clean_items.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import config\n",
    "\n",
    "df = pd.read_csv(config.WAREHOUSE_PATH+\"/items.csv\")\n",
    "\n",
    "df = df.drop(columns=\"Unnamed: 0\")\n",
    "df = df.rename({'id' : \"malID\"}, axis=1)\n",
    "df.reset_index(inplace=True)\n",
    "def remove_blanks(col):\n",
    "    return col.str.replace(\" \", \"\")\n",
    "\n",
    "def remove_double_words(s):\n",
    "    words = s.split(\",\")\n",
    "    half = lambda x: x[:len(x)//2]\n",
    "    return  \",\".join(map(half, words))\n",
    "\n",
    "def categorical2onehot(column, prefix, rmd=True,):\n",
    "    column = remove_blanks(column)\n",
    "    column.fillna(\"\", inplace=True)\n",
    "    if rmd:\n",
    "        column = column.apply(remove_double_words)\n",
    "    one_hot_encoded = column.str.get_dummies(',').astype('int8').add_prefix(prefix, axis=1)\n",
    "    return one_hot_encoded\n",
    "\n",
    "df\n",
    "\n",
    "one_hot_encoded = categorical2onehot(df.genres, \"OH_genre_\")\n",
    "df = pd.concat([df, one_hot_encoded], axis=1)\n",
    "one_hot_encoded = categorical2onehot(df.themes, \"OH_theme_\")\n",
    "df = pd.concat([df, one_hot_encoded], axis=1)\n",
    "one_hot_encoded = categorical2onehot(df.demographic, \"OH_dem_\")\n",
    "df = pd.concat([df, one_hot_encoded], axis=1)\n",
    "\n",
    "df.type = remove_blanks(df.type)\n",
    "\n",
    "df.to_csv(config.WAREHOUSE_PATH+\"/clean_items.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 96,
     "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": [
    "df_genres = df.filter(regex=r\"^OH_dem_\")\n",
    "df_genres.sum().plot.bar()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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