Notebook up till cleaning datasets and other goodies

jupyter-notes/Panda Bamboo.ipynb

@@ -11,6 +11,125 @@
     "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": []
   }
  ],
  "metadata": {
@@ -20,8 +139,16 @@
    "name": "python3"
   },
   "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
    "name": "python",
-   "version": "3.10.2"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
   },
   "orig_nbformat": 4
  },