Top_50_Amazon_Books.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Top-50-Amazon-Books.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPafbkyGvdQU9uWBgsdxNtt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hrtywhy/Machine-Learning/blob/main/Top_50_Amazon_Books.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RDLIePzJJiWo"
      },
      "source": [
        "\n",
        "# Data Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaZiToQUJtFP"
      },
      "source": [
        "Google Colab ini dibuat untuk memvisualisasikan data **Top 50 Buku Amazon Terlaris dari tahun 2009 hingga 2019**. Dataset yang digunakan diambil dari [Kaggle](https://www.kaggle.com/sootersaalu/amazon-top-50-bestselling-books-2009-2019)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkzNfAz2ODiA"
      },
      "source": [
        "### Setting Up Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LRZScOrUJ1PW"
      },
      "source": [
        "Karena kita akan menggunakan dataset dari kaggle, maka kita lakukan install kaggle terlebih dahulu"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "alf-fvZyIieN",
        "outputId": "a8624dff-adb5-464c-b60b-a2627bb1517f"
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.24.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.8.1)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle) (2021.5.30)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle) (1.15.0)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.41.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpS07B3cKEPu"
      },
      "source": [
        "Lakukan import pada drive "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eq5tOtf-IqtH",
        "outputId": "db76225d-5678-4633-8b06-b3df966db6a8"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkE7ru6IKIbv"
      },
      "source": [
        "Import os dari letak path kaggle.json. Kaggle.json merupakan file json berupa API Token pada akun kaggle kita yang mana gunanya untuk melakukan download dataset pada kaggle */content/gdrive/My Drive/Kaggle* merupakan path dimana kaggle.json berada "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUDggk7FIyiu"
      },
      "source": [
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/gdrive/My Drive/Kaggle\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vUJX021aLU53"
      },
      "source": [
        "Kemudian kita pindah direktori"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DzlDzNMEI03G",
        "outputId": "5abdaa7b-9013-426f-9972-4a07352ab7f2"
      },
      "source": [
        "%cd /content/gdrive/My Drive/Kaggle"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Kaggle\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "81j06SNnNS0o"
      },
      "source": [
        "Download dataset dengan melakukan API Command yang terdapat pada akun kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06S1lYzmI3SX",
        "outputId": "228178ab-79df-4b4a-8f4d-56794abdef9e"
      },
      "source": [
        "!kaggle datasets download -d sootersaalu/amazon-top-50-bestselling-books-2009-2019"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "amazon-top-50-bestselling-books-2009-2019.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KPCFYBD_NdXh"
      },
      "source": [
        "Kita lihat hasilnya apakah sudah selesai ter download apa belum"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePuT8KyII5rf",
        "outputId": "31c2dc4a-c111-4c8f-e6f1-fdb663941de8"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bestsellers-with-categories.csv  kaggle.json  Untitled0.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qUn7qVeyNtKg"
      },
      "source": [
        "Unzip file dengan ekstensi .zip kemudian hapus"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rj20aerMJARf",
        "outputId": "f6e74f85-a1fc-437b-eb70-52b65594d759"
      },
      "source": [
        "!unzip \\*.zip  && rm *.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  amazon-top-50-bestselling-books-2009-2019.zip\n",
            "  inflating: bestsellers with categories.csv  \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "26zp3G7hNy57"
      },
      "source": [
        "Kita cek kembali apakah filenya sudah ter unzip atau belum"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RMLRvBfJE7m",
        "outputId": "e7252004-8c4e-4564-c809-d7c981eaef12"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "bestsellers-with-categories.csv  kaggle.json  Untitled0.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HB1zRdcgOJyQ"
      },
      "source": [
        "### Import Library"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "elZUyD3lN7vI"
      },
      "source": [
        "Yang kita lakukan sekarang adalah import library "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_NvHhPsJI-2"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMu7fZ60OZO5"
      },
      "source": [
        "Baca file csv dengan nama **bestsellers-with-categories.csv**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Q-_81pHfJLAu",
        "outputId": "2ca39485-ed95-461b-ab5b-428d0dc07b77"
      },
      "source": [
        "df=pd.read_csv('bestsellers-with-categories.csv')\n",
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Name</th>\n",
              "      <th>Author</th>\n",
              "      <th>User Rating</th>\n",
              "      <th>Reviews</th>\n",
              "      <th>Price</th>\n",
              "      <th>Year</th>\n",
              "      <th>Genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10-Day Green Smoothie Cleanse</td>\n",
              "      <td>JJ Smith</td>\n",
              "      <td>4.7</td>\n",
              "      <td>17350</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11/22/63: A Novel</td>\n",
              "      <td>Stephen King</td>\n",
              "      <td>4.6</td>\n",
              "      <td>2052</td>\n",
              "      <td>22</td>\n",
              "      <td>2011</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12 Rules for Life: An Antidote to Chaos</td>\n",
              "      <td>Jordan B. Peterson</td>\n",
              "      <td>4.7</td>\n",
              "      <td>18979</td>\n",
              "      <td>15</td>\n",
              "      <td>2018</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1984 (Signet Classics)</td>\n",
              "      <td>George Orwell</td>\n",
              "      <td>4.7</td>\n",
              "      <td>21424</td>\n",
              "      <td>6</td>\n",
              "      <td>2017</td>\n",
              "      <td>Fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5,000 Awesome Facts (About Everything!) (Natio...</td>\n",
              "      <td>National Geographic Kids</td>\n",
              "      <td>4.8</td>\n",
              "      <td>7665</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>Non Fiction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Name  ...        Genre\n",
              "0                      10-Day Green Smoothie Cleanse  ...  Non Fiction\n",
              "1                                  11/22/63: A Novel  ...      Fiction\n",
              "2            12 Rules for Life: An Antidote to Chaos  ...  Non Fiction\n",
              "3                             1984 (Signet Classics)  ...      Fiction\n",
              "4  5,000 Awesome Facts (About Everything!) (Natio...  ...  Non Fiction\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fb_4ZDJBOmuI"
      },
      "source": [
        "### Visualisasi Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NQNTXd4hOqGw"
      },
      "source": [
        "Langkah terakhir yang kita lakukan adalah memvisualisasikan data berdasarkan genre. Jadi, perbandingan atau presentase genre yang terdapat pada Top 50 Buku Amazon Terlaris dari tahun 20090-2019 adalah"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "6WtYxRQKJRx3",
        "outputId": "6b949025-9a00-4d54-9130-d8b4ae2b3916"
      },
      "source": [
        "# Almost 56% rated as best selling books are Fiction\n",
        "df['Genre'].value_counts().plot.pie(autopct=\"%.1f%%\");\n",
        "df['Genre'].value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Non Fiction    310\n",
              "Fiction        240\n",
              "Name: Genre, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHXDKLVaPPgs"
      },
      "source": [
        "Kita jabarkan kembali dalam bentuk bar chart per tahunnya sehingga di dapatkan hasilnya seperti berikut. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "9qt_A07uJT8v",
        "outputId": "9387ab1a-252b-4f38-9f68-cba95b78d640"
      },
      "source": [
        "plt.figure(figsize=(12,7))\n",
        "sns.countplot(df['Year'],hue=df['Genre'])\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}