NSS Data Analysis.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 5763152 Nss68_1.0_Type1/Data/R6801T1L05.TXT\r\n"
     ]
    }
   ],
   "source": [
    "! wc -l Nss68_1.0_Type1/Data/R6801T1L05.TXT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.7M records are available with that have the state - district - consumption values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parser_level_5(x):\n",
    "    # Refer to the excel sheet for the data format in the following order\n",
    "    return [x[15:18], x[18:20], x[39:42], x[59:68], x[68:76]]\n",
    "\n",
    "fields = ['State', 'District', 'ItemCode', 'TC_Quantity', 'TC_Value']\n",
    "\n",
    "# Which item statistics would you want? Change the following number to that corresponding number\n",
    "item_code = '113'\n",
    "\n",
    "# Let's parse the file and extract relevant fields using the above parser_level_5 function\n",
    "data5 = pd.read_table(\"Nss68_1.0_Type1/Data/R6801T1L05.TXT\", header=None)#, nrows=6000)\n",
    "data5 = data5[0].apply(lambda x: pd.Series(parser_level_5(x)))\n",
    "data5.columns = fields\n",
    "\n",
    "# Filter the data that we need\n",
    "data5 = data5[data5['ItemCode'] == item_code].reset_index(drop=True)\n",
    "\n",
    "# Change data types for numerical aggregations\n",
    "for col in data5.columns:\n",
    "    data5[col] = data5[col].astype(np.int64)\n",
    "\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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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>District</th>\n",
       "      <th>TC_Quantity</th>\n",
       "      <th>TC_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>171</td>\n",
       "      <td>1</td>\n",
       "      <td>28200</td>\n",
       "      <td>1160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>171</td>\n",
       "      <td>2</td>\n",
       "      <td>18600</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>171</td>\n",
       "      <td>3</td>\n",
       "      <td>3800</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   State  District  TC_Quantity  TC_Value\n",
       "0    171         1        28200      1160\n",
       "1    171         2        18600       946\n",
       "2    171         3         3800       160"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data5 = data5.pivot_table(index=['State', 'District'], values=['TC_Quantity', 'TC_Value'], aggfunc=np.sum).reset_index()\n",
    "data5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data5.to_csv(\"States_Districts_Q_V_stats.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>st</th>\n",
       "      <th>dc</th>\n",
       "      <th>district name</th>\n",
       "      <th>state name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Kupwara</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Baramula</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Srinagar</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Badgam</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Pulwama</td>\n",
       "      <td>Jammu &amp; Kashmir</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   st  dc district name       state name\n",
       "0   1   1       Kupwara  Jammu & Kashmir\n",
       "1   1   2      Baramula  Jammu & Kashmir\n",
       "2   1   3      Srinagar  Jammu & Kashmir\n",
       "3   1   4        Badgam  Jammu & Kashmir\n",
       "4   1   5       Pulwama  Jammu & Kashmir"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# <<pip install xlrd>> before running this cell\n",
    "states_mappings = pd.read_excel(\"Nss68_1.0_Type1/Supporting Documents_68/District-codes.xls\", skiprows=2)\n",
    "states_mappings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bunch of questions:\n",
    "- While the state field is of length 3 in the layout68_1.0_typ1.xls master reference document, the state code is often found to be of length 2 in the state_code - district mappings document (District-codes.xls). This makes the data inconsistent and thus joins are risky here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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