craft_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from statsmodels.graphics.tsaplots import plot_acf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Names of State(s)</th>\n",
       "      <th>Total Male</th>\n",
       "      <th>Total Female</th>\n",
       "      <th>Total</th>\n",
       "      <th>Full time Female weavers</th>\n",
       "      <th>Full time Female weavers as a %age of total female weavers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>1,28,728</td>\n",
       "      <td>52,760</td>\n",
       "      <td>1,81,508</td>\n",
       "      <td>46384</td>\n",
       "      <td>87.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>210</td>\n",
       "      <td>25,662</td>\n",
       "      <td>25,872</td>\n",
       "      <td>9363</td>\n",
       "      <td>36.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>12,765</td>\n",
       "      <td>13,88,653</td>\n",
       "      <td>14,01,418</td>\n",
       "      <td>686457</td>\n",
       "      <td>49.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>9,280</td>\n",
       "      <td>2,473</td>\n",
       "      <td>11,753</td>\n",
       "      <td>1783</td>\n",
       "      <td>72.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>2,211</td>\n",
       "      <td>218</td>\n",
       "      <td>2,429</td>\n",
       "      <td>212</td>\n",
       "      <td>97.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Names of State(s) Total Male Total Female      Total  \\\n",
       "0     Andhra Pradesh   1,28,728       52,760   1,81,508   \n",
       "1  Arunachal Pradesh        210       25,662     25,872   \n",
       "2              Assam     12,765    13,88,653  14,01,418   \n",
       "3              Bihar      9,280        2,473     11,753   \n",
       "4       Chhattisgarh      2,211          218      2,429   \n",
       "\n",
       "  Full time Female weavers  \\\n",
       "0                    46384   \n",
       "1                     9363   \n",
       "2                   686457   \n",
       "3                     1783   \n",
       "4                      212   \n",
       "\n",
       "   Full time Female weavers as a %age of total female weavers  \n",
       "0                                              87.92           \n",
       "1                                              36.49           \n",
       "2                                              49.43           \n",
       "3                                              72.10           \n",
       "4                                              97.25           "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('D:\\Feynn labs - Internship\\data\\handloom-census.csv')\n",
    "df1 = df1.drop('Sl. No.', axis='columns')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Full time Female weavers as a %age of total female weavers</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>68.843333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.950937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>51.307500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>87.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Full time Female weavers as a %age of total female weavers\n",
       "count                                          30.000000         \n",
       "mean                                           68.843333         \n",
       "std                                            27.950937         \n",
       "min                                             5.040000         \n",
       "25%                                            51.307500         \n",
       "50%                                            82.180000         \n",
       "75%                                            87.720000         \n",
       "max                                           100.000000         "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>States</th>\n",
       "      <th>Artisan enrolled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A &amp; N Island</td>\n",
       "      <td>2232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>58816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>9401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Assam</td>\n",
       "      <td>79687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>131730</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              States  Artisan enrolled\n",
       "0       A & N Island              2232\n",
       "1     Andhra Pradesh             58816\n",
       "2  Arunachal Pradesh              9401\n",
       "3              Assam             79687\n",
       "4              Bihar            131730"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('D:\\Feynn labs - Internship\\data\\statewise-artisans.csv').drop('Sl. No', axis='columns').drop([32], axis=0)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Artisan enrolled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>83718.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>166406.254755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2232.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13538.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>36667.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>86215.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>935844.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Artisan enrolled\n",
       "count         32.000000\n",
       "mean       83718.500000\n",
       "std       166406.254755\n",
       "min         2232.000000\n",
       "25%        13538.000000\n",
       "50%        36667.000000\n",
       "75%        86215.000000\n",
       "max       935844.000000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Total Cluster(s)</th>\n",
       "      <th>Total Artisans / SHGs</th>\n",
       "      <th>Product(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>29</td>\n",
       "      <td>18405 / 741</td>\n",
       "      <td>2060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arunachal Pradesh</td>\n",
       "      <td>6</td>\n",
       "      <td>1141 / 65</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Assam</td>\n",
       "      <td>46</td>\n",
       "      <td>14914 / 946</td>\n",
       "      <td>2365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>6</td>\n",
       "      <td>1447 / 104</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chhattisgarh</td>\n",
       "      <td>8</td>\n",
       "      <td>4185 / 375</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               State  Total Cluster(s) Total Artisans / SHGs  Product(s)\n",
       "0     Andhra Pradesh                29           18405 / 741        2060\n",
       "1  Arunachal Pradesh                 6             1141 / 65         216\n",
       "2              Assam                46           14914 / 946        2365\n",
       "3              Bihar                 6            1447 / 104         178\n",
       "4       Chhattisgarh                 8            4185 / 375         391"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.read_csv('D:\\Feynn labs - Internship\\data\\statewise-artists-products.csv').drop('S.No.', axis=1).drop([29], axis=0)\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Cluster(s)</th>\n",
       "      <th>Product(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>25.655172</td>\n",
       "      <td>1217.655172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.840459</td>\n",
       "      <td>1431.067890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.000000</td>\n",
       "      <td>432.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>764.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1330.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>121.000000</td>\n",
       "      <td>6054.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Total Cluster(s)   Product(s)\n",
       "count         29.000000    29.000000\n",
       "mean          25.655172  1217.655172\n",
       "std           27.840459  1431.067890\n",
       "min            1.000000    19.000000\n",
       "25%            8.000000   432.000000\n",
       "50%           18.000000   764.000000\n",
       "75%           29.000000  1330.000000\n",
       "max          121.000000  6054.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Actual value of Production (Rs in lakhs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-91</td>\n",
       "      <td>271.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991-92</td>\n",
       "      <td>304.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992-93</td>\n",
       "      <td>320.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>342.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994-95</td>\n",
       "      <td>364.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Actual value of Production (Rs in lakhs)\n",
       "0  1990-91                                     271.8\n",
       "1  1991-92                                     304.8\n",
       "2  1992-93                                     320.4\n",
       "3  1993-94                                     342.2\n",
       "4  1994-95                                     364.2"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.read_csv('D:\\Feynn labs - Internship\\data\\Year-wise-production-of-handicrafts-Rs-in-lacs.png.csv').drop('Y=715.80 + 73.92X (Trend value of production)', axis=1)\n",
    "df4.columns = df4.columns.str.strip()\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Actual value of Production (Rs in lakhs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>788.820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>453.541344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>271.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>394.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>707.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1865.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual value of Production (Rs in lakhs)\n",
       "count                                 20.000000\n",
       "mean                                 788.820000\n",
       "std                                  453.541344\n",
       "min                                  271.800000\n",
       "25%                                  394.200000\n",
       "50%                                  707.100000\n",
       "75%                                 1079.350000\n",
       "max                                 1865.600000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Year</th>\n",
       "      <th>Actual Sales (Rs in lakhs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-91</td>\n",
       "      <td>269.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1991-92</td>\n",
       "      <td>302.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1992-93</td>\n",
       "      <td>320.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1993-94</td>\n",
       "      <td>342.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994-95</td>\n",
       "      <td>358.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year  Actual Sales (Rs in lakhs)\n",
       "0  1990-91                       269.8\n",
       "1  1991-92                       302.8\n",
       "2  1992-93                       320.4\n",
       "3  1993-94                       342.2\n",
       "4  1994-95                       358.2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.read_csv('D:\\Feynn labs - Internship\\data\\Year-wise-sales-of-handicrafts-Rs-in-lacs.png.csv').drop('Y = 750.80 + 74.05X (Trend value of Sales)', axis=1)\n",
    "df5.columns = df5.columns.str.strip()\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>Actual Sales (Rs in lakhs)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>787.860000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>454.299276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>269.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>392.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>704.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1079.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1865.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Actual Sales (Rs in lakhs)\n",
       "count                   20.000000\n",
       "mean                   787.860000\n",
       "std                    454.299276\n",
       "min                    269.800000\n",
       "25%                    392.850000\n",
       "50%                    704.400000\n",
       "75%                   1079.350000\n",
       "max                   1865.600000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 2 columns):\n",
      " #   Column                                    Non-Null Count  Dtype  \n",
      "---  ------                                    --------------  -----  \n",
      " 0   Year                                      20 non-null     object \n",
      " 1   Actual value of Production (Rs in lakhs)  20 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df4.info() # production year-wise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Actual value of Production (Rs in lakhs)</th>\n",
       "      <th>yearly_average_production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-91</th>\n",
       "      <td>271.8</td>\n",
       "      <td>271.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-92</th>\n",
       "      <td>304.8</td>\n",
       "      <td>304.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-93</th>\n",
       "      <td>320.4</td>\n",
       "      <td>320.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-94</th>\n",
       "      <td>342.2</td>\n",
       "      <td>342.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-95</th>\n",
       "      <td>364.2</td>\n",
       "      <td>364.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995-96</th>\n",
       "      <td>404.2</td>\n",
       "      <td>404.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-97</th>\n",
       "      <td>450.2</td>\n",
       "      <td>450.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997-98</th>\n",
       "      <td>496.6</td>\n",
       "      <td>496.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-99</th>\n",
       "      <td>563.2</td>\n",
       "      <td>563.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-00</th>\n",
       "      <td>664.0</td>\n",
       "      <td>664.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01</th>\n",
       "      <td>750.2</td>\n",
       "      <td>750.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001-02</th>\n",
       "      <td>820.4</td>\n",
       "      <td>820.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-03</th>\n",
       "      <td>900.6</td>\n",
       "      <td>900.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-04</th>\n",
       "      <td>981.2</td>\n",
       "      <td>981.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-05</th>\n",
       "      <td>1050.4</td>\n",
       "      <td>1050.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005-06</th>\n",
       "      <td>1166.2</td>\n",
       "      <td>1166.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-07</th>\n",
       "      <td>1262.4</td>\n",
       "      <td>1262.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-08</th>\n",
       "      <td>1366.6</td>\n",
       "      <td>1366.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-09</th>\n",
       "      <td>1431.2</td>\n",
       "      <td>1431.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-10</th>\n",
       "      <td>1865.6</td>\n",
       "      <td>1865.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual value of Production (Rs in lakhs)  yearly_average_production\n",
       "year                                                                        \n",
       "1990-91                                     271.8                      271.8\n",
       "1991-92                                     304.8                      304.8\n",
       "1992-93                                     320.4                      320.4\n",
       "1993-94                                     342.2                      342.2\n",
       "1994-95                                     364.2                      364.2\n",
       "1995-96                                     404.2                      404.2\n",
       "1996-97                                     450.2                      450.2\n",
       "1997-98                                     496.6                      496.6\n",
       "1998-99                                     563.2                      563.2\n",
       "1999-00                                     664.0                      664.0\n",
       "2000-01                                     750.2                      750.2\n",
       "2001-02                                     820.4                      820.4\n",
       "2002-03                                     900.6                      900.6\n",
       "2003-04                                     981.2                      981.2\n",
       "2004-05                                    1050.4                     1050.4\n",
       "2005-06                                    1166.2                     1166.2\n",
       "2006-07                                    1262.4                     1262.4\n",
       "2007-08                                    1366.6                     1366.6\n",
       "2008-09                                    1431.2                     1431.2\n",
       "2009-10                                    1865.6                     1865.6"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index for year column\n",
    "df4.set_index('Year', inplace=True)\n",
    "df4.index.name = 'year'\n",
    "# Yearly average production calculation\n",
    "df4['yearly_average_production'] = df4.mean(axis=1)\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMA over a period of 10 and 20 years \n",
    "df4['SMA_10'] = df4.yearly_average_production.rolling(10, min_periods=1).mean()\n",
    "df4['SMA_20'] = df4.yearly_average_production.rolling(20, min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Production Value (Lacs of Rs.)')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grean = Avg Prod, RED = 10 yrs, ORANG colors for the line plot\n",
    "colors = ['green', 'red', 'orange']\n",
    "# Line plot \n",
    "df4.plot(color=colors, linewidth=3, figsize=(12,6))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['Average production value', '10-years SMA', '20-years SMA'], fontsize=14)\n",
    "plt.title('The yearly average production value (Rs. Lakhs)', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Production Value (Lacs of Rs.)', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20 entries, 0 to 19\n",
      "Data columns (total 2 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   Year                        20 non-null     object \n",
      " 1   Actual Sales (Rs in lakhs)  20 non-null     float64\n",
      "dtypes: float64(1), object(1)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Actual Sales (Rs in lakhs)</th>\n",
       "      <th>yearly_average_sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-91</th>\n",
       "      <td>269.8</td>\n",
       "      <td>269.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991-92</th>\n",
       "      <td>302.8</td>\n",
       "      <td>302.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992-93</th>\n",
       "      <td>320.4</td>\n",
       "      <td>320.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993-94</th>\n",
       "      <td>342.2</td>\n",
       "      <td>342.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-95</th>\n",
       "      <td>358.2</td>\n",
       "      <td>358.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Actual Sales (Rs in lakhs)  yearly_average_sales\n",
       "year                                                     \n",
       "1990-91                       269.8                 269.8\n",
       "1991-92                       302.8                 302.8\n",
       "1992-93                       320.4                 320.4\n",
       "1993-94                       342.2                 342.2\n",
       "1994-95                       358.2                 358.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index for year column\n",
    "df5.set_index('Year', inplace=True)\n",
    "df5.index.name = 'year'\n",
    "# Yearly average production calculation\n",
    "df5['yearly_average_sales'] = df5.mean(axis=1)\n",
    "df5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SMA over a period of 10 and 20 years \n",
    "df5['SMA_10'] = df5.yearly_average_sales.rolling(10, min_periods=1).mean()\n",
    "df5['SMA_20'] = df5.yearly_average_sales.rolling(20, min_periods=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Sales (Lacs of Rs.)')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grean = Avg Prod, RED = 10 yrs, ORANG colors for the line plot\n",
    "colors = ['green', 'red', 'orange']\n",
    "# Line plot \n",
    "df5.plot(color=colors, linewidth=3, figsize=(12,6))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.legend(labels =['Average Sales value', '10-years SMA', '20-years SMA'], fontsize=14)\n",
    "plt.title('The yearly average sales value (Rs. Lakhs)', fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Sales (Lacs of Rs.)', fontsize=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}