EPF.ipynb

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      "# Employee Provident Fund company data\n",
      "\n",
      "I was doing my usual weekly ego-search for\n",
      "[Gramener](https://www.google.com/search?q=gramener) when I found\n",
      "[Gramener's employee provident fund records](http://esewa.epfoservices.in/ecr_dashboard/estt_wise_member_missing_detail.php?pageNum_Recordset1=439&totalRows_Recordset1=8983&office_name=HYDERABAD).\n",
      "\n",
      "What's interesting is that the page lets you download the full list for the\n",
      "entire city of Hyderabad. Which got me wondering... could we do this for other\n",
      "cities? It turns out that there's a full [list of offices](http://esewa.epfoservices.in/ecr_dashboard/summary_of_reg_estt.php)\n",
      "that we can pull data from.\n",
      "\n",
      "A simple set of `curl` statements like\n",
      "\n",
      "    curl -s -o DELHINORTH.xls http://esewa.epfoservices.in/ecr_dashboard/export_establishment_wise_missing_detail.php?office_name=DELHINORTH\n",
      "    curl -s -o DEHRADUN.xls http://esewa.epfoservices.in/ecr_dashboard/export_establishment_wise_missing_detail.php?office_name=DEHRADUN\n",
      "    curl -s -o HALDWANI.xls http://esewa.epfoservices.in/ecr_dashboard/export_establishment_wise_missing_detail.php?office_name=HALDWANI\n",
      "    ...\n",
      "\n",
      "gets you 146 MB of EPF data for every single company. Or, you could scrape it in Python.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "\n",
      "dfs = pd.read_html('http://esewa.epfoservices.in/ecr_dashboard/summary_of_reg_estt.php', infer_types=False, skiprows=2)\n",
      "dfs[0].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "      <th>9</th>\n",
        "      <th>10</th>\n",
        "      <th>11</th>\n",
        "      <th>12</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  RO</td>\n",
        "      <td> DL</td>\n",
        "      <td> CPM</td>\n",
        "      <td> DELHINORTH</td>\n",
        "      <td> 26752</td>\n",
        "      <td> 157184</td>\n",
        "      <td> 17</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 15713</td>\n",
        "      <td> 58.74</td>\n",
        "      <td> 209</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  RO</td>\n",
        "      <td> UK</td>\n",
        "      <td> DDN</td>\n",
        "      <td>   DEHRADUN</td>\n",
        "      <td>  4546</td>\n",
        "      <td>  36406</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0.04</td>\n",
        "      <td>  3192</td>\n",
        "      <td> 70.22</td>\n",
        "      <td>  26</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> SRO</td>\n",
        "      <td> UK</td>\n",
        "      <td> HLD</td>\n",
        "      <td>   HALDWANI</td>\n",
        "      <td>  2231</td>\n",
        "      <td>  21458</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0.09</td>\n",
        "      <td>  1676</td>\n",
        "      <td> 75.12</td>\n",
        "      <td>  18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 4</td>\n",
        "      <td> 1</td>\n",
        "      <td> SRO</td>\n",
        "      <td> DS</td>\n",
        "      <td> SHD</td>\n",
        "      <td> LAXMINAGAR</td>\n",
        "      <td>  6559</td>\n",
        "      <td>  36083</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0.05</td>\n",
        "      <td>  3811</td>\n",
        "      <td>  58.1</td>\n",
        "      <td>  70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td>  RO</td>\n",
        "      <td> DS</td>\n",
        "      <td> NHP</td>\n",
        "      <td> DELHISOUTH</td>\n",
        "      <td> 13915</td>\n",
        "      <td> 102883</td>\n",
        "      <td> 10</td>\n",
        "      <td> 0.07</td>\n",
        "      <td>  8282</td>\n",
        "      <td> 59.52</td>\n",
        "      <td> 166</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "  0  1    2   3    4           5      6       7   8     9      10     11   12\n",
        "2  1  1   RO  DL  CPM  DELHINORTH  26752  157184  17  0.06  15713  58.74  209\n",
        "3  2  1   RO  UK  DDN    DEHRADUN   4546   36406   2  0.04   3192  70.22   26\n",
        "4  3  1  SRO  UK  HLD    HALDWANI   2231   21458   2  0.09   1676  75.12   18\n",
        "5  4  1  SRO  DS  SHD  LAXMINAGAR   6559   36083   3  0.05   3811   58.1   70\n",
        "6  5  1   RO  DS  NHP  DELHISOUTH  13915  102883  10  0.07   8282  59.52  166"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os.path\n",
      "from urllib import urlretrieve\n",
      "\n",
      "def get_epf(offices, path):\n",
      "    data = []\n",
      "    base = 'http://esewa.epfoservices.in/ecr_dashboard/export_establishment_wise_missing_detail.php?office_name='\n",
      "    \n",
      "    # Weird workaround because pd.read_excel does not support encoding_override\n",
      "    xl = pd.ExcelFile.__new__(pd.ExcelFile)\n",
      "    \n",
      "    for office in offices:\n",
      "        excel_file = os.path.join(path, office) + '.xls'\n",
      "        if not os.path.exists(excel_file):\n",
      "            urlretrieve(base + office, excel_file)\n",
      "        try:\n",
      "            book = xlrd.open_workbook(excel_file, encoding_override='cp1252')\n",
      "        except UnicodeDecodeError:\n",
      "            book = xlrd.open_workbook(excel_file, encoding_override='utf8')\n",
      "        setattr(xl, 'book', book)\n",
      "        df = xl.parse('Sheet 1', skiprows=2)\n",
      "        df['Office'] = office\n",
      "        data.append(df)\n",
      "    \n",
      "    return pd.concat(data, ignore_index=True)\n",
      "\n",
      "path = 'd:/site/gramener.com/viz/epf/establishments/'\n",
      "datafile = os.path.join(path, 'data.csv')\n",
      "if os.path.exists(datafile):\n",
      "    data = pd.read_csv(datafile, encoding='cp1252')\n",
      "else:\n",
      "    data = get_epf(offices=dfs[0][5][:120], path=path)\n",
      "    data.to_csv(datafile, index=False, encoding='cp1252')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 146
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Presenting India's largest companies, by employee count"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.sort('On the basis of ECR (wage month Feb-2013 onwards)', ascending=False).head(20)[['Office', 'Estt. Name', 'On the basis of ECR (wage month Feb-2013 onwards)']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Office</th>\n",
        "      <th>Estt. Name</th>\n",
        "      <th>On the basis of ECR (wage month Feb-2013 onwards)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>315758</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                TATA CONSULTANCY SERVICES LIMITED</td>\n",
        "      <td> 249660</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>265395</th>\n",
        "      <td>        PARKSTREET</td>\n",
        "      <td>    LARSEN - TOUBRO LTD. (ECC CONSTRUCTION GROUP)</td>\n",
        "      <td> 157831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0     </th>\n",
        "      <td>        DELHINORTH</td>\n",
        "      <td>                              LARSEN - TOUBRO LTD</td>\n",
        "      <td> 149374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>203666</th>\n",
        "      <td>           CHENNAI</td>\n",
        "      <td> COGNIZANT TECHNOLOGY SOLUTIONS INDIA PRIVATE LTD</td>\n",
        "      <td> 138108</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315759</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                           LARSEN - TURBO LTD ECC</td>\n",
        "      <td> 136442</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129643</th>\n",
        "      <td>         HYDERABAD</td>\n",
        "      <td>                                      A P S R T C</td>\n",
        "      <td> 131587</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187848</th>\n",
        "      <td>       BOMMASANDRA</td>\n",
        "      <td>                           ADECCO INDIA PVT. LTD.</td>\n",
        "      <td> 130769</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187849</th>\n",
        "      <td>       BOMMASANDRA</td>\n",
        "      <td>                              M/S INFOSYS LIMITED</td>\n",
        "      <td> 126704</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>173884</th>\n",
        "      <td>         BANGALORE</td>\n",
        "      <td>                      TEAM LEASE SERVICES PVT LTD</td>\n",
        "      <td> 107921</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315760</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                     ACCENTURE SERVICES PVT. LTD.</td>\n",
        "      <td> 101822</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315761</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                     SHAPOORJI PALLONJI - CO LTD.</td>\n",
        "      <td>  99531</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>95233 </th>\n",
        "      <td>             PATNA</td>\n",
        "      <td>   SECURITY AND INTELLIGENCE SERVICE (INIDA) LTD.</td>\n",
        "      <td>  95299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>406613</th>\n",
        "      <td>         AHMEDABAD</td>\n",
        "      <td>                          LARSEN - TOUBRO LIMITED</td>\n",
        "      <td>  94941</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>198662</th>\n",
        "      <td>          TAMBARAM</td>\n",
        "      <td>                     M/S.LARSEN - TOUBRO LIMITED,</td>\n",
        "      <td>  94087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>190766</th>\n",
        "      <td> KRPURAMWHITEFIELD</td>\n",
        "      <td>                                IBM INDIA PVT.LTD</td>\n",
        "      <td>  93927</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129644</th>\n",
        "      <td>         HYDERABAD</td>\n",
        "      <td>                      LARSEN TOUBRO LTD.,[ECC-CG]</td>\n",
        "      <td>  90508</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315762</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                                H D F C BANK LTD.</td>\n",
        "      <td>  87726</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>315763</th>\n",
        "      <td>    BANDRAMUMBAI-I</td>\n",
        "      <td>                                         M.S.E.B.</td>\n",
        "      <td>  85844</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187850</th>\n",
        "      <td>       BOMMASANDRA</td>\n",
        "      <td>           M/S WIPRO LIMITED - TECHNOLOGIES GROUP</td>\n",
        "      <td>  85099</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41293 </th>\n",
        "      <td>           GURGAON</td>\n",
        "      <td>                             HINDUSTAN INSTRUMENT</td>\n",
        "      <td>  83170</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "                   Office                                        Estt. Name  \\\n",
        "315758     BANDRAMUMBAI-I                 TATA CONSULTANCY SERVICES LIMITED   \n",
        "265395         PARKSTREET     LARSEN - TOUBRO LTD. (ECC CONSTRUCTION GROUP)   \n",
        "0              DELHINORTH                               LARSEN - TOUBRO LTD   \n",
        "203666            CHENNAI  COGNIZANT TECHNOLOGY SOLUTIONS INDIA PRIVATE LTD   \n",
        "315759     BANDRAMUMBAI-I                            LARSEN - TURBO LTD ECC   \n",
        "129643          HYDERABAD                                       A P S R T C   \n",
        "187848        BOMMASANDRA                            ADECCO INDIA PVT. LTD.   \n",
        "187849        BOMMASANDRA                               M/S INFOSYS LIMITED   \n",
        "173884          BANGALORE                       TEAM LEASE SERVICES PVT LTD   \n",
        "315760     BANDRAMUMBAI-I                      ACCENTURE SERVICES PVT. LTD.   \n",
        "315761     BANDRAMUMBAI-I                      SHAPOORJI PALLONJI - CO LTD.   \n",
        "95233               PATNA    SECURITY AND INTELLIGENCE SERVICE (INIDA) LTD.   \n",
        "406613          AHMEDABAD                           LARSEN - TOUBRO LIMITED   \n",
        "198662           TAMBARAM                      M/S.LARSEN - TOUBRO LIMITED,   \n",
        "190766  KRPURAMWHITEFIELD                                 IBM INDIA PVT.LTD   \n",
        "129644          HYDERABAD                       LARSEN TOUBRO LTD.,[ECC-CG]   \n",
        "315762     BANDRAMUMBAI-I                                 H D F C BANK LTD.   \n",
        "315763     BANDRAMUMBAI-I                                          M.S.E.B.   \n",
        "187850        BOMMASANDRA            M/S WIPRO LIMITED - TECHNOLOGIES GROUP   \n",
        "41293             GURGAON                              HINDUSTAN INSTRUMENT   \n",
        "\n",
        "        On the basis of ECR (wage month Feb-2013 onwards)  \n",
        "315758                                             249660  \n",
        "265395                                             157831  \n",
        "0                                                  149374  \n",
        "203666                                             138108  \n",
        "315759                                             136442  \n",
        "129643                                             131587  \n",
        "187848                                             130769  \n",
        "187849                                             126704  \n",
        "173884                                             107921  \n",
        "315760                                             101822  \n",
        "315761                                              99531  \n",
        "95233                                               95299  \n",
        "406613                                              94941  \n",
        "198662                                              94087  \n",
        "190766                                              93927  \n",
        "129644                                              90508  \n",
        "315762                                              87726  \n",
        "315763                                              85844  \n",
        "187850                                              85099  \n",
        "41293                                               83170  "
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, a dataset like this requires a nice exploratory interface. Here's what I sketched quickly, and a working implementation is at https://gramener.com/companysize/"
     ]
    },
    {
     "cell_type": "markdown",
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