Import 1850-Total sample.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1850 DATA: P INDIVIDUAL DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_location = \"/Volumes/External/1850_3-2/us1850c_usa.dat\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_fwf(\n",
    "    data_location, header=None,\n",
    "    colspecs=[\n",
    "        (0, 1),\n",
    "        (1, 5),\n",
    "        (5, 11),\n",
    "        (11, 19),\n",
    "        (19, 23),\n",
    "        (31, 33),\n",
    "        (33, 34),\n",
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    "        (38, 39),\n",
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    "        (3015, 3065),\n",
    "        (3065, 3115),\n",
    "        (3115, 3140),\n",
    "        (3140, 3176),     \n",
    "    ],\n",
    "    names=[\n",
    "        'rectype',\n",
    "        'yearp',\n",
    "        'samplep',\n",
    "        'serialp',\n",
    "        'pernum',\n",
    "        'momloc',\n",
    "        'stepmom',\n",
    "        'momrule_hist',\n",
    "        'poploc',\n",
    "        'steppop',\n",
    "        'poprule_hist',\n",
    "        'sploc',\n",
    "        'sprule_hist',\n",
    "        'famsize',\n",
    "        'nchild',\n",
    "        'nchlt5',\n",
    "        'famunit',\n",
    "        'eldch',\n",
    "        'yngch',\n",
    "        'nsibs',\n",
    "        'relate',\n",
    "        'age',\n",
    "        'sex',\n",
    "        'race',\n",
    "        'marst',\n",
    "        'marrinyr',\n",
    "        'bpl',\n",
    "        'hispan',\n",
    "        'spanname',\n",
    "        'school',\n",
    "        'lit',\n",
    "        'labforce',\n",
    "        'occ1950',\n",
    "        'occscore',\n",
    "        'sei',\n",
    "        'ind1950',\n",
    "        'realprop',\n",
    "        'imppop',\n",
    "        'impsp',\n",
    "        'imprel',\n",
    "        'qage',\n",
    "        'qagemont',\n",
    "        'qbpl',\n",
    "        'qocc',\n",
    "        'qrace',\n",
    "        'qschool',\n",
    "        'qsex',\n",
    "        'racamind',\n",
    "        'racasian',\n",
    "        'racblk',\n",
    "        'racpacis',\n",
    "        'racother',\n",
    "        'racwht',\n",
    "        'agediff',\n",
    "        'racesing',\n",
    "        'probwht',\n",
    "        'proboth',\n",
    "        'probblk',\n",
    "        'probapi',\n",
    "        'probai',\n",
    "        'hisprule',\n",
    "        'presgl',\n",
    "        'erscor50',\n",
    "        'edscor50',\n",
    "        'npboss50',\n",
    "        'occstr',\n",
    "        'isrelate',\n",
    "        'slwt',\n",
    "        'perwt',\n",
    "        'birthyr',\n",
    "        'occ',\n",
    "        'namelast',\n",
    "        'namefrst',\n",
    "        'bplstr',\n",
    "        'agemonth',\n",
    "        'blind',\n",
    "        'deaf',\n",
    "        'idiotic',\n",
    "        'insane',\n",
    "        'crime',\n",
    "        'pauper',\n",
    "        'sursim',\n",
    "        'impmom',\n",
    "        'qlit',\n",
    "        'qmarinyr',\n",
    "        'histid',\n",
    "        'pid',\n",
    "        'us1850c_1000',\n",
    "        'us1850c_1001',\n",
    "        'us1850c_1002',\n",
    "        'us1850c_1003',\n",
    "        'us1850c_1004',\n",
    "        'us1850c_1005',\n",
    "        'us1850c_1006',\n",
    "        'us1850c_1007',\n",
    "        'us1850c_1008',\n",
    "        'us1850c_1009',\n",
    "        'us1850c_1010',\n",
    "        'us1850c_1011',\n",
    "        'us1850c_1012',\n",
    "        'us1850c_1013',\n",
    "        'us1850c_1014',\n",
    "        'us1850c_1015',\n",
    "        'us1850c_1016',\n",
    "        'us1850c_1017',\n",
    "        'us1850c_1018',\n",
    "        'us1850c_1019',\n",
    "        'us1850c_1020',\n",
    "        'us1850c_1021',\n",
    "        'us1850c_1022',\n",
    "        'us1850c_1023',\n",
    "        'us1850c_1024',\n",
    "        'us1850c_1025',\n",
    "        'us1850c_1026',\n",
    "        'us1850c_1027',\n",
    "        'us1850c_1028',\n",
    "        'us1850c_1029',\n",
    "        'us1850c_1030',\n",
    "        'us1850c_1031',\n",
    "        'us1850c_1032',\n",
    "        'us1850c_1033',\n",
    "        'us1850c_1034',\n",
    "        'us1850c_1035',\n",
    "        'us1850c_1036',\n",
    "        'us1850c_1037',\n",
    "        'us1850c_1038',\n",
    "        'us1850c_1039',\n",
    "        'us1850c_1040',\n",
    "        'us1850c_1041',\n",
    "        'us1850c_1042',\n",
    "        'us1850c_1043',\n",
    "        'us1850c_1044',\n",
    "        'us1850c_1045',\n",
    "        'us1850c_1046',\n",
    "        'us1850c_1047',\n",
    "        'us1850c_1048',\n",
    "        'us1850c_1049',\n",
    "        'us1850c_1050',\n",
    "        'us1850c_1051',\n",
    "        'us1850c_1052',\n",
    "        'us1850c_1053',\n",
    "    ],\n",
    "    dtype={\n",
    "        'rectype': object,\n",
    "        'yearp': object,\n",
    "        'samplep': object,\n",
    "        'serialp': object,\n",
    "        'pernum': object,\n",
    "        'momloc': object,\n",
    "        'stepmom': object,\n",
    "        'momrule_hist': object,\n",
    "        'poploc': object,\n",
    "        'steppop': object,\n",
    "        'poprule_hist': object,\n",
    "        'sploc': object,\n",
    "        'sprule_hist': object,\n",
    "        'famsize': object,\n",
    "        'nchild': object,\n",
    "        'nchlt5': object,\n",
    "        'famunit': object,\n",
    "        'eldch': object,\n",
    "        'yngch': object,\n",
    "        'nsibs': object,\n",
    "        'relate': object,\n",
    "        'age': object,\n",
    "        'sex': object,\n",
    "        'race': object,\n",
    "        'marst': object,\n",
    "        'marrinyr': object,\n",
    "        'bpl': object,\n",
    "        'hispan': object,\n",
    "        'spanname': object,\n",
    "        'school': object,\n",
    "        'lit': object,\n",
    "        'labforce': object,\n",
    "        'occ1950': object,\n",
    "        'occscore': object,\n",
    "        'sei': object,\n",
    "        'ind1950': object,\n",
    "        'realprop': object,\n",
    "        'imppop': object,\n",
    "        'impsp': object,\n",
    "        'imprel': object,\n",
    "        'qage': object,\n",
    "        'qagemont': object,\n",
    "        'qbpl': object,\n",
    "        'qocc': object,\n",
    "        'qrace': object,\n",
    "        'qschool': object,\n",
    "        'qsex': object,\n",
    "        'racamind': object,\n",
    "        'racasian': object,\n",
    "        'racblk': object,\n",
    "        'racpacis': object,\n",
    "        'racother': object,\n",
    "        'racwht': object,\n",
    "        'agediff': object,\n",
    "        'racesing': object,\n",
    "        'probwht': object,\n",
    "        'proboth': object,\n",
    "        'probblk': object,\n",
    "        'probapi': object,\n",
    "        'probai': object,\n",
    "        'hisprule': object,\n",
    "        'presgl': object,\n",
    "        'erscor50': object,\n",
    "        'edscor50': object,\n",
    "        'npboss50': object,\n",
    "        'occstr': object,\n",
    "        'isrelate': object,\n",
    "        'slwt': object,\n",
    "        'perwt': object,\n",
    "        'birthyr': object,\n",
    "        'occ': object,\n",
    "        'namelast': object,\n",
    "        'namefrst': object,\n",
    "        'bplstr': object,\n",
    "        'agemonth': object,\n",
    "        'blind': object,\n",
    "        'deaf': object,\n",
    "        'idiotic': object,\n",
    "        'insane': object,\n",
    "        'crime': object,\n",
    "        'pauper': object,\n",
    "        'sursim': object,\n",
    "        'impmom': object,\n",
    "        'qlit': object,\n",
    "        'qmarinyr': object,\n",
    "        'histid': object,\n",
    "        'pid': object,\n",
    "        'us1850c_1000': object,\n",
    "        'us1850c_1001': object,\n",
    "        'us1850c_1002': object,\n",
    "        'us1850c_1003': object,\n",
    "        'us1850c_1004': object,\n",
    "        'us1850c_1005': object,\n",
    "        'us1850c_1006': object,\n",
    "        'us1850c_1007': object,\n",
    "        'us1850c_1008': object,\n",
    "        'us1850c_1009': object,\n",
    "        'us1850c_1010': object,\n",
    "        'us1850c_1011': object,\n",
    "        'us1850c_1012': object,\n",
    "        'us1850c_1013': object,\n",
    "        'us1850c_1014': object,\n",
    "        'us1850c_1015': object,\n",
    "        'us1850c_1016': object,\n",
    "        'us1850c_1017': object,\n",
    "        'us1850c_1018': object,\n",
    "        'us1850c_1019': object,\n",
    "        'us1850c_1020': object,\n",
    "        'us1850c_1021': object,\n",
    "        'us1850c_1022': object,\n",
    "        'us1850c_1023': object,\n",
    "        'us1850c_1024': object,\n",
    "        'us1850c_1025': object,\n",
    "        'us1850c_1026': object,\n",
    "        'us1850c_1027': object,\n",
    "        'us1850c_1028': object,\n",
    "        'us1850c_1029': object,\n",
    "        'us1850c_1030': object,\n",
    "        'us1850c_1031': object,\n",
    "        'us1850c_1032': object,\n",
    "        'us1850c_1033': object,\n",
    "        'us1850c_1034': object,\n",
    "        'us1850c_1035': object,\n",
    "        'us1850c_1036': object,\n",
    "        'us1850c_1037': object,\n",
    "        'us1850c_1038': object,\n",
    "        'us1850c_1039': object,\n",
    "        'us1850c_1040': object,\n",
    "        'us1850c_1041': object,\n",
    "        'us1850c_1042': object,\n",
    "        'us1850c_1043': object,\n",
    "        'us1850c_1044': object,\n",
    "        'us1850c_1045': object,\n",
    "        'us1850c_1046': object,\n",
    "        'us1850c_1047': object,\n",
    "        'us1850c_1048': object,\n",
    "        'us1850c_1049': object,\n",
    "        'us1850c_1050': object,\n",
    "        'us1850c_1051': object,\n",
    "        'us1850c_1052': object,\n",
    "        'us1850c_1053': object,\n",
    "    },\n",
    "    usecols=['histid', 'namefrst', 'namelast']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"/Volumes/External/1850_3-2\", sep=',', index=False, encoding=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = '/Volumes/External/1850_3-2/1850.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = ['histid', 'namefrst', 'namelast']\n",
    "chunksize = 12 ** 6 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = pd.read_csv(\"file.dat\", \n",
    "                     sep=',', index_col = None,\n",
    "                     iterator = True, chunksize = 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "batch_no=1\n",
    "for chunk in pd.read_csv(data, sep=',', usecols=lista, chunksize=chunksize):\n",
    "    chunk.to_csv('chunk'+str(batch_no)+'.csv',index=False)\n",
    "    batch_no+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loc = '/Users/bcornejocostas/Desktop/surfdrive/OneDrive_1_07-06-2021/IPUMS/Codes/chunk1.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data_loc, sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>namelast</th>\n",
       "      <th>namefrst</th>\n",
       "      <th>histid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>NANCY</td>\n",
       "      <td>a5609a08-0156-452a-b3e3-184d1ce86c01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>ANN E</td>\n",
       "      <td>5baf7c80-f4eb-4d1b-bb14-7c5e953f4116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>WILEY</td>\n",
       "      <td>b9be7b78-c574-4520-a95a-bf75fdd3b33a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   namelast namefrst                                histid\n",
       "0       NaN      NaN                                   NaN\n",
       "1  LASSITER    NANCY  a5609a08-0156-452a-b3e3-184d1ce86c01\n",
       "2  LASSITER    ANN E  5baf7c80-f4eb-4d1b-bb14-7c5e953f4116\n",
       "3  LASSITER    WILEY  b9be7b78-c574-4520-a95a-bf75fdd3b33a\n",
       "4       NaN      NaN                                   NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\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>namelast</th>\n",
       "      <th>namefrst</th>\n",
       "      <th>histid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>NANCY</td>\n",
       "      <td>a5609a08-0156-452a-b3e3-184d1ce86c01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>ANN E</td>\n",
       "      <td>5baf7c80-f4eb-4d1b-bb14-7c5e953f4116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>WILEY</td>\n",
       "      <td>b9be7b78-c574-4520-a95a-bf75fdd3b33a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>CHARLES J</td>\n",
       "      <td>71341c11-6b0b-4ef9-9eb5-dcbdadeec5bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LASSITER</td>\n",
       "      <td>ELIZABETH</td>\n",
       "      <td>2abf6c28-d644-49dc-912b-5cc2e7ea7cc1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   namelast   namefrst                                histid\n",
       "1  LASSITER      NANCY  a5609a08-0156-452a-b3e3-184d1ce86c01\n",
       "2  LASSITER      ANN E  5baf7c80-f4eb-4d1b-bb14-7c5e953f4116\n",
       "3  LASSITER      WILEY  b9be7b78-c574-4520-a95a-bf75fdd3b33a\n",
       "5  LASSITER  CHARLES J  71341c11-6b0b-4ef9-9eb5-dcbdadeec5bb\n",
       "6  LASSITER  ELIZABETH  2abf6c28-d644-49dc-912b-5cc2e7ea7cc1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2985984, 3)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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