gcc .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4dd92746",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d5abfdd",
   "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>CITY NAME</th>\n",
       "      <th>ZONE Number</th>\n",
       "      <th>Zone Name</th>\n",
       "      <th>WARD NAME</th>\n",
       "      <th>NUMBER OF PUBLIC PRIMARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC SECONDARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>13</td>\n",
       "      <td>ADYAR</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>13</td>\n",
       "      <td>ADYAR</td>\n",
       "      <td>179</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>13</td>\n",
       "      <td>ADYAR</td>\n",
       "      <td>180</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>13</td>\n",
       "      <td>ADYAR</td>\n",
       "      <td>181</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>13</td>\n",
       "      <td>ADYAR</td>\n",
       "      <td>182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   CITY NAME  ZONE Number   Zone Name  WARD NAME  \\\n",
       "0    Chennai            4  TONDIARPET         34   \n",
       "1    Chennai            4  TONDIARPET         35   \n",
       "2    Chennai            4  TONDIARPET         36   \n",
       "3    Chennai            4  TONDIARPET         37   \n",
       "4    Chennai            4  TONDIARPET         38   \n",
       "..       ...          ...         ...        ...   \n",
       "95   Chennai           13       ADYAR        178   \n",
       "96   Chennai           13       ADYAR        179   \n",
       "97   Chennai           13       ADYAR        180   \n",
       "98   Chennai           13       ADYAR        181   \n",
       "99   Chennai           13       ADYAR        182   \n",
       "\n",
       "    NUMBER OF PUBLIC PRIMARY SCHOOLS  NUMBER OF PUBLIC SECONDARY SCHOOLS  \\\n",
       "0                                  1                                   0   \n",
       "1                                  2                                   3   \n",
       "2                                  1                                   1   \n",
       "3                                  5                                   0   \n",
       "4                                  1                                   0   \n",
       "..                               ...                                 ...   \n",
       "95                                 1                                   0   \n",
       "96                                 1                                   0   \n",
       "97                                 1                                   0   \n",
       "98                                 2                                   1   \n",
       "99                                 1                                   0   \n",
       "\n",
       "    NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS  \n",
       "0                                         0.0  \n",
       "1                                         0.0  \n",
       "2                                         0.0  \n",
       "3                                         0.0  \n",
       "4                                         1.0  \n",
       "..                                        ...  \n",
       "95                                        1.0  \n",
       "96                                        0.0  \n",
       "97                                        1.0  \n",
       "98                                        0.0  \n",
       "99                                        1.0  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc = pd.read_csv(\"Gcc_Schools.csv\")\n",
    "gcc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "75bf9cba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of    CITY NAME  ZONE Number   Zone Name  WARD NAME  \\\n",
       "0    Chennai            4  TONDIARPET         34   \n",
       "1    Chennai            4  TONDIARPET         35   \n",
       "2    Chennai            4  TONDIARPET         36   \n",
       "3    Chennai            4  TONDIARPET         37   \n",
       "4    Chennai            4  TONDIARPET         38   \n",
       "..       ...          ...         ...        ...   \n",
       "95   Chennai           13       ADYAR        178   \n",
       "96   Chennai           13       ADYAR        179   \n",
       "97   Chennai           13       ADYAR        180   \n",
       "98   Chennai           13       ADYAR        181   \n",
       "99   Chennai           13       ADYAR        182   \n",
       "\n",
       "    NUMBER OF PUBLIC PRIMARY SCHOOLS  NUMBER OF PUBLIC SECONDARY SCHOOLS  \\\n",
       "0                                  1                                   0   \n",
       "1                                  2                                   3   \n",
       "2                                  1                                   1   \n",
       "3                                  5                                   0   \n",
       "4                                  1                                   0   \n",
       "..                               ...                                 ...   \n",
       "95                                 1                                   0   \n",
       "96                                 1                                   0   \n",
       "97                                 1                                   0   \n",
       "98                                 2                                   1   \n",
       "99                                 1                                   0   \n",
       "\n",
       "    NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS  \n",
       "0                                         0.0  \n",
       "1                                         0.0  \n",
       "2                                         0.0  \n",
       "3                                         0.0  \n",
       "4                                         1.0  \n",
       "..                                        ...  \n",
       "95                                        1.0  \n",
       "96                                        0.0  \n",
       "97                                        1.0  \n",
       "98                                        0.0  \n",
       "99                                        1.0  \n",
       "\n",
       "[100 rows x 7 columns]>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "98631c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITY NAME                                     object\n",
       "ZONE Number                                    int64\n",
       "Zone Name                                     object\n",
       "WARD NAME                                      int64\n",
       "NUMBER OF PUBLIC PRIMARY SCHOOLS               int64\n",
       "NUMBER OF PUBLIC SECONDARY SCHOOLS             int64\n",
       "NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "159837b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CITY NAME', 'ZONE Number', 'Zone Name', 'WARD NAME',\n",
       "       'NUMBER OF PUBLIC PRIMARY SCHOOLS',\n",
       "       'NUMBER OF PUBLIC SECONDARY SCHOOLS',\n",
       "       'NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe92fa7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TONDIARPET', 'ROYAPURAM', 'THIRU-VI-KA NAGAR', 'ANNA NAGAR',\n",
       "       'TEYNAMPET', 'KODAMBAKKAM', 'ADYAR'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc[\"Zone Name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dad97efb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc[\"Zone Name\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fae5eb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TEYNAMPET            16\n",
       "TONDIARPET           15\n",
       "ROYAPURAM            15\n",
       "THIRU-VI-KA NAGAR    15\n",
       "KODAMBAKKAM          14\n",
       "ADYAR                13\n",
       "ANNA NAGAR           12\n",
       "Name: Zone Name, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc[\"Zone Name\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63e7508e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "      ...  \n",
       "95    False\n",
       "96    False\n",
       "97    False\n",
       "98    False\n",
       "99    False\n",
       "Length: 100, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee430b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITY NAME                                    0\n",
       "ZONE Number                                  0\n",
       "Zone Name                                    0\n",
       "WARD NAME                                    0\n",
       "NUMBER OF PUBLIC PRIMARY SCHOOLS             0\n",
       "NUMBER OF PUBLIC SECONDARY SCHOOLS           0\n",
       "NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f800f842",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.0\n",
       "1     0.0\n",
       "2     0.0\n",
       "3     0.0\n",
       "4     1.0\n",
       "     ... \n",
       "95    1.0\n",
       "96    0.0\n",
       "97    1.0\n",
       "98    0.0\n",
       "99    1.0\n",
       "Name: NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS, Length: 100, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phsc = gcc[\"NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS\"]\n",
    "phsc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d72d8ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phsc.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c1bf6156",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phsc.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dba72c21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    70\n",
       "1.0    26\n",
       "2.0     3\n",
       "Name: NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phsc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5cbf78f2",
   "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>ZONE Number</th>\n",
       "      <th>WARD NAME</th>\n",
       "      <th>NUMBER OF PUBLIC PRIMARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC SECONDARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.740000</td>\n",
       "      <td>97.910000</td>\n",
       "      <td>1.180000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>0.323232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.887171</td>\n",
       "      <td>44.100916</td>\n",
       "      <td>1.095261</td>\n",
       "      <td>1.159066</td>\n",
       "      <td>0.531234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>100.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>128.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ZONE Number   WARD NAME  NUMBER OF PUBLIC PRIMARY SCHOOLS  \\\n",
       "count   100.000000  100.000000                        100.000000   \n",
       "mean      7.740000   97.910000                          1.180000   \n",
       "std       2.887171   44.100916                          1.095261   \n",
       "min       4.000000   34.000000                          0.000000   \n",
       "25%       5.000000   58.750000                          0.000000   \n",
       "50%       8.000000  100.500000                          1.000000   \n",
       "75%      10.000000  128.500000                          2.000000   \n",
       "max      13.000000  182.000000                          5.000000   \n",
       "\n",
       "       NUMBER OF PUBLIC SECONDARY SCHOOLS  \\\n",
       "count                          100.000000   \n",
       "mean                             1.300000   \n",
       "std                              1.159066   \n",
       "min                              0.000000   \n",
       "25%                              0.000000   \n",
       "50%                              1.000000   \n",
       "75%                              2.000000   \n",
       "max                              5.000000   \n",
       "\n",
       "       NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS  \n",
       "count                                  99.000000  \n",
       "mean                                    0.323232  \n",
       "std                                     0.531234  \n",
       "min                                     0.000000  \n",
       "25%                                     0.000000  \n",
       "50%                                     0.000000  \n",
       "75%                                     1.000000  \n",
       "max                                     2.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2681bf8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "gcc1 = gcc.fillna(value = 0.0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c74eb7f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CITY NAME                                    0\n",
       "ZONE Number                                  0\n",
       "Zone Name                                    0\n",
       "WARD NAME                                    0\n",
       "NUMBER OF PUBLIC PRIMARY SCHOOLS             0\n",
       "NUMBER OF PUBLIC SECONDARY SCHOOLS           0\n",
       "NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3806e011",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.0\n",
       "1     0.0\n",
       "2     0.0\n",
       "3     0.0\n",
       "4     1.0\n",
       "     ... \n",
       "95    1.0\n",
       "96    0.0\n",
       "97    1.0\n",
       "98    0.0\n",
       "99    1.0\n",
       "Name: NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS, Length: 100, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillphss = gcc1[\"NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS\"]\n",
    "fillphss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "68db1354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillphss.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c8ba2970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    71\n",
       "1.0    26\n",
       "2.0     3\n",
       "Name: NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fillphss.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f5504c5e",
   "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>CITY NAME</th>\n",
       "      <th>ZONE Number</th>\n",
       "      <th>Zone Name</th>\n",
       "      <th>WARD NAME</th>\n",
       "      <th>NUMBER OF PUBLIC PRIMARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC SECONDARY SCHOOLS</th>\n",
       "      <th>NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>35</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>37</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chennai</td>\n",
       "      <td>4</td>\n",
       "      <td>TONDIARPET</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  CITY NAME ZONE Number   Zone Name WARD NAME  \\\n",
       "0   Chennai           4  TONDIARPET        34   \n",
       "1   Chennai           4  TONDIARPET        35   \n",
       "2   Chennai           4  TONDIARPET        36   \n",
       "3   Chennai           4  TONDIARPET        37   \n",
       "4   Chennai           4  TONDIARPET        38   \n",
       "\n",
       "  NUMBER OF PUBLIC PRIMARY SCHOOLS NUMBER OF PUBLIC SECONDARY SCHOOLS  \\\n",
       "0                                1                                  0   \n",
       "1                                2                                  3   \n",
       "2                                1                                  1   \n",
       "3                                5                                  0   \n",
       "4                                1                                  0   \n",
       "\n",
       "  NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS  \n",
       "0                                       0.0  \n",
       "1                                       0.0  \n",
       "2                                       0.0  \n",
       "3                                       0.0  \n",
       "4                                       1.0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcc1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9dc088c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['TEYNAMPET', 'TONDIARPET', 'ROYAPURAM', 'THIRU-VI-KA NAGAR',\n",
       "       'KODAMBAKKAM', 'ADYAR', 'ANNA NAGAR'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zones = gcc1[\"Zone Name\"].value_counts().index\n",
    "zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "201d28e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([43, 28, 17,  9,  2,  1], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pps = gcc1[\"NUMBER OF PUBLIC PRIMARY SCHOOLS\"].value_counts().values\n",
    "pps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6988d385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38, 27, 18, 14,  2,  1], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pss = gcc1[\"NUMBER OF PUBLIC SECONDARY SCHOOLS\"].value_counts().values\n",
    "pss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7bb97f44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([71, 26,  3], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hss = gcc1[\"NUMBER OF PUBLIC HIGHER SECONDARY SCHOOLS\"].value_counts().values\n",
    "hss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4e17431e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x22022a30370>,\n",
       "  <matplotlib.patches.Wedge at 0x22022a30730>,\n",
       "  <matplotlib.patches.Wedge at 0x22022a30c10>],\n",
       " [Text(-0.6741977004263886, 0.8691705590617815, 'TEYNAMPET'),\n",
       "  Text(0.5894093214328248, -0.9287608151769202, 'TONDIARPET'),\n",
       "  Text(1.0951181406855968, -0.10351936022465487, 'ROYAPURAM')])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(hss,labels = zones[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce85e044",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x220227fb5e0>,\n",
       "  <matplotlib.patches.Wedge at 0x220227fba30>,\n",
       "  <matplotlib.patches.Wedge at 0x220227fbf10>,\n",
       "  <matplotlib.patches.Wedge at 0x220228183d0>,\n",
       "  <matplotlib.patches.Wedge at 0x220228188b0>,\n",
       "  <matplotlib.patches.Wedge at 0x22022818d90>],\n",
       " [Text(0.4049370232742902, 1.0227541284110062, 'TEYNAMPET'),\n",
       "  Text(-1.0951181606757017, -0.10351914875166021, 'TONDIARPET'),\n",
       "  Text(-0.06906950569907265, -1.0978294054098232, 'ROYAPURAM'),\n",
       "  Text(0.8899187482945414, -0.6465637025335375, 'THIRU-VI-KA NAGAR'),\n",
       "  Text(1.0913261831276604, -0.1378664644502508, 'KODAMBAKKAM'),\n",
       "  Text(1.0994572192571674, -0.03455174414261459, 'ADYAR')])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JyBIReU1EJiXoPaY166PsHQveJBgQWrf7Nz/42VMHVg8fsq54TO36wuEtDf0G5jQHpZ9qfYtG6lW1AbQhiG4JoVvy0KZ+aEt/ITIANM/1e0hnTZKjMC3e1WcA84HpQNhf9q6q7gsgIrsBD4tIALgYWCAi/wDWAbcB31PVZhH5IvAWcIaIXKHaqkvpbFV9XUS+DtwAfL537zD9WfD2jgVvginoGaNHrKgds/W4LyysWT1iQ80h0deaQv03bCjefcW6QXt+Vlc8MdSQP2RYJJCzGyKt/h1Um7egjZ9ppGGTav1mIvWNqpu3aqS+RbU+QqQe1cYA2hhS3ZqLNveD5gEQKQCKgJxUv+9UytWmuNYTkQK86YKOAh5ne/Buo6rviciPgJtU9W4RuRG4HngNWKqq8/1VZwCzge8ChwAL2jnkAuDSbr2ZDGXB2zsWvAl2bcmgeZ+GQkcC3HtUYOXMZyM7RV/Laa4vHrp26b5D1y7dtn5LINT4WeGub9UOLlu3vngP3TxgRElLsN+uBAqGSqBgaE9qUG1qiAnuBj+4t/jBrduDe0uOF9xN/aBlAEQKgYGk//+reLtxTgH+rapvi0itiOwH1Laz3hvABP/7O4GZwFTgAAARyQeOAb4NFOOFcHvBewLwaJy1ZbR0/wVJd9ZHnkCL83KrHygs2NbCrThQDj1jHm/mb2XPjrYJRpr7Dap7Z89Bde9sW6ZIy6YBo96tHVz2yfpB45s3FuxU2JRTsCsig+KpQyQnH8nJl0Dh8J68D9Wtm4k0blT1glsj9Y3o5iaN1DdpZDNoA6qNQXRLSHVrHtqcDy39Y4I72X/Q452WaQbwa//7B/znt7eznkS/UdWIiPwOOEBV1/mLTwSeV9V6EXkIuFpELlbV6EnUe0VkAN773q97byUzWfD2zmeuC+gr6kU2nzdyeB4i2weliMhvTg5svfwf3Zu4WNBg4eaPxhZu/mjsLiuf2ba8od/gj9cXj/+wdnBZ/WcDS/O35BaP0UBwdMLexLaycwcQzB0gDOz2tl7f59aNGmnciNZv1khDg+rmRiL1TV7/9uYI2iB+cOeoNuWhTfkQiba4C+m6QbCp6/cgJcDRwOdERPFCUYHftrP6vkDsXeci/iNqBnC4iNT4z0vwui+i/zhnA0uAWXjBflpX9WW6ToPX/+E/6z8dAbQAa/znJwO3Anvi/UM/AVyqqltFZCrwPHCyqv7T39cTwI2qOldE5uINt92CN/rrGeAqVd3gr7tJVQti6rgYuA4Yrqp1/rKpwGPAe3jXJT6hqpf4r52L10n/kb//W1T1D22WR80E5vjf7wzU+Y+1qnpsZz8fYG0Xr5s4zRw5fFGTyOS2y/87LjBp7cDIwiGfcVBvj5HfWDsq/5MFo0Z9sv1TblNowPr1xeNqageXfbahaGyosd+QEZFAqLRtv3GqiIhAXqEE8wq97ubuUVVFt9SpNm7ygru+QbV+ixfcm1s00qAiuRvi2NXpwJ9V9dsxtb0AjGlTbylwI14WtPd+BgKTgZ1UdYu/7Ot4Ybztr6KqNonIVcC7IlKmqn369qGdBq//UWESgIiEgU2qeqP3y8GrwB2q+iXxfkl/D/yS7Z3jHwJXAv/sYPfRM5m5eKH6GDClg3Vn4HXWnwrcE7N8nqqe6PchLRKRR1T1Jf+1B1X1AhEZBiwTkcdjl7fZf/Q93oMX4P/o6GfShgVvAvx5YOHL1Xm5O4Ru1K9OD5Zc/6eWFknCR/Cc5s2Dhq1dPGjY2sXblrUEchrqBu76Xu3gstoNxeN0c/8RJS3Bfrvh/Z6lNRERpF+R0K/I605tVzzz4M3Aa4HGegi4AhgrIouAfsBG4FZVvbuD/ZwGPBcNXd9jwPUi0urqE1VtEJGbgEuA8+OoMWP1tKvhaKAx+sNW1Ra/VbpCRH7qr7MEyBGRz6vq0x3tyG8hXwYsF5F9VLXVvVpFZCxQgBfoV9A6eKP7aBCRxcAOHxtV9VMReZfk3MLRgreX3g+FVt4wuHivTtcZLmOXj2TeuFUckYqagpGm/MEb3t5r8Ia3ty1TpGVTwZjltYMmrK4dNKF5U8HooqacglJEilNRUwI1ff/Oo7vsIlPVqe0s+w3wmzi2vQf//2ns9zGv1wLRE59T27x2U1f77wt6enJoL+C/sQtU9TPgA2D3mMW/AK7qamd+J/sStp8ZjTUDuB+YB4z3W7CtiHfSZBzwYjuv7YZ31/3l/qIzRWRxzKM3rZhPe7Ft1muG5umjR9Qh0uVn6hu/HByvcfRNJougwcJNK3ffZeXTh++79NYpR7xcPunoFy4oPvSVn344/q37Xh26ZtHcvMbahWjLKlc1ximuxoKIbIr5/osi8o6I7CwiY0TkMf/5uyIy2//UiohMFZE6f3DFWyLyooic2Ga/nQ2k+MD/NB1d9mi0jpiBG4v9wRYvi8j4NvuYLSIf+dcUR5edKyK3+d8HRGSOiPxJPDUiMsR/bX8RWSEi+8Zsu0RE7m9zjHtEpF5ECtscV6P7ikdPW7yC19He6XJVnSciiEg8LRXpYPl04FT/bOnDwFfYfmb1CBFZijeP1CxV/SRmuzPF6zPcAnxbVWv9f9P2uhp66sME7ScrXTR8yEubAoGOupdaWV8owxZMkLmHVe/YEnMpv3HtmNGr1o4Zveqlbcu25hSs21A87v11g8s+qxs4Nrchv2SESqiUmEBwqFuTjorIMXj9t8f523bVxThPVU/0t50EPCoiDaoaPVd0HB0PpNiAd93wfPE+SbS97eq7qjrJ3/e38T4BR0fNBfC6IlcCRwJz27wPwbvULQf4uqpqNONFZG/gH8CZqrrIX1aG1zA9UkQGqOrmmN0tB74E/NU/7lG0Pm/UpZ4G7zLgy7EL/E70nYB3aX33o1/i9fU2d7Qz/x9wIq3PjEZ/IOOAp/0fUi7eybRo8Eb7ePfA+8d6RFUX+68lMmA78hHeH5qO/miYDjzTP3/RC/n53eo6uGNa4MBD3mpZHVB6dJlXquQ2bSoZtmZRybA1i7Ytawnkbq4r2m1F7aAJteuL95D6/sNLWoJ5Y2nTz5kCH8S7ot9g+gPwRVV91w/hrroYt1HVxSLyc+ACtp+k72wgxQN4Da35eH3DD+N9um7PQFr3VR8F/A940D/G3Dbrz8bLpTNVNfaKizK8k+tfU9WFMcvPAv7iv34y3qfuqPuBM4G/4nWVvAR8oYM629XT4H0WmCUi56jqn/3gvAm4x79Wb9uKqvqUiFwDjGpvRyKSgxfOK1V1aZuXZwBhVb0uZv0VItKqv9a/wPs64HJ/m9QI120lXLQa74oPE6faQGDdj4cNGdndFuCWXBnw2CHyxqkLNK2Dtz3ByNYBg9dXf27w+uptyyISaN5UMOad2kFln64fNL5504DRRU05A3aNp+ulF1bEuV4e3kmwqaoaLbrdLkYRadvFGOsN/NawdD2Q4lngD36eTAe+BVwd8/pY/1xOIdAfODjmtWiX5GPAtSKSo7ptiN5ZeI26qaratgH4GPDVmBF2UWfiDV0ej/eHIzZ43wG+5HdxzsAL4G4Fb48++vgfD04FviIi7wBv412UfUUHm/ySNpeh4F00vRTvr9QAvKZ7W9OBR9ose8Rf3tadeB8Ldu2i/LZ9vId1sX5XuvXRzcAZo0e8GxHp0R+rB48MHLY1SJ+YJj6gkdDAjR+MK/3gP4fvu+Q3U454+fJJR79wQdEhr4ZX7vH2A68MWbN4bl7j+tfQSCL7jeP92TUBL9P66oK4uhjbeS1q20AKvCskTpXWl+214LV2zwTyVbWmbe2qOklVxwIX4XVz4PcxfxF41D/X9Cpel0bUG3gn19u7JPEZ4BuxdYjIgcAaVX0f74/BfrLj4JuH8XLoYLzzT90Sd4tXVcNtnq8ETupg3bnENPVV9XFaj26Z2sWxCvyvO4Soqv4o5mnsMRrYflXDCtq/+uGe9pbHvH5uZ3V1YBlwYA+2y0rXDR704mp/SHBPRAISvOuEwLrvVUTGJrKudNK/Yc1O/RvW7DTm4+3/n7fmFK5dX7zH+7WDyzZuKNott7FfyUiV4C496Dde3vUqgDcA4gzgGb8v9lq618UYFTu4oquBFOB1NzxCO/eFaONxIHoJ2wl4Fz1X+p+2+wP1QIX/ejXwE+BvInK8qi6L2c8FeI223+K1xKN1ToipcyDe+76rTZ1vAHP8809dlNuajVzrvdeBc10XkQmW5uW+dd/AgoO7XrNzc/cOHPTV5yKLBzZ4119ng9ymjUOGr/nvkOFrtn/Sbw7mbaobuNuK2sFl6zcUj5P6/sOHtARyx7Ya/bejZZ281orfbXgiME9EVgN/Is4uRth2juZqvBZlXAMp8FqP19H6o317JrO99T4D+Iaq3u/vdwBev3P/mPfysoh8B6gQkSNVNdrXHfG3/4/fHx3GO4G/t6p+5O/vKLyrs+6K2d8HInJlm9rjZsHbe6+7LiATNIjUf33k8JxEnUy66bRgbvjeFpUsPrEZatlSULK+amLJ+u3npCMSaNpYsPPbtYMnfLq+eHzLpoLRxc2h/rviBd+n37/z6E863uOO/KuBTsC7VHMtXhfjb0Xkaryuyidp3cV4hHiDK/rjXW75Q1V9VrxRo10OpPC7MW/soJxoH6/g3ejnG364Hs/21iqqullE5tPmE7mqPiEiQ4F/x15ppapbRORLwAvAauCjaOj6XgT2FJGRbfb3uw7q7JKozbTSO+Gifnj3bOjTtxLsrTNHDZ//Zl5eh6PTeuKW3ze/PHodve2j7/MUtD5/+Acbind/+tjn7/qm63qM3V2r98J1jXTj41s2+uvAwgWJDl2AWacHd1bvOm3TCQEZ0LB6l9GrXrIBP2nCgjcxrLuhAx+EQh/+anBxh7d17I3Vg2XM0lJ5JRn77qMWdr2KSQUL3sSw4G1HMzSfOWrE+mRelzr7lMA+6o14Ml2z4E0TFryJYcHbjh8NG/LSpmBgYjKPsSlfip+ZJEu6XjPrfVRWXZXu95HIGha8ibEU62ts5bn++Yuf79+9IcE9dffnA4c0B+y+GV143nUBZjsL3kQI1zXhXYpigPWBQO3Fw4YMT9VNYZpDknff1EDc9yDIUo+5LsBsZ8GbOG2HNmetM0ePWB5pc81jsj1xkBzakMubqTxmBtkC/Nt1EWY7C97EeYyOx6tnjesHF7+4KhTq9TQ93SYit54UiHf23GzzfFl1lbN7GZsdWfAmSrhuFZDVlzZV5ua+/ZeBhakPXd/rewQmrS20M/ftsG6GNGPBm1hZ293QIFJ/7sjhQUT6uazj+tODJerd5cp4FO+GMiaNWPAmVtYG73kjh/13a0Cc3zGsZoSMfXckL7uuI428XlZd9bHrIkxrFryJFK5bjnd/4axyX2HBgv/l5aXk0rF43ODNz7a56zWzgrV205AFb+JlVav3w1Dwo+tKBiVlSHBPrS+UYa9MkNdc15EmrH83DVnwJt5DrgtIlRZoOWPUyHVJnqqmR+6YFjggIlk/C/SysuqqStdFmB1Z8CZauG4JWXJ1wyXDhszfGAzs7bqO9jTmSsHjB0t112v2ab9xXYBpnwVvcvzadQHJNrd//uJnUjQkuKcemBI4vK/Mz9YDtXiz5Jo0ZMGbHA/RhyfB3BAIrL8whUOCeyoSkOBdxwfWua7DkbvKqqsaXBdh2pfW/3EyVriuGbjddRnJcuaoEW+nekhwT83dJ3DQxnwWu64jxVrow79/fYEFb/L8Hm+m0z7lxsHFL36cE+r1hJWpdNOpwVzNruHcj5RVV9lNg9KYBW+yhOvWA392XUYiLcvNfWeOwyHBPfXmLrLnx4NZ4LqOFJrtugDTOQve5JpNH2lpNYo0zBw5TFwPCe6pX30luJN6M9P2dW+UVVfNd12E6ZwFbzKF66qBp1yXkQjnjRj2+pZAYHfXdfTUJ4Nlp//tItnQ6rVLyDKABW/y/cJ1Ab31YGHBK5X90mdIcE/dckpgb4U613Uk0VvAfa6LMF2z4E22cN18Mni8/Eeh4Me/LBk0wXUdibCpvwx6dh9Z7LqOJLqkrLqqyXURpmsWvKlxORl4q0JvSPCINSpS7LqWRPnTcX12franyqqrnnBdhImPBW8qeH29f3RdRnddNmzI/M+CwX1c15FIzSHJe2BKn5ufrQX4kesiTPwseFPnJ8BnrouI14v5/ZY81T9/sus6kuHxg+XQhhyqXNeRQL8vq65a5roIEz8L3lQJ160Gfuq6jHjUBQIbfjh86FBEgq5rSQoRue2kQKPrMhJkA94fdZNBLHhT6zYy4Ebp00eNqG4RGeW6jmR6bXxg33WF9IV79l5TVl211nURpnsseFPJu4fDD1yX0ZlbBhXP+zAndIjrOlLh+tODgzJ8frZ3gFtdF2G6z4I31cJ1c4G7XJfRnjdzc5b/qajwANd1pMqKEbL7eyMydn42BS6wy8cykwWvGxfhXeyeNrYIjeeMHK6I5LuuJZVu+HJwjwydn+32suqqPjEqMhtZ8LoQrtsMzCCN7h1w/ojhC7cEAuNc15FqtQNl+MLx8rrrOrqpCrjMdRGm5yx4XQnXLQKucF0GwN8LB7y6pF/eka7rcOX2EwP7R4Q1ruuIUxPwVbvJeWaz4HXrZuA/Lgv4OBRcdU3J4D1c1uBaY64U/PNgyZTreq8sq656w3URpncseF0K1ykwE9zMhhuByBmjRnyqIoNcHD+d3D8lcFhTkPdc19GFJ4AbO1tBREpEZLH/+EREPop5Xt9m3XNF5Db/+7CIXOJ/f4+IrPC3WSIix8RsUyMiQ2KeTxWRJ9rst1REPpQ2U0P5+zso9ljt1L8p5vsvisg7IrKz/zwkImtF5LpO3v89/nvO858PEZGaNutcLCKN0mZ2bBE5QUQWiki1X+uD0WN3dnwRmSsib/k/q9dEZFJH9UVZ8LrmDaw4Fwf37b18aMm8uj42JLinIgEJ/fG4QDpfD/sBMLOsuqrT3xNVXaeqk1R1EnAncEvM80g3jnepv81F/n7ipqo1eHMObrujnYhMAApVdWE8+/DD/lbgBFWNDvE+Du+k9BkiIp1s3gKc18nrM4DXgFNjjvc5/3gzVXWC/97vBUpjtuvs+Ger6j7Ab4EbOn93FrzpIVz3L+CWVB5yfn6/pf8e0L9PDgnuqecmBQ7a2I8lrutoRxNwRll1Va2DYy8ARvdgu/uB6THPp/vLuiQiRwB/AKapauws0TPwJhf4AOjsWvNfAxeLSKidfY8FCoCr/P1FXQ5cq6rbupxU9XFVfbGbx4/r52XBmz4uBR5JxYHqAlJ3wfChJX12SHAv3HxaIJRm87Mp8M2y6qpXE7Cv/Jhuh8XAz+PY5gTg0R4c62/AKTHhdybwQBzb5QGPAaeoanV0oXiXOR6D191yP61Ds60PgPnA19p5bYa//TxgvIgM85fvBXTYd96N48f187LgTRfhughwFt4vTFLNGDXizRaRnrRi+rxluwT2WpVe87NdXlZdNSdB+2qIdjv4H6U7u8fDDSLyHvBX4NqY5e39Udphmap+AiwDjvH7PJtUNZ7h8k3Ay8D5bZafCDyvqvXAQ8Cp0nnD4Vq8xkzbjJsOPKCqEeBh4CttN4zpJ387pi+6q+PfKyIf4rWcuxxNaMGbTsJ1jcDJwJvJOsTsQUXzVubkHJqs/fcFvzo9OCZN5me7qay6qsv+wiS5FNgd7yN5bPCvA2JPxg4G1gKIyH/8wIqOzIx2N7TbzSAiO8W0wL/jL44AZwAHikjs5ZYzgGP9E2X/BUqAozoqXlWXA4v9fUWPtzcwDnja3890trdclwH7+duu8/8w/R6vWyKe458N7Io3A8jtHdUVZcGbbrzZiU+AxN+suyo35927igZmzZDgnlpVIjsvcz8/21/wws8Zv1U4GwiIyPH+4rn4H+H9Ft9Xgef99Y/3W9Pf8Nd9CPgiHXQzqOrKmBb4nTHL6/FamGeLyPkiMhCYDOysqqWqWgp8n867GwB+CcRePTEDCEf3oaqjgNEisgtwPXCliJTFrN/ff59xHV9Vm/D+UB3SZj87sOBNR+G6lcAX8G75lxD+kOCWbBsS3FM3u52f7UngvK6uYEgFVVW8eQOjI+WuAXYXkSXAImA5XndEe9tuAF4BVqvqim4etxavAXIV8HXgOVXdErPKY8DJ0cvGOtjHMlr3205nx/MojwDTVbUSuBD4s3852UtAGV4L9rR4j6+qDcBNtA78HYj3czVpKVw0BW+ARYe/XPE6Z+SwFxf165e1o9N64ttPtsw9ZolOTfFhFwDHllVX1Xe5pslY1uJNZ+G6F/D+Sveqv/HhggELLXS774/HBQ5tSe38bG8CJ1ro9n0WvOkuXPcoXj/Zxp5svioYXBUeMnj3hNaUJZpDkvfAkYH3U3S4xXgtXRfX6poUs+DNBOG6Z4EpwOrubBaByBmjR6xWkcHJKazve+wQOawx+fOzPQUcWVZdtSrJxzFpwoI3U3h3MzsM72RGXP5vaMm8DcHgpKTVlA2SPz/b3cC0suqqHn2iMZnJgjeThOveAw7Hu46wUy/l96t8ckD/w5NfVN+3cHxg39oCknHP3p+VVVedV1Zd1ZyEfZs0ZsGbacJ1nwJTgac7WuWzgNR9f/jQQbQzVt30zPWnB4sSOD9bM3B+WXVVOEH7MxnGgjcThes2AdPwLrLfwVnekOAxqS2qb3tvpIxbkZj52TYBJ5VVV/0pAfsyGcqCN1OF65oI150DfBfYdmH3rcVF89+3IcFJccOXg+MUenOp13JgSll11b8TVZPJTBa8mS5cdydwKPDuWzk57/2+eOB+rkvqq9YNlBEL95DXerj5H4F9bfYIAzZyre8IFw08bqdR160Khb7nupS+LH+Lbrz7lpbGgDI0zk3W4t3W8dEklmUyjLV4+4pw3WdPnV/1fbyphOzSpCRpyJPCJw6Ke362fwMTLXRNW9bi7YMmzplYCtyBd5MRk2CBiDb/5caWlTkt7NrBKg3AZWXVVbelsi6TOazF2wdVzqysqZxZ+QW8+zx84rqeviYSkNCfPh/oaILSRcD+FrqmMxa8fVjlzMoHgQl4rd/uTHRouvDsvoGDN/VjacyidXj3aD2wrLoqU6aKN45YV0OWmDhn4iF4E2p2Nkmg6YbP1USW/eT+yB54M8v+rKy6ar3rmkxmsODNMhPnTPwS3p3593JdSx/wyEWPtFx+/qNvvuO6EJNZLHiz0MQ5EwN4U7b8DCh1W01Geh64onJm5SuuCzGZyYI3i02cMzEXOA+4GNjDcTnpLoI3Tcz1lTMrF7ouxmQ2C17DxDkTBe/eDxcDRzsuJ9004s2ye2PlzMq4b8lpTGcseE0rE+dM3Ae4CO9StH5uq3FqOXAP8IfKmZUdXTpmTI9Y8Jp2TZwzcSBwOt5U3lMAcVtRSmwE/g7cXTmzcr7rYkzfZcFrujRxzsSdgLOAs4GJjstJtHrgGeAh4KHKmZWbHddjsoAFr+kWfzjyCf7jGKDAaUE98yHwhP94tnJmZTKn9jFmBxa8pscmzpmYA0wGjse7NeV+pGcQvw8s8B8vVs6sXOy2HJPtLHhNwvjXB5cBBwEHAgcA44GBKSqhBS9k3wKW4Ydt5cxKm73XpBULXpN0E+dMLAHGArvFPEYAJf6jGCik86soGoA1ePe3jT7WAB8Db/uP5ZUzK7cm5U0Yk0AWvCZt+F0XIbzBCtselTMr7ZfU9CkWvMYYk2J2W0hjjEkxC15jjEkxC940IyItIrJYRP4nIv8UkeKY1/YSkedE5G0ReUdErhbPfSLy3Zj1DhaRpSIS8h9rReS6NseZKyJvicgSEXlJRMb7y2tEZEjMelNF5An/+3NFZI1fX7WIXNxmn/uKiIrI8W2Wq4j8JeZ5yN/PEwn6sRmTUSx400+Dqk5S1c8BtXizGiAi+cDjwCxV3QPYBzgM+B7ezW0uFZGhIhIAbgO+p6rNwHF4l1edISJth/2erar74N0E5oY463tQVScBhwNXishOMa/NAOb7X2NtBj7nvweAzwMfxXk8Y/ocC970tgAY7X9/FvCSqj4FoKr1wAVAuaquBm4Erge+AyxV1ei9BmYAs4EP6Hj2iReB3btTmKquw7uRzEgAP9RPB84FjhORtpeG/QvvDmjRmu7vzvGM6UsseNOUiATxhuQ+7i/aC/hv7Dqq+i5QICIDgTuBPYFLgcv8feT7+3gCL+jatkSjTgIqu1nfznjX3UbnHTscWOHXNBf4YptNHgCm+4G8N/Bqd45nTF9iwZt+8kVkMd7kiYOBp/3lAnR07Z+qagT4HfAvvzUKcCLwvN86fgg41Q/0qHv9Yx0OXBLdV3v7j/n+TBFZBrwHzFbV6H0OZuCFK/7XViGvqkvxZruYATzZwfswJitY8KafBr8PdRcgF7+PF28I7AGxK4rIbsAmVd3oL4oOOoiaARwrIjV4reUS4KiY18/2+5NPUdWV/rJ1wKCYdQbjjRKLelBV9wKOAG4SkRF+mH8Z+Il/rFuBL4hIYZv39jhel4h1M5isZsGbplS1DvghcImI5AD3ApNF5FjY1o3wG7x+3R343Q+TgZ1VtVRVS/FCvKPuhqi5ePfgjXZ3fBVvjrG29S0A/gJcCBwLLFHVnfxj7YLXwj6lzWZ/An6uqt3q1jCmr7HgTWOqughYAkxX1QbgS8BVIvIWXp/sa3hXMLTnNOA5Vd0Ss+wx4GQRyevksNcAu4vIEmAR3gm0v3aw7q+ArwPfwpuPLNZDeCcEY9/Ph6o6u5NjG5MVbMiwMcakmLV4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxSx4jTEmxf4fK3yKAwT9DFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(pss,labels = zones[:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "da7989f1",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x2202284f670>,\n",
       "  <matplotlib.patches.Wedge at 0x2202284fa30>,\n",
       "  <matplotlib.patches.Wedge at 0x2202284ff10>,\n",
       "  <matplotlib.patches.Wedge at 0x2202285e430>,\n",
       "  <matplotlib.patches.Wedge at 0x2202285e910>,\n",
       "  <matplotlib.patches.Wedge at 0x2202285edf0>],\n",
       " [Text(0.23995754141400957, 1.0735084435245696, 'TEYNAMPET'),\n",
       "  Text(-0.9953097349102212, -0.4683572691791443, 'TONDIARPET'),\n",
       "  Text(0.30689027796121454, -1.0563230364300917, 'ROYAPURAM'),\n",
       "  Text(0.9801072140121813, -0.4993894763020948, 'THIRU-VI-KA NAGAR'),\n",
       "  Text(1.0913261831276604, -0.1378664644502508, 'KODAMBAKKAM'),\n",
       "  Text(1.0994572192571674, -0.03455174414261459, 'ADYAR')])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(pps,labels = zones[:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ded1ed6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}