3ynkO0TSfXlfv5I0MKdfImlObn1+N6cdL+nRvL9ufcDMbA3Rc4M95gHDC8+3BOaXZ5K0O3AVcFREvPJONfL5NCJeBn5DhTvad5UD2aomAcMlPSnpR5I+WFg3EPg/4LqI+EmFbd8DnAfsDGwD7FfF/hZFxJ7AFcCXKqy/DLgzIvYA9gRml1ZI2gG4ETgtIqbl5FHACcBuwAmShudW5deBg/K+pgNflLQxcAywS0TsDvx7LuNC4MN5n0dWqrSkM0vdDq2LF1dxmGbW56LKR8emASMlbS1pbeBEynqQJL0buAn4ZEQ8WUhfX9IGpWXgEKDbl0nctVgmIt6UtBfwD8ABwA2F61U3A9+JiGvb2XxqRMwDkDQDGAH8qZNd3pT/Pgh8rML6A4FTct1agdckDQY2yfU5NiJmF/JPjojXch3mAFsBG5GC6z25kbg2cB/wOvB34CpJvwNuyWXcA4yX9MtC/VYSEeNII5EYsOXwBv2ZpVlj6YnfkUXEcknnABOBFuDqiJgt6ay8/krSl+EhwI/yOWd57qocBvwmp/UjNQpu626dHMgqyAFjCjBF0iygND36PcBhkq6LqDj+5+3CcivVvb6lbarNX/Ia6YLrfhRaae3UQcDtEXFSeSGSxgAfIn2rOgc4MCLOkrQP8BFghqRRxa4BM6s/AbS19cwPoiPiVuDWsrQrC8ufAj5VYbu5wB7l6d3lrsUyknaQNLKQNAp4Li9fCLwC/KgXqzQZODvXrUXShjl9KXA0cIqkf+ykjPuB/SRtl8tZT9L2+TrZoPyhPI90rEjaNiIeiIgLgUWs3B9uZvUogFB1jzrjQLaqgcA1pQEQpC65iwrrzwMGFAdS1NjngQNyy/BBYJfSiohYTBph+QVJR7VXQEQsBMYCv8jHdD+wI7ABcEtOuxP4Qt7k0jz45FHgLuCRHj8qM+t1EdU96o27FstExINApSH1IwrLpxXyD8x/p5C6I0vp55SVu9L6nDaisDwd2D8vjwfG5+WXSD82LLdrXv83YO9C+vhCmUcUlv9Ylq9klRFDEVHpWp2Z1bs6DFLVcCAzM2sK1c2jWI8cyMzMmoVbZGZmVrcCoodGLa5pHMjMzJqGA5mZmdUzdy2amVldcyAza18tB0MNG/HX2hUOvPT84JqWD3DkU4fWtPwJI7s9y0+HRk4ZW9Pye0PrgBqfxdduq13ZPfGL39IPohuQA5mZWZOoxx87V8OBzMysWXjUopmZ1TO5RWZmZnWrunuN1SUHMjOzplCfM9tXw4HMzKxZuEVmZmZ1rYa/EOhLDmRmZs2ggX9H5htr1glJb/ZweSPyjTORNFrSZT1ZvpmteRTVPTotRzpU0hOSnpZ0foX1knRZXj9T0p7Vbrs63CKz0k09p/d1PcysxnrgGpmkFuBy4GBgHjBN0oSImFPIdhgwMj/2Aa4A9qly2y5zi6zOSNpf0hRJv5b0uKRrJSmvu0TSnPwN6Ls5bbyk4wrbr9Kyy2XekpcvknR13sdcSZ/rrWMzs7owBng6IuZGxFLgela9i/1RwM8iuR/YSNLmVW7bZW6R1af3ALsA84F7gP0kzQGOAXaMiJC0UTfK3xE4ANgAeELSFRGxrJhB0pnAmQD9Nqr9XIVm1n1d+EH0UEnFXppxETEuL28BPF9YN4/U6iqqlGeLKrftMgey+jQ1IuYBSJoBjADuB/4OXCXpd8At3Sj/dxHxNvC2pJeBYaQP3Dvyh3ocwIAthzfooF6zBhJ0ZYqqRRExup11lQopPwe0l6eabbvMXYv16e3CcivQLyKWk5rtNwJHA6Xp0JeT3+fcBbn26pTfzfqa2Zogqnx0bB4wvPB8S1LvUDV5qtm2yxzIGoSkgcCgiLgVOA8YlVc9C+yVl48C+vd23cxszdBDoxanASMlbS1pbeBEYEJZngnAKXn04nuB1yJiQZXbdpm/aTeODYCbJQ0gNd+/kNN/ktOnApOBxX1UPzPraz1wESAilks6B5gItABXR8RsSWfl9VcCtwKHA08DbwGndbRtd+vkQFYnImJg/jsFmFJIP6eQbUyF7V4C3ltI+mpOfxbYtbzMiLiobPtdu1t3M1tD9NDV7Nzzc2tZ2pWF5QA+W+223eVAZmbWBKr9sXM9ciAzM2sWvrGmmZnVM7fIzMysvjmQmZlZ3fI1MjMzq3sOZGaVxVqwfP3a/Ye8+sZ6NSsbYMNhPXqHnIqWLK/t79BHThlb0/Kf2n98TcvfZtIZNS0fgPVqe1fJjYbU7nO0sF/P1F0NemNNz+xhZmZ1zS0yM7Nm4a5FMzOrWx7sYWZmdc+BzMzM6poDmZmZ1SvRuKMWHcjMzJqBr5GZmVnda9BA5t+RNThJrZJmSHpE0kOS3pfTR0gKSRcX8g6VtEzSD/PziyR9qa/qbmY9LKp81BkHssa3JCJGRcQepJtqfquwbi5wROH58UC379ZqZmum0j3JOnvUGwey5rIh8Grh+RLgMUmj8/MTgF/2eq3MrHe4RWZ1at3ctfg4cBVwcdn664ETJW0JtALzqylU0pmSpkua3vrm4p6tsZn1vEijFqt5dIekjSXdLump/HdwhTzDJd0h6TFJsyV9vrDuIkkv5PPWDEmHd7ZPB7LGV+pa3BE4FPiZpOJtYm8DDgZOAm6ottCIGBcRoyNidMvA9Xu2xmZWG73TIjsfmBwRI4HJ+Xm55cA/R8ROwHuBz0raubD+e/m8NSoibu1shw5kTSQi7gOGApsU0pYCDwL/DNzYR1Uzs17QS9fIjgKuycvXAEeXZ4iIBRHxUF5+A3gM2GJ1d+hA1kQk7Qi0AK+UrfpP4CsRUZ5uZo2k+hbZ0NKlg/w4swt7GRYRCyAFLGDTjjJLGgG8B3igkHyOpJmSrq7UNVnOvyNrfOtKmpGXBZwaEa3F3sWImI1HK5o1tq51Gy6KiNHtrZT0B2CzCqsu6EqVJA0k9QSdFxGv5+QrSNfyI//9T+D0jspxIGtwEdHSTvqzwK4V0scD4/PyRbWrmZn1JtFzQ+sj4qB29yO9JGnziFggaXPg5Xby9ScFsWsj4qZC2S8V8vwEuKWz+rhr0cysSfTSNbIJwKl5+VTg5lXqkbqE/gd4LCL+q2zd5oWnxwCPdrZDBzIzs2bRO6MWLwEOlvQUaUT0JQCS3iWpNAJxP+CTwIEVhtl/R9IsSTOBA4AvdLZDdy2amTWLXvixcx409qEK6fOBw/Pyn0i9nZW2/2RX9+lAZmbWDOp0+qlqOJCZmTULBzIzM6tnvrGmWXsCWt6u2N3dI5a+1b9mZQMsW7huTcsHWDx4QM33UUvbTDqjpuXPPeR/alo+wDY3fqam5b/+Ru0+R62tPfP/5a5FMzOrX3U6s301HMjMzJqFA5mZmdWrnpzZY03jQGZm1iTU1piRzIHMzKwZ+BqZmZnVO3ctmplZfXMgMzOzetaoLbJOZ7+X1JpnJp4t6RFJX5S0Vl43WtJlnWw/VtIPu1IpSV/rSv6ybcdLeibX+SFJ+3Zh23fqKuksSaesbj2q3N8ISUsKsz/PkLR2D5Y/VtK7Cs+vkrRzT5VvZnWmd2a/73XVtMiWRMQoAEmbAtcBg4B/jYjpwPQa1OtrwH90Y/svR8SvJR0C/BjYvasFRMSVXckvqV9ELO/qfoA/l17fGhhLupfPfICI+FSN9mNma7po3CmqunQ/soh4GTgTOEfJ/pJuAZA0RtK9kh7Of3cobDpc0m2SnpD0r6VESZ+QNDW3RH4sqUXSJcC6Oe3aDvK15NbXo/neNZXuWXMXsF17ZeT00yQ9KelO0j1ySnW7SNKX8vLekmZKuk/SpZIezeljJf1K0v8BkyStL+lqSdPy63BUzteSt5uWy+lwrhxJbxaWj5M0Pi+Pl3RZfn3nSjqukO//5dfhEUmX5HWjgWvzMa8raYqk0Tn/STn/o5K+Xdy3pG/mcu6XNKyjuppZfSj9jqwXbqzZ67p8Y82ImJu327Rs1ePAByLiPcCFrNyiGgOcDIwCjs9dkjsBJwD75RZJK3ByRJxPbgVGxMnt5ctlbRERu0bEbsBPK1T3o8Cs9spQuhPpN0gB7GCgvW63nwJnRcS+eduifYFTI+JA4ALgjxGxN+mGcJdKWh84A3gtp+8NfFrS1nn7bQvdipe3s/+izYH3A0ew4oZ1hwFHA/tExB7AdyLi16TW8sn5tVxSKiB3N34bOJD0Ou4t6ei8en3g/lzOXcCnK1VC0pmSpkua3vbm4iqqbWZ9LqK6R51Z3cEelWawHARcI2kkqZe1ONPr7flma0i6iXQiXg7sBUyTBLAu8HKFcj/UTr7/A7aR9APgd8CkwjaXSvo6sJAURNorYx9gSkQszHW7Adh+pQOVNgI2iIh7c9J1pCBSPLa/5uVDgCNLLTlgAPDunL57oQU1CBgJPEnXuxZ/GxFtwJxCa+kg4KcR8RZAoT7t2ZuVj/ta4APAb4GlwC0534OkAL+KiBgHjANYZ/jw+vvkmzWhemxtVaPLgUzSNqRWycvAToVVFwN3RMQxkkYAUwrryl++IAXDayLiq53tsr18kvYAPgx8Fvg4cHpe9eXcIinlO6BSGbkV0tlb29m008XmiIBjI+KJsv0IODciJpalj2inzGKdyqdNf7tC3UTXLtF2dEzLIt75StaKR7aaNYY6HchRjS51LUraBLgS+GHhZFcyCHghL48tW3ewpI0lrUvqArsHmAwcpzSAhLx+q5x/maRSi65iPklDgbUi4kbgX4A9O6h6e/t6ANhf0pC8v+PLN4yIV4E3JL03J53YwX4mAufmwIWk9xTSzy4dk6Ttc5dje16StJPS6NBjOshXMgk4XdJ6pePL6W8AG1TI/wDwQUlD87XCk4A7q9iPmdUxtVX36NY+0vn1dklP5b+D28n3bL5OP0PS9K5uX1RNICsNvJgN/IF00vxGhXzfAb4l6R6gpWzdn4CfAzOAGyNiekTMAb5OGiQxE7iddP0HUpfVTEnXdpBvC2CKpBnAeKDdll17ZUTEAuAi4L58bA+1U8QZwDhJ95FaM6+1k+9iUpfqTKUBIRfn9KuAOcBDOf3HdNzSOZ/UvfdHYEEH+UrHdxswAZieX49S1+Z44MrSYI9C/gWk1+sO4BHgoYi4ubP9mFl9641ARjp/TY6IkaRGxPkd5D0gX8MfvZrbA6BVG1ZWTtLAiHgzL59PCoKf7+NqrTHWGT48tvx8pUGjPWP5JktrVjaAFte+93StwbU9hlprW9blcWFd0gg31lQN3+P5X7+ct+e+0K27aw4cPDz2+FB1p617b/zyg2XBpWqSngD2j4gFeUDdlIjYoUK+Z4HREbFodbYvqu2ns3F8JLdqHgX+Afj3vq6QmVlXdWH4/dDSqOT8OLMLuxmWe31KvT/lI9xLgtRL9mBZ+dVu/w5fyK9CRNwA3NDX9TAz65bqO+AWddQik/QHYLMKqy7oQm32i4j5eezC7ZIej4i7urD9OxzIzMyaQE/eWDMiDmp3P9JLkjYvdA1W+lkVEVGacehlSb8h/d74LtJgt063L3LXoplZM4hAbdU9umkCcGpePhVYZSCZ0ixIG5SWSb+1fbTa7cs5kJmZNYvemTT4EtJPrp4iTahQmoHoXZJuzXmGAX+S9AgwFfhdHn3d7vYdcdeimVmT6I2ZPfIsTh+qkD4fODwvzwX26Mr2HXEgMzNrBgF0v9twjeRAZt02aIO3+PAB7f2WvPumPL9dzcoGaB1Y+x72mFNpgpWe0zqgxieo9Wp7/49a/8YLYO6xP65p+Tvd88mala1u/YKsoDHjmAOZmVmz8KTBZmZW13pgROIayYHMzKwZNPDs9w5kZmZNIP0gujEjmQOZmVmzqO2YnT7jQGZm1iTcIjMzs/rVwNfIPEVVg5N0jKSQtGNf18XM+lKvzbXY6xzIGt9JpDt0n9jXFTGzPhZR3aPOOJA1MEkDgf2AM8iBTNJakn4kabakWyTdKum4vG4vSXfmG91NzLdQMLNGEKC26h71xtfIGtvRwG0R8aSkv0raE9gGGAHsRrrz6mPA1ZL6Az8AjoqIhZJOAL4JnN4nNTeznleHra1qOJA1tpOA/87L1+fn/YFfRUQb8KKkO/L6HYBdSXdqBWgBFrRXcL41+ZkAAzdbrxZ1N7Oe1phxzIGsUUkaAhwI7CopSIEpgN+0twkwOyL2rab8iBgHjAPYdOchDfrvYdZY1FaH/YZV8DWyxnUc8LOI2CoiRkTEcOAZYBFwbL5WNgzYP+d/AthE0r4AkvpL2qUvKm5mNRCkH0RX86gzbpE1rpNY9c6qNwI7AfNItxV/EngAeC0iluZBH5dJGkT6bPw3MLvXamxmNSPCP4i2+hIR+1dIuwzSaMaIeDN3P04FZuX1M4AP9GI1zaw3NWggc9dic7pF0gzgbuDiiHixj+tjZr2hF35HJmljSbdLeir/HVwhzw6SZhQer0s6L6+7SNILhXWHd7ZPt8iaUKXWmpk1uNI1sto7H5gcEZdIOj8//8pKVYl4AhgFIKkFeIGVB6J9LyK+W+0O3SIzM2sSamur6tFNRwHX5OVrSL9n7ciHgD9HxHOru0MHMjOzplBlt2L3r6MNi4gFAPnvpp3kPxH4RVnaOZJmSrq6UtdkOQcyM7NmEHQlkA2VNL3wOLNYlKQ/SHq0wuOorlRJ0trAkcCvCslXANuSuh4XAP/ZWTm+RmZm1iyq7zVcFBGj21sZEQe1t07SS5I2j4gFeb7WlzvYz2HAQxHxUqHsd5Yl/QS4pbPKukVmZtYkFFHVo5smAKfm5VOBmzvIexJl3Yplk5UfQ/rNa4fcIrNue/3tAdw+d4ealb/HFi/UrGyAabO2rWn5AAxbXtvy167tcLSNhrxZ0/Jff2PdmpYPsNM9n6xp+Y/t9/OalT1m4Cs9U1Dv/I7sEuCXks4A/gIcDyDpXcBVEXF4fr4ecDDwmbLtvyNpFKkz9NkK61fhQGZm1gwioLX24+8j4hXSSMTy9PnA4YXnbwFDKuTr8jcOBzIzs2bRoDN7OJCZmTULBzIzM6tbAbQ5kJmZWd0KiDq8R0sVHMjMzJpB0CuDPfqCA5mZWbPwNTIzM6trDmRWTyRdBLzZlVshmFkj65EJgddIDmRmZs0ggO7fomWN5LkWG4SkU/JtDx6R9POydZ+WNC2vuzFPDYOk4/OM1Y9Iuiun7SJpar4z60xJI/vieMysBnrnNi69zoGsAUjaBbgAODAi9gA+X5blpojYO697DDgjp18IfDinH5nTzgK+HxGjgNHAvHb2eWbpFg+try/u2QMysxrIU1RV86gzDmSN4UDg1xGxCCAi/lq2fldJd0uaBZwM7JLT7wHGS/o00JLT7gO+JukrwFYRsaTSDiNiXESMjojRLRuu39PHY2Y9LSCirapHvXEgawwi9YC3ZzxwTkTsBnwDGAAQEWcBXweGAzMkDYmI60itsyXAREkH1rLiZtaL2qK6R51xIGsMk4GPSxoCIGnjsvUbAAsk9Se1yMj5to2IByLiQmARMFzSNsDciLiMdF+h3XvlCMys9hr0GplHLTaAiJgt6ZvAnZJagYdJ9/Ep+RfgAeA5YBYpsAFcmgdziBQMHwHOBz4haRnwIvBvvXIQZlZbEQ07atGBrEFExDXANe2suwK4okL6xypk/1Z+mFmjqcPWVjUcyMzMmkIQra19XYmacCAzM2sGvo2LmZnVvTocWl8Nj1o0M2sCAURbVPXojjxj0GxJbZJGd5DvUElPSHpa0vmF9I0l3S7pqfx3cGf7dCAzM2sGkW+sWc2jex4FPgbc1V4GSS3A5cBhwM7ASZJ2zqvPByZHxEjSaOrzK5eyggOZmVmTiNbWqh7d2kfEYxHxRCfZxgBPR8TciFgKXA8cldcdxYoR2NcAR3e2T18js257e+78RU9//MLnurDJUNIPsKvydNer1KXyV0PTlf+XXthHvZff0nmW7pS/VdeKX9UbvDrxD/HroVVmHyBpeuH5uIgY1906FGwBPF94Pg/YJy8Pi4gFABGxQNKmnRXmQGbdFhGbdCW/pOkR0W7feXe5/L4tvzf24fK7LiIO7amyJP0B2KzCqgsi4uZqiqiQttoX5xzIzMysSyLioG4WMY80x2vJlsD8vPySpM1za2xz4OXOCvM1MjMz623TgJGStpa0NnAiaW5X8t9T8/KpQKctPAcy6ws92dfu8te88ntjHy5/DSXpGEnzgH2B30mamNPfJelWgIhYDpwDTCTdI/GXETE7F3EJcLCkp4CD8/OO9xkNOveWmZk1B7fIzMysrjmQmZlZXXMgMzOzuuZAZmZmdc2BzMzM6poDmZmZ1TUHMjMzq2v/H2LuLh0Df6MpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure()\n",
    "ax=fig.add_subplot(111)\n",
    "cax=ax.matshow(correlations, vmin=-1, vmax=1)\n",
    "fig.colorbar(cax)\n",
    "\n",
    "ticks=np.arange(0, 9, 1)\n",
    "names=['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',\n",
    "       'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age', 'class']\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_xticklabels(names)\n",
    "ax.set_yticklabels(names)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ef69c9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "52306e73",
   "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>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  DiabetesPedigreeFunction  \\\n",
       "0              6      148             72             35        0  33.6                     0.627   \n",
       "1              1       85             66             29        0  26.6                     0.351   \n",
       "2              8      183             64              0        0  23.3                     0.672   \n",
       "3              1       89             66             23       94  28.1                     0.167   \n",
       "4              0      137             40             35      168  43.1                     2.288   \n",
       "..           ...      ...            ...            ...      ...   ...                       ...   \n",
       "763           10      101             76             48      180  32.9                     0.171   \n",
       "764            2      122             70             27        0  36.8                     0.340   \n",
       "765            5      121             72             23      112  26.2                     0.245   \n",
       "766            1      126             60              0        0  30.1                     0.349   \n",
       "767            1       93             70             31        0  30.4                     0.315   \n",
       "\n",
       "     Age  class  \n",
       "0     50      1  \n",
       "1     31      0  \n",
       "2     32      1  \n",
       "3     21      0  \n",
       "4     33      1  \n",
       "..   ...    ...  \n",
       "763   63      0  \n",
       "764   27      0  \n",
       "765   30      0  \n",
       "766   47      1  \n",
       "767   23      0  \n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c4ee28f7",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'pandas.tools'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-8f90542b9c4b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtools\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplotting\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'pandas.tools'"
     ]
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "1f1f8d18",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'scatter_matrix' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-55-7dec7b8bc448>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscatter_matrix\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'scatter_matrix' is not defined"
     ]
    }
   ],
   "source": [
    "scatter_matrix(df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05cea84c",
   "metadata": {},
   "source": [
    "# Prepare Your Data For Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfd68446",
   "metadata": {},
   "outputs": [],
   "source": [
    "Objectives\n",
    "\"\"\"\"\n",
    "Rescale data.\n",
    "Standardize data.\n",
    "Normalize data.\n",
    "Binarize data.\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd0f5c85",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b704faac",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=[['Ahmed', 35], ['Ali', 12], ['Apau', 34]]\n",
    "\n",
    "df2=pd.DataFrame(df, columns=['Name', 'Age'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1e64a476",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ahmed</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ali</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apau</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age\n",
       "0  Ahmed   35\n",
       "1    Ali   12\n",
       "2   Apau   34"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "872d4ac2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ali</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apau</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Age\n",
       "1   Ali   12\n",
       "2  Apau   34"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "2e98f886",
   "metadata": {},
   "outputs": [],
   "source": [
    "myData={'Names':['Ahamed', 'Ali', 'Omar', 'Salma'],\n",
    "       'Age':[12, 34, 15, 17]}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0b0233f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "myData2=pd.DataFrame(myData, index=[100, 101, 102, 103])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a46d3b9c",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Ahamed</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Ali</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>Omar</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>Salma</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names  Age\n",
       "100  Ahamed   12\n",
       "101     Ali   34\n",
       "102    Omar   15\n",
       "103   Salma   17"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "5cb0b60e",
   "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>Names</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>Ahamed</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Ali</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Names  Age\n",
       "100  Ahamed   12\n",
       "101     Ali   34"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "57c5f8f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100    Ahamed\n",
       "101       Ali\n",
       "102      Omar\n",
       "103     Salma\n",
       "Name: Names, dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myData2['Names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcfe052d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8d2ad09d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[60.44, 75.2, 113.0, 53.6]\n",
      "[15.799999999999999, 24.000000000000004, 45.0, 12.000000000000002]\n"
     ]
    }
   ],
   "source": [
    "def fahrenheit(T):\n",
    "    \n",
    "    return ((float(9)/5) * T + 32)\n",
    "\n",
    "\n",
    "def celsius(T):\n",
    "    return (float(5)/9 ) * (T - 32)\n",
    "\n",
    "\n",
    "Temp=(15.8, 24, 45, 12)\n",
    "\n",
    "F=list(map(fahrenheit, Temp))\n",
    "\n",
    "C=list(map(celsius, F))\n",
    "\n",
    "\n",
    "print(F)\n",
    "\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5e25001",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "48fc981d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "fib=[1, 2, 3, 4, 5, 6, 7, 8]\n",
    "result=filter(lambda x: x % 2 == 0, fib)\n",
    "\n",
    "for x in result:\n",
    "    print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b911e0b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "f=lambda a, b: a if (a > b) else b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3268c0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "reduce(f, [])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "a4fb5057",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     Lion\n",
       "1    Tiger\n",
       "2     Bear\n",
       "3      Dog\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals=['Lion', 'Tiger', 'Bear', 'Dog']\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "a4690775",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Bear'"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "300f7454",
   "metadata": {},
   "outputs": [],
   "source": [
    "s=pd.Series([70,90,65,25, 99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "98c96d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    70\n",
       "1    90\n",
       "2    65\n",
       "3    25\n",
       "4    99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c1e8fb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "447ce9c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "349\n"
     ]
    }
   ],
   "source": [
    "total=0\n",
    "\n",
    "for val in s:\n",
    "    \n",
    "    total +=val\n",
    "    \n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c0c673bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "total=np.sum(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "d547ddc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "349"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e5665faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "115cc41b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=sns.load_dataset('tips')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "171cf043",
   "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"
    }
   ],
   "source": [
    "sns.regplot(x='total_bill', y='tip', data=df)\n",
    "\n",
    "plt.xlabel('Total Bill')\n",
    "plt.ylabel('Bill Tips')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85eded77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "26d173a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=sns.load_dataset('iris')\n",
    "sns.pairplot(df, kind='scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35a21b77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "a2d4bcd1",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {\n",
    "'Name': pd.Series(['Ahmed','Omar','Ali','Salwa','Majid',\n",
    "'Othman','Gameel','Ziad','Ahlam','Zahrah',\n",
    "'Ayman','Alaa']),\n",
    "'Age': pd.Series([34,26,25,27,30,54,23,43,40,30,28,46]),\n",
    "'Height':pd.Series([114.23,173.24,153.98,172.0,153.20,164.6,\n",
    "183.8,163.78,172.0,164.8 ])}\n",
    "\n",
    "\n",
    "df=pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "31799658",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age        9.740574\n",
      "Height    18.977090\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(df.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b4195c8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8c228a5b",
   "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>Age</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>10.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.833333</td>\n",
       "      <td>161.56300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.740574</td>\n",
       "      <td>18.97709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>114.23000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>26.750000</td>\n",
       "      <td>156.43000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>164.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40.750000</td>\n",
       "      <td>172.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>54.000000</td>\n",
       "      <td>183.80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age     Height\n",
       "count  12.000000   10.00000\n",
       "mean   33.833333  161.56300\n",
       "std     9.740574   18.97709\n",
       "min    23.000000  114.23000\n",
       "25%    26.750000  156.43000\n",
       "50%    30.000000  164.70000\n",
       "75%    40.750000  172.00000\n",
       "max    54.000000  183.80000"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5adef70",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "9bc3e9f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.rand(20, 5), \n",
    "               columns=['Jan', 'Feb', 'Mar', 'Apr', 'May'])\n",
    "\n",
    "\n",
    "\n",
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e86c8be9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "f9f5b936",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Temperature over two months'}, xlabel='Feb', ylabel='Jan'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.DataFrame(np.random.rand(20, 5), columns=['Jan', 'Feb', \n",
    "                                               'Mar', 'Apr', 'May'])\n",
    "\n",
    "\n",
    "\n",
    "df.plot.scatter(x='Feb', y='Jan', title='Temperature over two months')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32056652",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Write a Python script to create a data frame for the\n",
    "following table:\n",
    "\n",
    "A. Create a bar plot of the sales volume\n",
    "B. Create a pie chart of item sales\n",
    "Name   Mobile_Sales TV_Sales\n",
    "Ahmed 2540 2200\n",
    "Omar  1370 1900\n",
    "Ali   1320 2150\n",
    "Ziad   2000 1850\n",
    "Salwa   2100 1770\n",
    "Lila   2150 2000\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c9d79c75",
   "metadata": {},
   "outputs": [],
   "source": [
    "salesMen=['Ahmed', 'Omar', 'Ali', 'Ziad', 'Salwa', 'Lila']\n",
    "\n",
    "\n",
    "mobile_Sales=[2540, 1370, 1320, 2000, 2100, 2150]\n",
    "\n",
    "\n",
    "TV_Sales=[2200, 1900, 2150, 1850, 1770, 2000]\n",
    "\n",
    "\n",
    "df=pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "7e56e4e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Name']=salesMen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e2ec18a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Mobile_Sales']=mobile_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "91dcbd5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TV_Sales']=TV_Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e0696297",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('Name', drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5dda32c7",
   "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>Mobile_Sales</th>\n",
       "      <th>TV_Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ahmed</th>\n",
       "      <td>2540</td>\n",
       "      <td>2200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Omar</th>\n",
       "      <td>1370</td>\n",
       "      <td>1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ali</th>\n",
       "      <td>1320</td>\n",
       "      <td>2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ziad</th>\n",
       "      <td>2000</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Salwa</th>\n",
       "      <td>2100</td>\n",
       "      <td>1770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lila</th>\n",
       "      <td>2150</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mobile_Sales  TV_Sales\n",
       "Name                         \n",
       "Ahmed          2540      2200\n",
       "Omar           1370      1900\n",
       "Ali            1320      2150\n",
       "Ziad           2000      1850\n",
       "Salwa          2100      1770\n",
       "Lila           2150      2000"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2ada4a5",
   "metadata": {},
   "source": [
    "# Bar graph of sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "deca058b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(figsize=(20, 10), rot=0).legend(bbox_to_anchor=(1.2, 1))\n",
    "\n",
    "\n",
    "plt.xlabel('Salesmen')\n",
    "plt.ylabel('Sales')\n",
    "\n",
    "\n",
    "plt.title('Sales Volume for two salesmen in \\nJanuary and April 2017')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea0d10fd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d9073794",
   "metadata": {},
   "source": [
    "# Pie plot of Sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f0fec3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:ylabel='Mobile_Sales'>,\n",
       "       <AxesSubplot:ylabel='TV_Sales'>], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "df.plot.pie(subplots=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "205154e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c4b2ad16",
   "metadata": {},
   "source": [
    "# Box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0be178ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "3eda2c99",
   "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>Mobile_Sales</th>\n",
       "      <th>TV_Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1913.333333</td>\n",
       "      <td>1978.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>477.144283</td>\n",
       "      <td>170.342792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1320.000000</td>\n",
       "      <td>1770.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1527.500000</td>\n",
       "      <td>1862.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2050.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2137.500000</td>\n",
       "      <td>2112.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2540.000000</td>\n",
       "      <td>2200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mobile_Sales     TV_Sales\n",
       "count      6.000000     6.000000\n",
       "mean    1913.333333  1978.333333\n",
       "std      477.144283   170.342792\n",
       "min     1320.000000  1770.000000\n",
       "25%     1527.500000  1862.500000\n",
       "50%     2050.000000  1950.000000\n",
       "75%     2137.500000  2112.500000\n",
       "max     2540.000000  2200.000000"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6c3574",
   "metadata": {},
   "source": [
    "# Area plot of sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f9c69c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15f9fd1ee20>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.area(figsize=(6, 4)).legend(bbox_to_anchor=(1.3, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9d7d02",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "df9912c9",
   "metadata": {},
   "source": [
    "# E. Create a stacked bar plot of item sales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "59ad7ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x15f9f5b3040>"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.bar(stacked=True, figsize=(20, 10)).legend(bbox_to_anchor=(1.1, 1))\n",
    "            \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a83da3d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "0a2cd78a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame({'A':[1, 2, 3], 'B':[4, 5, 6]})\n",
    "df=df.assign(C=lambda x : x['A'] + x['B'])\n",
    "\n",
    "df=df.assign(D=lambda x : x['A'] + x['C'])"
   ]
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   ]
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    {
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       "A    4\n",
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      ]
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    "df.iloc[1]"
   ]
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    {
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       "0    4\n",
       "1    5\n",
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      ]
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     "execution_count": 123,
     "metadata": {},
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    "df['B']"
   ]
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   "source": [
    "df.T.dot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "016ecccc",
   "metadata": {},
   "outputs": [
    {
     "ename": "UnicodeDecodeError",
     "evalue": "'charmap' codec can't decode byte 0x9d in position 166: character maps to <undefined>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-127-6d9c8e1607fe>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mmyFile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'text.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mfile\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmyFile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mwords\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfile\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcounts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\conda\\lib\\encodings\\cp1252.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, input, final)\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mIncrementalDecoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIncrementalDecoder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 23\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcharmap_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdecoding_table\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mStreamWriter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mCodec\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcodecs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mStreamWriter\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mUnicodeDecodeError\u001b[0m: 'charmap' codec can't decode byte 0x9d in position 166: character maps to <undefined>"
     ]
    }
   ],
   "source": [
    "myFile=open('text.txt')\n",
    "file=myFile.read()\n",
    "words=file.split()\n",
    "\n",
    "counts=dict()\n",
    "\n",
    "for word in words:\n",
    "    counts[word]=counts.get(word, 0)+1\n",
    "    \n",
    "\n",
    "print(counts)\n",
    "\n",
    "print('++++++++++++++++++++++++++++++++++++++++++\\n')\n",
    "\n",
    "\n",
    "\n",
    "bigCount=None\n",
    "bigWord=None\n",
    "\n",
    "\n",
    "for word, count in counts.items():\n",
    "    if bigCount is None or count > bigCount:\n",
    "        bigWord=word\n",
    "        bigCount=count\n",
    "        \n",
    "\n",
    "        \n",
    "print('\\n\\t\\tBigWord and BigCount')\n",
    "print(bigWord, bigCount)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3da39890",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "092f4ff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name of the file:  text.txt\n",
      "Closed or not :  False\n",
      "Opening mode :  r\n",
      "Closed or not :  False\n"
     ]
    }
   ],
   "source": [
    "myFile=open('text.txt', 'r')\n",
    "print('Name of the file: ', myFile.name)\n",
    "\n",
    "print('Closed or not : ', myFile.closed)\n",
    "\n",
    "print('Opening mode : ', myFile.mode)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "print('Closed or not : ', myFile.closed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "07ba3770",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df=pd.read_csv('users.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "54d5e6c9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Country Internet Penetration Facebook Penetration</th>\n",
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       "      <td>China 42.3% 0.1%</td>\n",
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       "      <td>Colombia 49.0% 36.3%</td>\n",
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       "      <th>5</th>\n",
       "      <td>Egypt 44.1% 15.1%</td>\n",
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       "      <th>6</th>\n",
       "      <td>France 83.0% 39.0%</td>\n",
       "    </tr>\n",
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       "      <td>Germany 84.0% 30.9%</td>\n",
       "    </tr>\n",
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       "      <th>8</th>\n",
       "      <td>Hong Kong 72.8% 56.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>India 12.6% 5.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Indonesia 15.4% 20.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Italy 58.0% 38.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Japan 79.1% 13.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Malaysia 65.8% 46.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mexico 38.4% 31.8%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Netherlands 93.0% 45.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Peru 38.2% 31.2%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Philippines 36.2% 30.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Poland 65.0% 25.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Russia 53.3% 5.6%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Saudi Arabia 54.0% 20.7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>South Africa 41.0% 12.3%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Spain 72.0% 38.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Sweden 94.0% 52.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Thailand 26.5% 26.5%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Turkey 45.1% 43.4%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>United Kingdom 87.0% 52.1%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>United States 81.0% 52.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Venezuela 44.1% 32.6%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Internet Penetration Facebook Penetration\n",
       "0                                 Brazil 49.9% 29.5%\n",
       "1                                 Canada 86.8% 51.9%\n",
       "2                                  Chile 61.4% 55.5%\n",
       "3                                   China 42.3% 0.1%\n",
       "4                               Colombia 49.0% 36.3%\n",
       "5                                  Egypt 44.1% 15.1%\n",
       "6                                 France 83.0% 39.0%\n",
       "7                                Germany 84.0% 30.9%\n",
       "8                              Hong Kong 72.8% 56.4%\n",
       "9                                   India 12.6% 5.1%\n",
       "10                             Indonesia 15.4% 20.7%\n",
       "11                                 Italy 58.0% 38.1%\n",
       "12                                 Japan 79.1% 13.5%\n",
       "13                              Malaysia 65.8% 46.5%\n",
       "14                                Mexico 38.4% 31.8%\n",
       "15                           Netherlands 93.0% 45.1%\n",
       "16                                  Peru 38.2% 31.2%\n",
       "17                           Philippines 36.2% 30.9%\n",
       "18                                Poland 65.0% 25.6%\n",
       "19                                 Russia 53.3% 5.6%\n",
       "20                          Saudi Arabia 54.0% 20.7%\n",
       "21                          South Africa 41.0% 12.3%\n",
       "22                                 Spain 72.0% 38.1%\n",
       "23                                Sweden 94.0% 52.0%\n",
       "24                              Thailand 26.5% 26.5%\n",
       "25                                Turkey 45.1% 43.4%\n",
       "26                        United Kingdom 87.0% 52.1%\n",
       "27                         United States 81.0% 52.9%\n",
       "28                             Venezuela 44.1% 32.6%"
      ]
     },
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