notebooks/00-explore-data.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_orig = pd.read_csv(\"../data/bbc-news-data.csv\", delimiter='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>business</td>\n",
       "      <td>001.txt</td>\n",
       "      <td>Ad sales boost Time Warner profit</td>\n",
       "      <td>Quarterly profits at US media giant TimeWarne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>business</td>\n",
       "      <td>002.txt</td>\n",
       "      <td>Dollar gains on Greenspan speech</td>\n",
       "      <td>The dollar has hit its highest level against ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>business</td>\n",
       "      <td>003.txt</td>\n",
       "      <td>Yukos unit buyer faces loan claim</td>\n",
       "      <td>The owners of embattled Russian oil giant Yuk...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>business</td>\n",
       "      <td>004.txt</td>\n",
       "      <td>High fuel prices hit BA's profits</td>\n",
       "      <td>British Airways has blamed high fuel prices f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>business</td>\n",
       "      <td>005.txt</td>\n",
       "      <td>Pernod takeover talk lifts Domecq</td>\n",
       "      <td>Shares in UK drinks and food firm Allied Dome...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>tech</td>\n",
       "      <td>397.txt</td>\n",
       "      <td>BT program to beat dialler scams</td>\n",
       "      <td>BT is introducing two initiatives to help bea...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>tech</td>\n",
       "      <td>398.txt</td>\n",
       "      <td>Spam e-mails tempt net shoppers</td>\n",
       "      <td>Computer users across the world continue to i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>tech</td>\n",
       "      <td>399.txt</td>\n",
       "      <td>Be careful how you code</td>\n",
       "      <td>A new European directive could put software w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>tech</td>\n",
       "      <td>400.txt</td>\n",
       "      <td>US cyber security chief resigns</td>\n",
       "      <td>The man making sure US computer networks are ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>tech</td>\n",
       "      <td>401.txt</td>\n",
       "      <td>Losing yourself in online gaming</td>\n",
       "      <td>Online role playing games are time-consuming,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category filename                              title  \\\n",
       "0     business  001.txt  Ad sales boost Time Warner profit   \n",
       "1     business  002.txt   Dollar gains on Greenspan speech   \n",
       "2     business  003.txt  Yukos unit buyer faces loan claim   \n",
       "3     business  004.txt  High fuel prices hit BA's profits   \n",
       "4     business  005.txt  Pernod takeover talk lifts Domecq   \n",
       "...        ...      ...                                ...   \n",
       "2220      tech  397.txt   BT program to beat dialler scams   \n",
       "2221      tech  398.txt    Spam e-mails tempt net shoppers   \n",
       "2222      tech  399.txt            Be careful how you code   \n",
       "2223      tech  400.txt    US cyber security chief resigns   \n",
       "2224      tech  401.txt   Losing yourself in online gaming   \n",
       "\n",
       "                                                content  \n",
       "0      Quarterly profits at US media giant TimeWarne...  \n",
       "1      The dollar has hit its highest level against ...  \n",
       "2      The owners of embattled Russian oil giant Yuk...  \n",
       "3      British Airways has blamed high fuel prices f...  \n",
       "4      Shares in UK drinks and food firm Allied Dome...  \n",
       "...                                                 ...  \n",
       "2220   BT is introducing two initiatives to help bea...  \n",
       "2221   Computer users across the world continue to i...  \n",
       "2222   A new European directive could put software w...  \n",
       "2223   The man making sure US computer networks are ...  \n",
       "2224   Online role playing games are time-consuming,...  \n",
       "\n",
       "[2225 rows x 4 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df_orig.copy()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>2225</td>\n",
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       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>511</td>\n",
       "      <td>2096</td>\n",
       "      <td>2092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>sport</td>\n",
       "      <td>221.txt</td>\n",
       "      <td>SEC to rethink post-Enron rules</td>\n",
       "      <td>The US created fewer jobs than expected in Ja...</td>\n",
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      "text/plain": [
       "       category filename                            title  \\\n",
       "count      2225     2225                             2225   \n",
       "unique        5      511                             2096   \n",
       "top       sport  221.txt  SEC to rethink post-Enron rules   \n",
       "freq        511        5                                2   \n",
       "\n",
       "                                                  content  \n",
       "count                                                2225  \n",
       "unique                                               2092  \n",
       "top      The US created fewer jobs than expected in Ja...  \n",
       "freq                                                    2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['business' 'entertainment' 'politics' 'sport' 'tech']\n",
      "['001.txt' '002.txt' '003.txt' '004.txt' '005.txt' '006.txt' '007.txt'\n",
      " '008.txt' '009.txt' '010.txt' '011.txt' '012.txt' '013.txt' '014.txt'\n",
      " '015.txt' '016.txt' '017.txt' '018.txt' '019.txt' '020.txt' '021.txt'\n",
      " '022.txt' '023.txt' '024.txt' '025.txt' '026.txt' '027.txt' '028.txt'\n",
      " '029.txt' '030.txt' '031.txt' '032.txt' '033.txt' '034.txt' '035.txt'\n",
      " '036.txt' '037.txt' '038.txt' '039.txt' '040.txt' '041.txt' '042.txt'\n",
      " '043.txt' '044.txt' '045.txt' '046.txt' '047.txt' '048.txt' '049.txt'\n",
      " '050.txt' '051.txt' '052.txt' '053.txt' '054.txt' '055.txt' '056.txt'\n",
      " '057.txt' '058.txt' '059.txt' '060.txt' '061.txt' '062.txt' '063.txt'\n",
      " '064.txt' '065.txt' '066.txt' '067.txt' '068.txt' '069.txt' '070.txt'\n",
      " '071.txt' '072.txt' '073.txt' '074.txt' '075.txt' '076.txt' '077.txt'\n",
      " '078.txt' '079.txt' '080.txt' '081.txt' '082.txt' '083.txt' '084.txt'\n",
      " '085.txt' '086.txt' '087.txt' '088.txt' '089.txt' '090.txt' '091.txt'\n",
      " '092.txt' '093.txt' '094.txt' '095.txt' '096.txt' '097.txt' '098.txt'\n",
      " '099.txt' '100.txt' '101.txt' '102.txt' '103.txt' '104.txt' '105.txt'\n",
      " '106.txt' '107.txt' '108.txt' '109.txt' '110.txt' '111.txt' '112.txt'\n",
      " '113.txt' '114.txt' '115.txt' '116.txt' '117.txt' '118.txt' '119.txt'\n",
      " '120.txt' '121.txt' '122.txt' '123.txt' '124.txt' '125.txt' '126.txt'\n",
      " '127.txt' '128.txt' '129.txt' '130.txt' '131.txt' '132.txt' '133.txt'\n",
      " '134.txt' '135.txt' '136.txt' '137.txt' '138.txt' '139.txt' '140.txt'\n",
      " '141.txt' '142.txt' '143.txt' '144.txt' '145.txt' '146.txt' '147.txt'\n",
      " '148.txt' '149.txt' '150.txt' '151.txt' '152.txt' '153.txt' '154.txt'\n",
      " '155.txt' '156.txt' '157.txt' '158.txt' '159.txt' '160.txt' '161.txt'\n",
      " '162.txt' '163.txt' '164.txt' '165.txt' '166.txt' '167.txt' '168.txt'\n",
      " '169.txt' '170.txt' '171.txt' '172.txt' '173.txt' '174.txt' '175.txt'\n",
      " '176.txt' '177.txt' '178.txt' '179.txt' '180.txt' '181.txt' '182.txt'\n",
      " '183.txt' '184.txt' '185.txt' '186.txt' '187.txt' '188.txt' '189.txt'\n",
      " '190.txt' '191.txt' '192.txt' '193.txt' '194.txt' '195.txt' '196.txt'\n",
      " '197.txt' '198.txt' '199.txt' '200.txt' '201.txt' '202.txt' '203.txt'\n",
      " '204.txt' '205.txt' '206.txt' '207.txt' '208.txt' '209.txt' '210.txt'\n",
      " '211.txt' '212.txt' '213.txt' '214.txt' '215.txt' '216.txt' '217.txt'\n",
      " '218.txt' '219.txt' '220.txt' '221.txt' '222.txt' '223.txt' '224.txt'\n",
      " '225.txt' '226.txt' '227.txt' '228.txt' '229.txt' '230.txt' '231.txt'\n",
      " '232.txt' '233.txt' '234.txt' '235.txt' '236.txt' '237.txt' '238.txt'\n",
      " '239.txt' '240.txt' '241.txt' '242.txt' '243.txt' '244.txt' '245.txt'\n",
      " '246.txt' '247.txt' '248.txt' '249.txt' '250.txt' '251.txt' '252.txt'\n",
      " '253.txt' '254.txt' '255.txt' '256.txt' '257.txt' '258.txt' '259.txt'\n",
      " '260.txt' '261.txt' '262.txt' '263.txt' '264.txt' '265.txt' '266.txt'\n",
      " '267.txt' '268.txt' '269.txt' '270.txt' '271.txt' '272.txt' '273.txt'\n",
      " '274.txt' '275.txt' '276.txt' '277.txt' '278.txt' '279.txt' '280.txt'\n",
      " '281.txt' '282.txt' '283.txt' '284.txt' '285.txt' '286.txt' '287.txt'\n",
      " '288.txt' '289.txt' '290.txt' '291.txt' '292.txt' '293.txt' '294.txt'\n",
      " '295.txt' '296.txt' '297.txt' '298.txt' '299.txt' '300.txt' '301.txt'\n",
      " '302.txt' '303.txt' '304.txt' '305.txt' '306.txt' '307.txt' '308.txt'\n",
      " '309.txt' '310.txt' '311.txt' '312.txt' '313.txt' '314.txt' '315.txt'\n",
      " '316.txt' '317.txt' '318.txt' '319.txt' '320.txt' '321.txt' '322.txt'\n",
      " '323.txt' '324.txt' '325.txt' '326.txt' '327.txt' '328.txt' '329.txt'\n",
      " '330.txt' '331.txt' '332.txt' '333.txt' '334.txt' '335.txt' '336.txt'\n",
      " '337.txt' '338.txt' '339.txt' '340.txt' '341.txt' '342.txt' '343.txt'\n",
      " '344.txt' '345.txt' '346.txt' '347.txt' '348.txt' '349.txt' '350.txt'\n",
      " '351.txt' '352.txt' '353.txt' '354.txt' '355.txt' '356.txt' '357.txt'\n",
      " '358.txt' '359.txt' '360.txt' '361.txt' '362.txt' '363.txt' '364.txt'\n",
      " '365.txt' '366.txt' '367.txt' '368.txt' '369.txt' '370.txt' '371.txt'\n",
      " '372.txt' '373.txt' '374.txt' '375.txt' '376.txt' '377.txt' '378.txt'\n",
      " '379.txt' '380.txt' '381.txt' '382.txt' '383.txt' '384.txt' '385.txt'\n",
      " '386.txt' '387.txt' '388.txt' '389.txt' '390.txt' '391.txt' '392.txt'\n",
      " '393.txt' '394.txt' '395.txt' '396.txt' '397.txt' '398.txt' '399.txt'\n",
      " '400.txt' '401.txt' '402.txt' '403.txt' '404.txt' '405.txt' '406.txt'\n",
      " '407.txt' '408.txt' '409.txt' '410.txt' '411.txt' '412.txt' '413.txt'\n",
      " '414.txt' '415.txt' '416.txt' '417.txt' '418.txt' '419.txt' '420.txt'\n",
      " '421.txt' '422.txt' '423.txt' '424.txt' '425.txt' '426.txt' '427.txt'\n",
      " '428.txt' '429.txt' '430.txt' '431.txt' '432.txt' '433.txt' '434.txt'\n",
      " '435.txt' '436.txt' '437.txt' '438.txt' '439.txt' '440.txt' '441.txt'\n",
      " '442.txt' '443.txt' '444.txt' '445.txt' '446.txt' '447.txt' '448.txt'\n",
      " '449.txt' '450.txt' '451.txt' '452.txt' '453.txt' '454.txt' '455.txt'\n",
      " '456.txt' '457.txt' '458.txt' '459.txt' '460.txt' '461.txt' '462.txt'\n",
      " '463.txt' '464.txt' '465.txt' '466.txt' '467.txt' '468.txt' '469.txt'\n",
      " '470.txt' '471.txt' '472.txt' '473.txt' '474.txt' '475.txt' '476.txt'\n",
      " '477.txt' '478.txt' '479.txt' '480.txt' '481.txt' '482.txt' '483.txt'\n",
      " '484.txt' '485.txt' '486.txt' '487.txt' '488.txt' '489.txt' '490.txt'\n",
      " '491.txt' '492.txt' '493.txt' '494.txt' '495.txt' '496.txt' '497.txt'\n",
      " '498.txt' '499.txt' '500.txt' '501.txt' '502.txt' '503.txt' '504.txt'\n",
      " '505.txt' '506.txt' '507.txt' '508.txt' '509.txt' '510.txt' '511.txt']\n"
     ]
    }
   ],
   "source": [
    "for col in ['category', 'filename']:\n",
    "    print(df[col].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>business</td>\n",
       "      <td>001.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>business</td>\n",
       "      <td>002.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>business</td>\n",
       "      <td>003.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>business</td>\n",
       "      <td>004.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>business</td>\n",
       "      <td>005.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>tech</td>\n",
       "      <td>397.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>tech</td>\n",
       "      <td>398.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>tech</td>\n",
       "      <td>399.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>tech</td>\n",
       "      <td>400.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>tech</td>\n",
       "      <td>401.txt</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2225 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category filename\n",
       "0     business  001.txt\n",
       "1     business  002.txt\n",
       "2     business  003.txt\n",
       "3     business  004.txt\n",
       "4     business  005.txt\n",
       "...        ...      ...\n",
       "2220      tech  397.txt\n",
       "2221      tech  398.txt\n",
       "2222      tech  399.txt\n",
       "2223      tech  400.txt\n",
       "2224      tech  401.txt\n",
       "\n",
       "[2225 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['category', 'filename']]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fd216d97370>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create bar plot to visualize frequency of each team\n",
    "df['category'].value_counts().plot(kind='bar', xlabel='Category', ylabel='Articles Count', rot=0, title='Number of articles per category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Length "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>filename</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>business</td>\n",
       "      <td>001.txt</td>\n",
       "      <td>Ad sales boost Time Warner profit</td>\n",
       "      <td>Quarterly profits at US media giant TimeWarne...</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>business</td>\n",
       "      <td>002.txt</td>\n",
       "      <td>Dollar gains on Greenspan speech</td>\n",
       "      <td>The dollar has hit its highest level against ...</td>\n",
       "      <td>379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>business</td>\n",
       "      <td>003.txt</td>\n",
       "      <td>Yukos unit buyer faces loan claim</td>\n",
       "      <td>The owners of embattled Russian oil giant Yuk...</td>\n",
       "      <td>258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>business</td>\n",
       "      <td>004.txt</td>\n",
       "      <td>High fuel prices hit BA's profits</td>\n",
       "      <td>British Airways has blamed high fuel prices f...</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>business</td>\n",
       "      <td>005.txt</td>\n",
       "      <td>Pernod takeover talk lifts Domecq</td>\n",
       "      <td>Shares in UK drinks and food firm Allied Dome...</td>\n",
       "      <td>260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2220</th>\n",
       "      <td>tech</td>\n",
       "      <td>397.txt</td>\n",
       "      <td>BT program to beat dialler scams</td>\n",
       "      <td>BT is introducing two initiatives to help bea...</td>\n",
       "      <td>414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2221</th>\n",
       "      <td>tech</td>\n",
       "      <td>398.txt</td>\n",
       "      <td>Spam e-mails tempt net shoppers</td>\n",
       "      <td>Computer users across the world continue to i...</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2222</th>\n",
       "      <td>tech</td>\n",
       "      <td>399.txt</td>\n",
       "      <td>Be careful how you code</td>\n",
       "      <td>A new European directive could put software w...</td>\n",
       "      <td>1045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2223</th>\n",
       "      <td>tech</td>\n",
       "      <td>400.txt</td>\n",
       "      <td>US cyber security chief resigns</td>\n",
       "      <td>The man making sure US computer networks are ...</td>\n",
       "      <td>374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2224</th>\n",
       "      <td>tech</td>\n",
       "      <td>401.txt</td>\n",
       "      <td>Losing yourself in online gaming</td>\n",
       "      <td>Online role playing games are time-consuming,...</td>\n",
       "      <td>2964</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2225 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      category filename                              title  \\\n",
       "0     business  001.txt  Ad sales boost Time Warner profit   \n",
       "1     business  002.txt   Dollar gains on Greenspan speech   \n",
       "2     business  003.txt  Yukos unit buyer faces loan claim   \n",
       "3     business  004.txt  High fuel prices hit BA's profits   \n",
       "4     business  005.txt  Pernod takeover talk lifts Domecq   \n",
       "...        ...      ...                                ...   \n",
       "2220      tech  397.txt   BT program to beat dialler scams   \n",
       "2221      tech  398.txt    Spam e-mails tempt net shoppers   \n",
       "2222      tech  399.txt            Be careful how you code   \n",
       "2223      tech  400.txt    US cyber security chief resigns   \n",
       "2224      tech  401.txt   Losing yourself in online gaming   \n",
       "\n",
       "                                                content  word_count  \n",
       "0      Quarterly profits at US media giant TimeWarne...         415  \n",
       "1      The dollar has hit its highest level against ...         379  \n",
       "2      The owners of embattled Russian oil giant Yuk...         258  \n",
       "3      British Airways has blamed high fuel prices f...         400  \n",
       "4      Shares in UK drinks and food firm Allied Dome...         260  \n",
       "...                                                 ...         ...  \n",
       "2220   BT is introducing two initiatives to help bea...         414  \n",
       "2221   Computer users across the world continue to i...         352  \n",
       "2222   A new European directive could put software w...        1045  \n",
       "2223   The man making sure US computer networks are ...         374  \n",
       "2224   Online role playing games are time-consuming,...        2964  \n",
       "\n",
       "[2225 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"word_count\"] = df[\"content\"].apply(lambda n: len(n.split()))\n",
    "df"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### plot `word_count` per `category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7fd202d3a6d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.catplot(data=df, x=\"category\", y=\"word_count\", kind=\"box\", height=5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_csv(\"../data/bbc-news-data-00.csv\", sep='\\t')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "azureml_py38",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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