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 "cells": [
  {
   "attachments": {},
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   "source": [
    "# Pandas Crash Course\n",
    "As usual, documenting what is being used from pandas here ig\n",
    "\n",
    "Docs:\n",
    "- https://pandas.pydata.org/docs/getting_started/index.html#getting-started"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning Game/Score/Rating Dataset\n",
    "Error found: Game Names had Reviews attached to them"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Game Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "filename = askopenfilename()\n",
    "df1= pd.read_csv(filename)\n",
    "from tkinter.filedialog import askopenfilename"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Cleaning:  Removing the word review and anything after it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unclean showcase\n",
    "unclean = df1\n",
    "#limit this output 3 rows pls\n",
    "print(unclean[['GameName']].head(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                       Name\n",
      "0             Baldur's Gate 3 Early Access \n",
      "1  Control: Ultimate Edition Cloud Version \n",
      "2    Doom Eternal: The Ancient Gods Part 1 \n",
      "3                       Watch Dogs: Legion \n",
      "4                             Ring Of Pain \n",
      "5                          Pikmin 3 Deluxe \n",
      "6                              Ghostrunner \n",
      "7                                Disc Room \n",
      "8                                   NHL 21 \n",
      "9                                    Noita \n"
     ]
    }
   ],
   "source": [
    "# CLeaning\n",
    "nuke=df1['GameName'].to_list()\n",
    "nuke2 = list()\n",
    "\n",
    "for orphan in nuke : \n",
    "    orphan = orphan.split('Review')[0]\n",
    "    nuke2.append(orphan)\n",
    "\n",
    "df1['GameName']=nuke\n",
    "\n",
    "\n",
    "\n",
    "nuke_frame = pd.DataFrame(nuke2)\n",
    "clean=df1.drop(columns=['GameName'])\n",
    "\n",
    "clean['Name'] = nuke2\n",
    "#limit this output 3 rows pls\n",
    "print(clean[['Name']].head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CSV output\n",
    "df1.to_csv('cleaned_games.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrating Game Sales with the previous cleaned data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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