split_large_csv_into_smaller_csvs.ipynb

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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "#!/usr/bin/env python3\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>work_title</th>\n",
       "      <th>sentence</th>\n",
       "      <th>sentence_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>சிந்தனை_துளிகள்</td>\n",
       "      <td>முதல் பதிப்பு ஜூலை பக்கம் விலை ரூ. பதிப்பாசிரி...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>சிந்தனை_துளிகள்</td>\n",
       "      <td>அதுபோல நெருக்கடியும் துன்பங்களும் இல்லாத பொழுத...</td>\n",
       "      <td>11</td>\n",
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      "text/plain": [
       "        work_title                                           sentence  \\\n",
       "0  சிந்தனை_துளிகள்  முதல் பதிப்பு ஜூலை பக்கம் விலை ரூ. பதிப்பாசிரி...   \n",
       "1  சிந்தனை_துளிகள்  அதுபோல நெருக்கடியும் துன்பங்களும் இல்லாத பொழுத...   \n",
       "\n",
       "   sentence_length  \n",
       "0               13  \n",
       "1               11  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"ta_wikisource_sentences.csv\")\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"id\"] = df.index + 1\n",
    "df[\"validation_status\"] = \"\"\n",
    "df[\"validation_issue\"] = \"\"\n",
    "df[\"notes\"] = \"\"\n",
    "df = df[['id', 'work_title', 'sentence_length', 'sentence', 'validation_status', 'validation_issue', 'notes']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[0:49999].to_csv(\"split_csvs/1_50000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[49999:99999].to_csv(\"split_csvs/50000_100000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[99999:149999].to_csv(\"split_csvs/100000_150000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[149999:199999].to_csv(\"split_csvs/150000_200000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[199999:249999].to_csv(\"split_csvs/200000_250000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[249999:299999].to_csv(\"split_csvs/250000_300000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[299999:349999].to_csv(\"split_csvs/300000_350000.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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