data-cleaning-rees46-purchase.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime as dt\n",
    "import os\n",
    "from constants import (DATASET_SALE, EXPORT_DIR, T, DATA_DIR, VAL_THRESHOLD, TEST_THRESHOLD)\n",
    "# from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "FONT_SIZE = 24\n",
    "plt.rcParams['figure.figsize'] = (20,8)\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.size'] = FONT_SIZE\n",
    "plt.rcParams['legend.fontsize'] = FONT_SIZE\n",
    "plt.rcParams['xtick.labelsize'] = FONT_SIZE\n",
    "plt.rcParams['ytick.labelsize'] = FONT_SIZE\n",
    "%config InlineBackend.figure_format ='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 37.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "df = pd.read_csv(DATASET_SALE+'kz.csv',engine='c', sep=',',usecols=[\"product_id\",\"event_time\",\"user_id\",'category_code'],\n",
    "                 dtype={'product_id': np.int64, 'user_id': np.float64, 'category_code': str},\n",
    "                 infer_datetime_format=True, parse_dates=[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2633521, 4)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop guest users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have \u001b[91m0\u001b[0m events with user_id being \u001b[1m0\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "print(f'We have {T.R}{len(df[df[\"user_id\"]==0]):,}{T.E} events with user_id being {T.b}0{T.E}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = df[\"user_id\"].isna()\n",
    "df.loc[mask,\"user_id\"] = 0\n",
    "df[\"user_id\"] = df[\"user_id\"].astype(\"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we have \u001b[92m2,069,352\u001b[0m events with user_id being \u001b[1m0\u001b[0m, meaning guest users.\n"
     ]
    }
   ],
   "source": [
    "print(f'Now we have {T.G}{len(df[df[\"user_id\"]==0]):,}{T.E} events with user_id being {T.b}0{T.E}, meaning guest users.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop guest users\n",
    "df = df[df[\"user_id\"]!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(564169, 4)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop wrong event time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.event_time>'2019']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(562862, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop missing categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(433938, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 433938 entries, 0 to 2633520\n",
      "Data columns (total 4 columns):\n",
      " #   Column         Non-Null Count   Dtype              \n",
      "---  ------         --------------   -----              \n",
      " 0   event_time     433938 non-null  datetime64[ns, UTC]\n",
      " 1   product_id     433938 non-null  int64              \n",
      " 2   category_code  433938 non-null  object             \n",
      " 3   user_id        433938 non-null  int64              \n",
      "dtypes: datetime64[ns, UTC](1), int64(2), object(1)\n",
      "memory usage: 16.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 123 categories in the dataframe slice.\n"
     ]
    }
   ],
   "source": [
    "print(f'There are {len(df.category_code.value_counts())} categories in the dataframe slice.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "electronics.smartphone                    102554\n",
       "computers.notebook                         25918\n",
       "appliances.kitchen.refrigerators           20285\n",
       "electronics.audio.headphone                20055\n",
       "electronics.video.tv                       17625\n",
       "appliances.environment.vacuum              15965\n",
       "appliances.kitchen.washer                  14565\n",
       "appliances.kitchen.kettle                  12713\n",
       "computers.peripherals.mouse                12662\n",
       "furniture.kitchen.table                    11233\n",
       "appliances.kitchen.hood                     9053\n",
       "appliances.kitchen.blender                  8668\n",
       "appliances.iron                             8233\n",
       "appliances.kitchen.microwave                7615\n",
       "appliances.environment.air_conditioner      7378\n",
       "appliances.personal.scales                  6714\n",
       "computers.network.router                    6473\n",
       "electronics.clocks                          6468\n",
       "electronics.tablet                          6393\n",
       "stationery.battery                          5183\n",
       "computers.components.hdd                    5066\n",
       "appliances.kitchen.meat_grinder             4519\n",
       "computers.components.cpu                    4139\n",
       "appliances.kitchen.oven                     4002\n",
       "appliances.environment.fan                  3857\n",
       "computers.peripherals.keyboard              3808\n",
       "appliances.environment.water_heater         3644\n",
       "computers.peripherals.printer               3465\n",
       "computers.components.cooler                 3374\n",
       "computers.peripherals.monitor               3278\n",
       "furniture.living_room.cabinet               3106\n",
       "furniture.kitchen.chair                     3078\n",
       "accessories.bag                             3059\n",
       "appliances.personal.hair_cutter             2718\n",
       "appliances.kitchen.dishwasher               2662\n",
       "appliances.environment.air_heater           2634\n",
       "appliances.kitchen.mixer                    2607\n",
       "furniture.living_room.shelving              2562\n",
       "stationery.cartrige                         2475\n",
       "electronics.telephone                       2435\n",
       "construction.tools.screw                    2411\n",
       "appliances.ironing_board                    2133\n",
       "apparel.glove                               1968\n",
       "furniture.bedroom.pillow                    1869\n",
       "appliances.personal.massager                1720\n",
       "computers.components.memory                 1603\n",
       "appliances.kitchen.grill                    1582\n",
       "appliances.sewing_machine                   1580\n",
       "furniture.living_room.chair                 1443\n",
       "appliances.kitchen.toster                   1382\n",
       "Name: category_code, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.category_code.value_counts().head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purchase fluctuation of registered users during the observed daterange"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "fluct = df.loc[:,['event_time','user_id']]\n",
    "fluct['day'] = fluct['event_time'].apply(lambda s: str(s)[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "day              \n",
       "2020-01-05    325\n",
       "2020-01-06    321\n",
       "2020-01-07    373"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_visitors = fluct.drop_duplicates().groupby(['day'])['user_id'].agg(['count']).sort_values(by=['day'], ascending=True)\n",
    "daily_visitors.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-19</th>\n",
       "      <td>4173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-20</th>\n",
       "      <td>512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-21</th>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count\n",
       "day              \n",
       "2020-11-19   4173\n",
       "2020-11-20    512\n",
       "2020-11-21    223"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_visitors.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = pd.Series(daily_visitors.index.values).apply(lambda s: dt.strptime(s, '%Y-%m-%d').date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 468,
       "width": 1168
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.subplot()\n",
    "\n",
    "plt.plot(X,daily_visitors['count'],color='k')\n",
    "plt.ylim((0,daily_visitors['count'].max()*1.05))\n",
    "plt.axhline(y = daily_visitors.mean()[0],linestyle='--', color='k')\n",
    "\n",
    "for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
    "    label.set_fontsize(14)\n",
    "\n",
    "plt.legend(['Daily purchases by registered users', r\"$\\bar{y}=$\"+f\"{daily_visitors.mean()[0]:.2f}\"])\n",
    "plt.savefig(EXPORT_DIR+'fe-daily-purchases-registered.png',dpi=300)\n",
    "plt.savefig(EXPORT_DIR+'fe-daily-purchases-registered.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[['cat','subcat','deepcat']] = df.category_code.str.split(\".\",expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123 unique categories\n"
     ]
    }
   ],
   "source": [
    "print(f\"{len(df.category_code.value_counts())} unique categories\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "electronics     157739\n",
       "appliances      150259\n",
       "computers        76929\n",
       "furniture        24509\n",
       "stationery        8842\n",
       "construction      3990\n",
       "accessories       3092\n",
       "apparel           2698\n",
       "kids              2307\n",
       "auto              1370\n",
       "medicine          1106\n",
       "sport              895\n",
       "country_yard       202\n",
       "Name: cat, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cat.value_counts().head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "kitchen        107771\n",
       "smartphone     102554\n",
       "environment     33580\n",
       "notebook        25918\n",
       "peripherals     25518\n",
       "audio           21634\n",
       "video           17740\n",
       "components      17552\n",
       "personal        11152\n",
       "iron             8233\n",
       "Name: subcat, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.subcat.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "refrigerators    20285\n",
       "headphone        20055\n",
       "tv               17625\n",
       "vacuum           15965\n",
       "washer           14565\n",
       "kettle           12713\n",
       "mouse            12662\n",
       "table            11233\n",
       "hood              9053\n",
       "blender           8668\n",
       "Name: deepcat, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.deepcat.value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Column Name</th>\n",
       "      <th>Number of Uniques</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cat</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>subcat</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>deepcat</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>category_code</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>product_id</td>\n",
       "      <td>12314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>user_id</td>\n",
       "      <td>82534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>event_time</td>\n",
       "      <td>330864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Column Name  Number of Uniques\n",
       "4            cat                 13\n",
       "5         subcat                 58\n",
       "6        deepcat                 79\n",
       "2  category_code                123\n",
       "1     product_id              12314\n",
       "3        user_id              82534\n",
       "0     event_time             330864"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_counts = pd.DataFrame.from_records([(col, df[col].nunique()) for col in df.columns],\n",
    "                          columns=['Column Name', 'Number of Uniques']).sort_values(by=['Number of Uniques'])\n",
    "unique_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['product_id','cat','subcat','deepcat','event_time'],inplace = True)\n",
    "df.rename(columns={\"category_code\": \"csd\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>csd</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>1515915625441993984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>electronics.tablet</td>\n",
       "      <td>1515915625441993984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>1515915625447879424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>electronics.audio.headphone</td>\n",
       "      <td>1515915625447879424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>furniture.kitchen.table</td>\n",
       "      <td>1515915625450382848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633515</th>\n",
       "      <td>electronics.video.tv</td>\n",
       "      <td>1515915625513809920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633516</th>\n",
       "      <td>electronics.smartphone</td>\n",
       "      <td>1515915625514888704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633517</th>\n",
       "      <td>electronics.smartphone</td>\n",
       "      <td>1515915625514891264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633518</th>\n",
       "      <td>appliances.personal.scales</td>\n",
       "      <td>1515915625514834176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633520</th>\n",
       "      <td>appliances.kitchen.blender</td>\n",
       "      <td>1515915625514834176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>433938 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 csd              user_id\n",
       "0                 electronics.tablet  1515915625441993984\n",
       "1                 electronics.tablet  1515915625441993984\n",
       "2        electronics.audio.headphone  1515915625447879424\n",
       "3        electronics.audio.headphone  1515915625447879424\n",
       "5            furniture.kitchen.table  1515915625450382848\n",
       "...                              ...                  ...\n",
       "2633515         electronics.video.tv  1515915625513809920\n",
       "2633516       electronics.smartphone  1515915625514888704\n",
       "2633517       electronics.smartphone  1515915625514891264\n",
       "2633518   appliances.personal.scales  1515915625514834176\n",
       "2633520   appliances.kitchen.blender  1515915625514834176\n",
       "\n",
       "[433938 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(DATA_DIR+'rees46-data-cleaned.csv', index = False)"
   ]
  }
 ],
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