0_0_downloading_datasets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Downloading datasets:\n",
    "\n",
    "The datasets are stored in PostgreSQL database in amazon RDS. So I used psycopg2 to connect to the server and pandas to download the databases in to a datafram and then save them to  local drive for future use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import psycopg2\n",
    "from IPython.display import display, Audio\n",
    "from IPython.display import HTML\n",
    "\n",
    "from pandas_summary import DataFrameSummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "host= open('host.txt', 'r').read()\n",
    "password= open('password.txt', 'r').read()\n",
    "user= open('user.txt', 'r').read()\n",
    "dbname= open('dbname.txt', 'r').read()\n",
    "port= open('port.txt', 'r').read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    conn = psycopg2.connect(dbname=dbname , user=user, host=host, password=password, port = '5432')\n",
    "    cur = conn.cursor()\n",
    "except:\n",
    "    print (\"I am unable to connect to the database\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('companies_apps',),\n",
       " ('companies_tags',),\n",
       " ('companies_name',),\n",
       " ('companies_social_ids',),\n",
       " ('implicit_feedback',),\n",
       " ('operating_profile_score',),\n",
       " ('app_ids_mapping',),\n",
       " ('schema_migrations',),\n",
       " ('companies_black_list',),\n",
       " ('competitors',),\n",
       " ('companies_description',),\n",
       " ('companies_founding_date',),\n",
       " ('companies_headcount',),\n",
       " ('companies_funding',),\n",
       " ('companies_index_contacts',),\n",
       " ('companies_location',),\n",
       " ('id_map',),\n",
       " ('investors_exit_score_ver1',),\n",
       " ('tables_update_statuses',),\n",
       " ('companies_status',),\n",
       " ('investor_id_map',),\n",
       " ('investors_exit_score',),\n",
       " ('spatial_ref_sys',),\n",
       " ('isup',)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur.execute(\"select relname from pg_class where relkind='r' and relname !~ '^(pg_|sql_)';\")\n",
    "table_list = cur.fetchall()\n",
    "table_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tables = [pd.read_sql('select * from '+table_list[i][0]+';', conn) for i in range(len(table_list))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looking at the Summary of each datafram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset has non hashable objects\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ds_uuid</th>\n",
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       "      <th>count</th>\n",
       "      <td>1080956</td>\n",
       "      <td>1083058</td>\n",
       "      <td>977452</td>\n",
       "      <td>1083058</td>\n",
       "      <td>1022508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>307872</td>\n",
       "      <td>308061</td>\n",
       "      <td>23805</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
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       "      <th>top</th>\n",
       "      <td>7433bb5c-79c2-49e0-8a5d-e5c43ded1de5</td>\n",
       "      <td>surprizbul.com</td>\n",
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       "      <td>2016-06-15</td>\n",
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       "      <th>freq</th>\n",
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       "      <th>missing</th>\n",
       "      <td>2102</td>\n",
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       "      <td>60550</td>\n",
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       "    <tr>\n",
       "      <th>missing_perc</th>\n",
       "      <td>0.19%</td>\n",
       "      <td>0%</td>\n",
       "      <td>9.75%</td>\n",
       "      <td>0%</td>\n",
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      "text/plain": [
       "                                           ds_uuid       clean_url  \\\n",
       "count                                      1080956         1083058   \n",
       "unique                                      307872          308061   \n",
       "top           7433bb5c-79c2-49e0-8a5d-e5c43ded1de5  surprizbul.com   \n",
       "freq                                           102             102   \n",
       "counts                                     1080956         1083058   \n",
       "uniques                                     307872          308061   \n",
       "missing                                       2102               0   \n",
       "missing_perc                                 0.19%              0%   \n",
       "types                                  categorical     categorical   \n",
       "\n",
       "                      tag  update_date update_status  \n",
       "count              977452      1083058       1022508  \n",
       "unique              23805           34             2  \n",
       "top                    NA   2016-06-15       success  \n",
       "freq                75284       236179        841618  \n",
       "counts             977452      1083058       1022508  \n",
       "uniques             23805           34             2  \n",
       "missing            105606            0         60550  \n",
       "missing_perc        9.75%           0%         5.59%  \n",
       "types         categorical  categorical          bool  "
      ]
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       "                                           ds_uuid         clean_url  \\\n",
       "count                                       698464            789834   \n",
       "unique                                      698444            729374   \n",
       "top           025060c2-01e3-4ffb-9252-c4339f0a3e9e  outdoorprivé.com   \n",
       "freq                                             2                24   \n",
       "counts                                      698464            789834   \n",
       "uniques                                     698444            729374   \n",
       "missing                                      91370                 0   \n",
       "missing_perc                                11.57%                0%   \n",
       "types                                  categorical       categorical   \n",
       "\n",
       "                     name  update_date update_status  \n",
       "count              764566       789834        698487  \n",
       "unique             691281           87             2  \n",
       "top               no data   2017-12-13       success  \n",
       "freq                 1488       145498        679709  \n",
       "counts             764566       789834        698487  \n",
       "uniques            691281           87             2  \n",
       "missing             25268            0         91347  \n",
       "missing_perc        3.20%           0%        11.57%  \n",
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       "      <td>2.48%</td>\n",
       "      <td>0%</td>\n",
       "      <td>12.13%</td>\n",
       "      <td>12.13%</td>\n",
       "      <td>2.92%</td>\n",
       "      <td>0.00%</td>\n",
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       "      <td>categorical</td>\n",
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      ],
      "text/plain": [
       "                                           ds_uuid      clean_url  \\\n",
       "count                                      4142364        4247573   \n",
       "unique                                      697976         702305   \n",
       "top           41e90b60-4cd9-40d4-93b2-42c39caf2ad5  micampaña.com   \n",
       "freq                                           242            487   \n",
       "counts                                     4142364        4247573   \n",
       "uniques                                     697976         702305   \n",
       "missing                                     105209              0   \n",
       "missing_perc                                 2.48%             0%   \n",
       "types                                  categorical    categorical   \n",
       "\n",
       "                  id_type social_id_clean update_status  update_date  \n",
       "count             3732446         3732446       4123544      4247570  \n",
       "unique                 68         3067600             2          130  \n",
       "top               twitter    wordpresscom       success   2017-12-19  \n",
       "freq               543103            1114       3608417      1081033  \n",
       "counts            3732446         3732446       4123544      4247570  \n",
       "uniques                68         3067600             2          130  \n",
       "missing            515127          515127        124029            3  \n",
       "missing_perc       12.13%          12.13%         2.92%        0.00%  \n",
       "types         categorical     categorical          bool  categorical  "
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       "                                           ds_uuid    clean_url  \\\n",
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       "top           89e016ee-71f8-4c99-a0cd-ec36dbdaa092    4info.com   \n",
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       "\n",
       "                                competitor_ds_uuid  update_date  \n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.33418e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0005e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>counts</th>\n",
       "      <td>427281</td>\n",
       "      <td>336574</td>\n",
       "      <td>336574</td>\n",
       "      <td>281504</td>\n",
       "      <td>336574</td>\n",
       "      <td>427261</td>\n",
       "      <td>336575</td>\n",
       "      <td>336575</td>\n",
       "      <td>282650</td>\n",
       "      <td>427316</td>\n",
       "      <td>427316</td>\n",
       "      <td>206113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uniques</th>\n",
       "      <td>184699</td>\n",
       "      <td>44831</td>\n",
       "      <td>6455</td>\n",
       "      <td>15825</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>54</td>\n",
       "      <td>184734</td>\n",
       "      <td>122</td>\n",
       "      <td>37558</td>\n",
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       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>35</td>\n",
       "      <td>90742</td>\n",
       "      <td>90742</td>\n",
       "      <td>145812</td>\n",
       "      <td>90742</td>\n",
       "      <td>55</td>\n",
       "      <td>90741</td>\n",
       "      <td>90741</td>\n",
       "      <td>144666</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>221203</td>\n",
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       "    <tr>\n",
       "      <th>missing_perc</th>\n",
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       "      <td>34.12%</td>\n",
       "      <td>21.24%</td>\n",
       "      <td>0.01%</td>\n",
       "      <td>21.24%</td>\n",
       "      <td>21.24%</td>\n",
       "      <td>33.85%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>51.77%</td>\n",
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       "    <tr>\n",
       "      <th>types</th>\n",
       "      <td>categorical</td>\n",
       "      <td>categorical</td>\n",
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       "      <td>categorical</td>\n",
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       "      <td>numeric</td>\n",
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       "      <td>categorical</td>\n",
       "      <td>categorical</td>\n",
       "      <td>categorical</td>\n",
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       "                  ds_uuid     investor         date amount_raised  \\\n",
       "count                 NaN          NaN          NaN        281504   \n",
       "mean                  NaN          NaN          NaN   3.60712e+07   \n",
       "std                   NaN          NaN          NaN   1.57354e+09   \n",
       "min                   NaN          NaN          NaN             0   \n",
       "25%                   NaN          NaN          NaN         1e+06   \n",
       "50%                   NaN          NaN          NaN       3.8e+06   \n",
       "75%                   NaN          NaN          NaN   1.33418e+07   \n",
       "max                   NaN          NaN          NaN    5.0005e+11   \n",
       "counts             427281       336574       336574        281504   \n",
       "uniques            184699        44831         6455         15825   \n",
       "missing                35        90742        90742        145812   \n",
       "missing_perc        0.01%       21.24%       21.24%        34.12%   \n",
       "types         categorical  categorical  categorical       numeric   \n",
       "\n",
       "               round_type update_status  source round_id     currency  \\\n",
       "count                 NaN           NaN     NaN   336575          NaN   \n",
       "mean                  NaN           NaN     NaN  2.17937          NaN   \n",
       "std                   NaN           NaN     NaN  1.61995          NaN   \n",
       "min                   NaN           NaN     NaN        1          NaN   \n",
       "25%                   NaN           NaN     NaN        1          NaN   \n",
       "50%                   NaN           NaN     NaN        2          NaN   \n",
       "75%                   NaN           NaN     NaN        3          NaN   \n",
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       "counts             336574        427261  336575   336575       282650   \n",
       "uniques                32             2       2       10           54   \n",
       "missing             90742            55   90741    90741       144666   \n",
       "missing_perc       21.24%         0.01%  21.24%   21.24%       33.85%   \n",
       "types         categorical          bool    bool  numeric  categorical   \n",
       "\n",
       "                clean_url  update_date investor_external_id  \n",
       "count                 NaN          NaN                  NaN  \n",
       "mean                  NaN          NaN                  NaN  \n",
       "std                   NaN          NaN                  NaN  \n",
       "min                   NaN          NaN                  NaN  \n",
       "25%                   NaN          NaN                  NaN  \n",
       "50%                   NaN          NaN                  NaN  \n",
       "75%                   NaN          NaN                  NaN  \n",
       "max                   NaN          NaN                  NaN  \n",
       "counts             427316       427316               206113  \n",
       "uniques            184734          122                37558  \n",
       "missing                 0            0               221203  \n",
       "missing_perc           0%           0%               51.77%  \n",
       "types         categorical  categorical          categorical  "
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       "             index_contact        clean_url  \\\n",
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       "uniques                                                   33114           80  \n",
       "missing                                                       0            0  \n",
       "missing_perc                                                 0%           0%  \n",
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       "      <td>178.441</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>430164</td>\n",
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       "                  ds_uuid country_code         city      lat      lon  \\\n",
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       "50%                   NaN          NaN          NaN  39.7833 -74.8716   \n",
       "75%                   NaN          NaN          NaN  45.5335  6.56279   \n",
       "max                   NaN          NaN          NaN  80.1483  178.441   \n",
       "counts             576575       427576       398154   430164   430164   \n",
       "uniques            576574          209        18199    29353    29713   \n",
       "missing              3467       152466       181888   149878   149878   \n",
       "missing_perc        0.60%       26.29%       31.36%   25.84%   25.84%   \n",
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       "\n",
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       "counts             580042        573689       580042  \n",
       "uniques               165             2       578715  \n",
       "missing                 0          6353            0  \n",
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       "      <th>uniques</th>\n",
       "      <td>44525</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing_perc</th>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "      <td>0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>types</th>\n",
       "      <td>categorical</td>\n",
       "      <td>numeric</td>\n",
       "      <td>numeric</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 investor   period exit_scores\n",
       "count                 NaN   118816      118816\n",
       "mean                  NaN  12.3122    0.825125\n",
       "std                   NaN  6.26926      6.2641\n",
       "min                   NaN        5         -69\n",
       "25%                   NaN        5           0\n",
       "50%                   NaN       10           0\n",
       "75%                   NaN       20           0\n",
       "max                   NaN       20         492\n",
       "counts             118816   118816      118816\n",
       "uniques             44525        3         188\n",
       "missing                 0        0           0\n",
       "missing_perc           0%       0%          0%\n",
       "types         categorical  numeric     numeric"
      ]
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      "text/plain": [
       "                 srid auth_name auth_srid  srtext    proj4text\n",
       "count            5009       NaN      5009     NaN          NaN\n",
       "mean          11341.9       NaN   11341.9     NaN          NaN\n",
       "std           16901.9       NaN   16901.9     NaN          NaN\n",
       "min              2000       NaN      2000     NaN          NaN\n",
       "25%              3284       NaN      3284     NaN          NaN\n",
       "50%              4780       NaN      4780     NaN          NaN\n",
       "75%             22992       NaN     22992     NaN          NaN\n",
       "max            900913       NaN    900913     NaN          NaN\n",
       "counts           5009      5009      5009    5009         5009\n",
       "uniques          5009         2      5009    5009         3949\n",
       "missing             0         0         0       0            0\n",
       "missing_perc       0%        0%        0%      0%           0%\n",
       "types         numeric      bool   numeric  unique  categorical"
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       "      <th></th>\n",
       "      <th>url_min</th>\n",
       "      <th>is_up_bool</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>680462</td>\n",
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       "      <th>top</th>\n",
       "      <td>accessmidstream.com</td>\n",
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       "      <td>2015-07-09 00:00:00</td>\n",
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       "      <td>0%</td>\n",
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       "      <td>0%</td>\n",
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       "      <th>types</th>\n",
       "      <td>categorical</td>\n",
       "      <td>bool</td>\n",
       "      <td>date</td>\n",
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      ],
      "text/plain": [
       "                          url_min is_up_bool                 date\n",
       "count                      680462     680462               680462\n",
       "unique                     298497          2                    4\n",
       "top           accessmidstream.com       True  2015-07-09 00:00:00\n",
       "freq                            3     622031               243745\n",
       "first                         NaN        NaN  2015-06-02 00:00:00\n",
       "last                          NaN        NaN  2015-09-02 00:00:00\n",
       "counts                     680462     680462               680462\n",
       "uniques                    298497          2                    4\n",
       "missing                         0          0                    0\n",
       "missing_perc                   0%         0%                   0%\n",
       "types                 categorical       bool                 date"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in tables: \n",
    "    try: display(DataFrameSummary(t).summary())\n",
    "    except : print(\"Dataset has non hashable objects\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saveing each datafram to a .csv:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tb_name = []\n",
    "dflist = []\n",
    "for i in range(len(table_list)):\n",
    "    tb_name.append(table_list[i][0])\n",
    "    \n",
    "for i,val in enumerate(tb_name):\n",
    "    globals()[val] = tables[i]\n",
    "    dflist.append(globals()[val])\n",
    "\n",
    "for i in range(len(table_list)): \n",
    "    df = dflist[i]\n",
    "    df.to_csv('./data2/'+tb_name[i]+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/project/data2\n",
      "app_ids_mapping.csv           companies_tags.csv\n",
      "companies_apps.csv            competitors.csv\n",
      "companies_black_list.csv      id_map.csv\n",
      "companies_description.csv     implicit_feedback.csv\n",
      "companies_founding_date.csv   investor_id_map.csv\n",
      "companies_funding.csv         investors_exit_score.csv\n",
      "companies_headcount.csv       investors_exit_score_ver1.csv\n",
      "companies_index_contacts.csv  isup.csv\n",
      "companies_location.csv        operating_profile_score.csv\n",
      "companies_name.csv            schema_migrations.csv\n",
      "companies_social_ids.csv      spatial_ref_sys.csv\n",
      "companies_status.csv          tables_update_statuses.csv\n"
     ]
    }
   ],
   "source": [
    "% cd data2\n",
    "% ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\r\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "! find . -type f | wc -l\n",
    "len(table_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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