flask-ui-skeleton.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone Project Flask UI"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pada capstone ini anda diminta untuk membangun sebuah Flask UI sederhana yang berisi beberapa tampilan plot dari skeleton yang sudah disediakan dan satu plot tambahan berdasarkan analisis anda.\n",
    "\n",
    "File ini dapat dimanfaatkan untuk membantu anda dalam proses wrangling dan visualization. Apabila proses wrangling sudah tepat dan hasil visualisasi sudah sesuai dengan ketentuan, anda dapat memindahkan kembali kode program yang sudah anda lengkapi ke dalam file `app.py`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Import library**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T09:56:05.789070Z",
     "start_time": "2021-02-02T09:56:03.554294Z"
    }
   },
   "outputs": [],
   "source": [
    "from flask import Flask, render_template\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from io import BytesIO\n",
    "import base64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Load data**\n",
    "\n",
    "Bacalah file `googleplaystore.csv` data dan simpan ke objek dataframe dengan nama playstore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T09:59:28.086254Z",
     "start_time": "2021-02-02T09:59:28.032673Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore = pd.read_csv(\"data/googleplaystore.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Data Cleansing** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 1:_** Hapus data yang duplikat berdasarkan kolom App, dengan tetap keep data pertama (hint : gunakan parameter subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:00:43.408513Z",
     "start_time": "2021-02-02T10:00:43.384541Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore.drop_duplicates(subset = 'App', keep='first', inplace=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagian ini untuk menghapus row 10472 karena nilai data tersebut tidak tersimpan pada kolom yang benar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:01:18.760162Z",
     "start_time": "2021-02-02T10:01:18.747974Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore.drop([10472], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 2:_** Cek tipe data kolom Category. Jika masih tersimpan dengan format tipe data yang salah, ubah ke tipe data yang sesuai\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:03:32.164554Z",
     "start_time": "2021-02-02T10:03:32.153250Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore.Category = playstore.Category.astype('category')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 3:_** Pada kolom Installs Buang tanda koma(,) dan tanda tambah(+) kemudian ubah tipe data menjadi integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:16:20.985521Z",
     "start_time": "2021-02-02T10:16:20.969640Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore.Installs = playstore['Installs'].apply(lambda x: x.replace(',',''))\n",
    "playstore.Installs = playstore['Installs'].apply(lambda x: x.replace('+',''))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagian ini untuk merapikan kolom Size, Anda tidak perlu mengubah apapun di bagian ini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:17:51.003771Z",
     "start_time": "2021-02-02T10:17:50.934201Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore['Size'].replace('Varies with device', np.nan, inplace = True ) \n",
    "playstore.Size = (playstore.Size.replace(r'[kM]+$', '', regex=True).astype(float) * \\\n",
    "             playstore.Size.str.extract(r'[\\d\\.]+([kM]+)', expand=False)\n",
    "            .fillna(1)\n",
    "            .replace(['k','M'], [10**3, 10**6]).astype(int))\n",
    "playstore['Size'].fillna(playstore.groupby('Category')['Size'].transform('mean'),inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 4:_** Pada kolom Price, buang karakater $ pada nilai Price lalu ubah tipe datanya menjadi float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:17:51.850791Z",
     "start_time": "2021-02-02T10:17:51.831652Z"
    }
   },
   "outputs": [],
   "source": [
    "playstore.Price = playstore['Price'].apply(lambda x: x.replace('$',''))\n",
    "playstore.Price = playstore.Price.astype('float64')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ubah tipe data Reviews, Size, Installs ke dalam tipe data integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:17:53.379441Z",
     "start_time": "2021-02-02T10:17:53.359741Z"
    }
   },
   "outputs": [],
   "source": [
    "##code here\n",
    "playstore[['Reviews','Size','Installs']]=playstore[['Reviews','Size','Installs']].astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:18:50.436751Z",
     "start_time": "2021-02-02T10:18:50.425327Z"
    }
   },
   "outputs": [],
   "source": [
    "df2 = playstore.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 5:_** Dataframe top_category dibuat untuk menyimpan frekuensi aplikasi untuk setiap Category. \n",
    "Gunakan crosstab untuk menghitung frekuensi aplikasi di setiap category kemudian gunakan `Jumlah`\n",
    "sebagai nama kolom dan urutkan nilai frekuensi dari nilai yang paling banyak. Terakhir reset index dari dataframe top_category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:22:20.387102Z",
     "start_time": "2021-02-02T10:22:20.334148Z"
    }
   },
   "outputs": [],
   "source": [
    "top_category = pd.crosstab(\n",
    "        index=df2['Category'],\n",
    "        columns='Jumlah',\n",
    "        values='App',\n",
    "        aggfunc='count'\n",
    ").sort_values(by='Jumlah', ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:24:18.993255Z",
     "start_time": "2021-02-02T10:24:18.969721Z"
    }
   },
   "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>col_0</th>\n",
       "      <th>Category</th>\n",
       "      <th>Jumlah</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FAMILY</td>\n",
       "      <td>1832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GAME</td>\n",
       "      <td>959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TOOLS</td>\n",
       "      <td>827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BUSINESS</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MEDICAL</td>\n",
       "      <td>395</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col_0  Category  Jumlah\n",
       "0        FAMILY    1832\n",
       "1          GAME     959\n",
       "2         TOOLS     827\n",
       "3      BUSINESS     420\n",
       "4       MEDICAL     395"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_category.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 6:_** Ini adalah bagian untuk melengkapi konten value box \n",
    "most category mengambil nama category paling banyak mengacu pada dataframe `top_category`\n",
    "total mengambil frekuensi/jumlah category paling banyak mengacu pada dataframe `top_category`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:25:55.268573Z",
     "start_time": "2021-02-02T10:25:55.257944Z"
    }
   },
   "outputs": [],
   "source": [
    "most_categories = top_category.iloc[0]['Category']\n",
    "total = top_category.iloc[0]['Jumlah']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'FAMILY'"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1832"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rev_table` adalah tabel yang berisi 10 aplikasi yang paling banyak direview oleh pengguna.\n",
    "Silahkan melakukan agregasi data yang tepat menggunakan `groupby` untuk menampilkan 10 aplikasi yang diurutkan berdasarkan jumlah Review pengguna. \n",
    "\n",
    "Tabel yang ditampilkan terdiri dari 4 kolom yaitu nama Category, nama App, total Reviews, dan rata-rata Rating.\n",
    "Pindahkan kode wrangling yang disimpan dalam variable `rev_table` pada `blank code` yang telah di chaining dengan kode `.to_html`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:32:27.727779Z",
     "start_time": "2021-02-02T10:32:24.299980Z"
    }
   },
   "outputs": [],
   "source": [
    "rev_table = df2.groupby(['Category','App']).agg({    \n",
    "            'Reviews':'sum',  \n",
    "            'Rating':'mean'\n",
    "            }).reset_index().sort_values(by='Reviews', ascending=False).head(10).set_index('Category').reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apabila menuliskan kode program yang tepat maka hasil wrangling adalah sebagai berikut :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:32:37.483995Z",
     "start_time": "2021-02-02T10:32:37.467075Z"
    }
   },
   "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>App</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>78158306</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>WhatsApp Messenger</td>\n",
       "      <td>69119316</td>\n",
       "      <td>4.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SOCIAL</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>66577313</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COMMUNICATION</td>\n",
       "      <td>Messenger – Text and Video Chat for Free</td>\n",
       "      <td>56642847</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GAME</td>\n",
       "      <td>Clash of Clans</td>\n",
       "      <td>44891723</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TOOLS</td>\n",
       "      <td>Clean Master- Space Cleaner &amp; Antivirus</td>\n",
       "      <td>42916526</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GAME</td>\n",
       "      <td>Subway Surfers</td>\n",
       "      <td>27722264</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>VIDEO_PLAYERS</td>\n",
       "      <td>YouTube</td>\n",
       "      <td>25655305</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TOOLS</td>\n",
       "      <td>Security Master - Antivirus, VPN, AppLock, Boo...</td>\n",
       "      <td>24900999</td>\n",
       "      <td>4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GAME</td>\n",
       "      <td>Clash Royale</td>\n",
       "      <td>23133508</td>\n",
       "      <td>4.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category                                                App   Reviews  \\\n",
       "0         SOCIAL                                           Facebook  78158306   \n",
       "1  COMMUNICATION                                 WhatsApp Messenger  69119316   \n",
       "2         SOCIAL                                          Instagram  66577313   \n",
       "3  COMMUNICATION           Messenger – Text and Video Chat for Free  56642847   \n",
       "4           GAME                                     Clash of Clans  44891723   \n",
       "5          TOOLS            Clean Master- Space Cleaner & Antivirus  42916526   \n",
       "6           GAME                                     Subway Surfers  27722264   \n",
       "7  VIDEO_PLAYERS                                            YouTube  25655305   \n",
       "8          TOOLS  Security Master - Antivirus, VPN, AppLock, Boo...  24900999   \n",
       "9           GAME                                       Clash Royale  23133508   \n",
       "\n",
       "   Rating  \n",
       "0     4.1  \n",
       "1     4.4  \n",
       "2     4.5  \n",
       "3     4.0  \n",
       "4     4.6  \n",
       "5     4.7  \n",
       "6     4.5  \n",
       "7     4.3  \n",
       "8     4.7  \n",
       "9     4.6  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Membuat Bar Plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 7:_** \n",
    "Lengkapi tahap agregasi untuk membuat dataframe yang mengelompokkan aplikasi berdasarkan Category.\n",
    "Buatlah bar plot dimana axis x adalah nama Category dan axis y adalah jumlah aplikasi pada setiap kategori, kemudian urutkan dari jumlah terbanyak"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:37:53.665508Z",
     "start_time": "2021-02-02T10:37:52.980775Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shind\\AppData\\Local\\Temp\\ipykernel_16128\\648101890.py:11: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated since 3.2 and will be removed two minor releases later. Use an explicit list instead.\n",
      "  plt.barh(X,Y, color=my_colors)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 2400x900 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cat_order = df2.groupby('Category').agg({\n",
    "'App' : 'count'\n",
    " }).rename({'Category':'Total'}, axis=1).sort_values(by='App', ascending=False).head(5)\n",
    "X = cat_order.reset_index()['Category']\n",
    "Y = cat_order['App']\n",
    "my_colors = 'rgbkymc'\n",
    "# bagian ini digunakan untuk membuat kanvas/figure\n",
    "fig = plt.figure(figsize=(8,3),dpi=300)\n",
    "fig.add_subplot()\n",
    "# bagian ini digunakan untuk membuat bar plot\n",
    "plt.barh(X,Y, color=my_colors)\n",
    "# bagian ini digunakan untuk menyimpan plot dalam format image.png\n",
    "plt.savefig('cat_order.png',bbox_inches=\"tight\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Membuat Scatter Plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 8:_** Buatlah scatter plot untuk menampilkan hubungan dan persebaran apalikasi dilihat dari Review vs Rating.\n",
    "Ukuran scatter menggambarkan berapa banyak pengguna yang telah menginstall aplikasi \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:41:20.616033Z",
     "start_time": "2021-02-02T10:41:20.149283Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = df2['Reviews'].values # axis x\n",
    "Y = df2['Rating'].values # axis y\n",
    "area = playstore['Installs'].values/10000000 # ukuran besar/kecilnya lingkaran scatter plot\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "fig.add_subplot()\n",
    "# isi nama method untuk scatter plot, variabel x, dan variabel y\n",
    "plt.scatter(x=X,y=Y, s=area, alpha=0.3)\n",
    "plt.xlabel('Reviews')\n",
    "plt.ylabel('Rating')\n",
    "plt.savefig('rev_rat.png',bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Membuat Histogram Size Distribution**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 9:_** Buatlah sebuah histogram yang menggambarkan distribusi Size aplikasi dalam satuan Mb(Megabytes). Histogram yang terbentuk terbagi menjadi 100 bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:44:25.038732Z",
     "start_time": "2021-02-02T10:44:24.670174Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=(df2['Size']/1000000).values\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "fig.add_subplot()\n",
    "plt.hist(X,bins=100, density=True,  alpha=0.75)\n",
    "plt.xlabel('Size')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('hist_size.png',bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**_Task 10:_** Buatlah plot berdasarkan hasil analisis anda. Plot yang dibuat tidak diperbolehkan sama dengan plot yang sudah dicontohkan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-02-02T10:47:53.948656Z",
     "start_time": "2021-02-02T10:47:53.936544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## code here\n",
    "green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "df2.pivot(columns='Content Rating', values = 'Rating').plot.box(figsize=(7,7),flierprops=green_diamond)\n",
    "plt.xlabel('Content Rating')\n",
    "plt.ylabel('Rating')\n",
    "plt.savefig('Content_Rate.png',bbox_inches=\"tight\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "capstone-flask",
   "language": "python",
   "name": "capstone-flask"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}