MergeDataTest.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-Clet7DUDuN",
        "papermill": {
          "duration": 0.004443,
          "end_time": "2022-11-11T08:46:48.019499",
          "exception": false,
          "start_time": "2022-11-11T08:46:48.015056",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "# Read data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 376,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-11-11T12:18:57.654968Z",
          "iopub.status.busy": "2022-11-11T12:18:57.653997Z",
          "iopub.status.idle": "2022-11-11T12:18:57.688915Z",
          "shell.execute_reply": "2022-11-11T12:18:57.687095Z",
          "shell.execute_reply.started": "2022-11-11T12:18:57.654825Z"
        },
        "id": "7MKMQLCfUDuP",
        "papermill": {
          "duration": 1.688634,
          "end_time": "2022-11-11T08:46:49.712044",
          "exception": false,
          "start_time": "2022-11-11T08:46:48.02341",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 377,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4X-1k3O6UpiZ",
        "outputId": "2faebc49-6904-4e82-e08a-e04919b0ac94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "fatal: destination path 'Datathon' already exists and is not an empty directory.\n"
          ]
        }
      ],
      "source": [
        "! git clone https://github.com/JNADatathon2022/Datathon"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 378,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ck1ngAJQVas6",
        "outputId": "a8e2b1a9-4471-475b-d94e-f76c2f582aec"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mv: cannot stat 'Datathon/data/*': No such file or directory\n"
          ]
        }
      ],
      "source": [
        "! mv Datathon/data/* . && ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 379,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-11-11T12:20:21.571998Z",
          "iopub.status.busy": "2022-11-11T12:20:21.570436Z",
          "iopub.status.idle": "2022-11-11T12:20:22.201905Z",
          "shell.execute_reply": "2022-11-11T12:20:22.200276Z",
          "shell.execute_reply.started": "2022-11-11T12:20:21.571943Z"
        },
        "id": "1UePaD-PUDuR",
        "papermill": {
          "duration": 0.383908,
          "end_time": "2022-11-11T08:46:50.10727",
          "exception": false,
          "start_time": "2022-11-11T08:46:49.723362",
          "status": "completed"
        },
        "tags": []
      },
      "outputs": [],
      "source": [
        "# read train data\n",
        "df_orders = pd.read_csv(\"test.csv\", sep=\";\")\n",
        "df_products = pd.read_csv(\"product_attributes.csv\", sep=\",\")\n",
        "df_dists = pd.read_csv(\"cities_data.csv\", sep=\";\")\n",
        "df_weight = pd.read_csv(\"product_weight_class.csv\", sep = \",\")\n",
        "df_cities_costs = pd.read_csv(\"cities_data_costs.csv\", sep = \",\")\n",
        "\n",
        "# read test data\n",
        "df_test = pd.read_csv(\"test.csv\", sep=\";\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1b-T2aT5WMa6"
      },
      "source": [
        "# Data Cleaning\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 380,
      "metadata": {
        "id": "qwWBwUQkdfDu"
      },
      "outputs": [],
      "source": [
        "df_orders.loc[df_orders['origin_port'] == 'BCN', 'origin_port'] = 'Barcelona'\n",
        "df_orders.loc[df_orders['origin_port'] == 'ATHENAS', 'origin_port'] = 'Athens'\n",
        "df_orders.loc[df_orders['logistic_hub'].isnull(), 'logistic_hub'] = '-1'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 381,
      "metadata": {
        "id": "_ciQamz4lPM0"
      },
      "outputs": [],
      "source": [
        "df_orders['units'] = pd.qcut(df_orders.units, q=10, precision=1,\n",
        "                                 labels=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 382,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "ezRTZcNOllcR",
        "outputId": "47344317-a016-4f4f-862d-12ce13e2b604"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'units')"
            ]
          },
          "execution_count": 382,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(x=df_orders['units'].value_counts().index, y=df_orders['units'].value_counts()).set_title('units')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnwXg6PRkc0X"
      },
      "source": [
        "# Data Joining"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 383,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlF4I7t5kfPd",
        "outputId": "f39443e7-b2a4-48f3-eb0f-5d92aead26c0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['order_id', 'origin_port', '3pl', 'customs_procedures', 'logistic_hub',\n",
              "       'customer', 'product_id', 'units'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 383,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 384,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s55XDap0rNMy",
        "outputId": "f3935dd5-d529-4048-a80e-f92d5cd1193d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(df_orders.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 385,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "KnTf3Zc6lN1e",
        "outputId": "d3dc85c4-b0c0-4be3-902c-974e68550961"
      },
      "outputs": [
        {
          "data": {
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              "\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>weight</th>\n",
              "      <th>material_handling</th>\n",
              "      <th>weight_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1672707</td>\n",
              "      <td>2876</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1694456</td>\n",
              "      <td>2785</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1682865</td>\n",
              "      <td>2751</td>\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-1755bdfb-950f-4030-b49f-9ef3f2946a71');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   product_id  weight  material_handling  weight_class\n",
              "0     1672707    2876                  3             9\n",
              "1     1694456    2785                  5             9\n",
              "2     1682865    2751                  3             9\n",
              "3     1689146    2736                  0             9\n",
              "4     1664069    2707                  4             9"
            ]
          },
          "execution_count": 385,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Merge info about products\n",
        "df_merge_product = pd.merge(df_products,df_weight,on='product_id')\n",
        "df_merge_product.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 386,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ae_EfXr_sIUU",
        "outputId": "7c05793c-6306-4941-ec69-5a58a6b9f1e8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1303.2979274611398\n"
          ]
        }
      ],
      "source": [
        "weight_av = np.average(df_merge_product[\"weight\"].values)\n",
        "print(weight_av)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 387,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wVjCfXv9tJkx",
        "outputId": "0710fd5c-2661-456d-9e5b-b65739373b34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5\n"
          ]
        }
      ],
      "source": [
        "av_weight_class = np.average(df_merge_product[\"weight_class\"].values)\n",
        "av_weight_class = str(int(av_weight_class.round()))\n",
        "print(av_weight_class)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 388,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2JbPFWss2hj",
        "outputId": "10b672d0-e4f1-4c81-bd50-7dabf76525ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "5\n"
          ]
        }
      ],
      "source": [
        "counts = np.bincount(df_merge_product[\"material_handling\"].values)\n",
        "moda = np.argmax(counts)\n",
        "print(moda)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 389,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "XU4Al_RjvL1h",
        "outputId": "03e24626-5ff4-4890-bd48-9cc182022880"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
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              "    }\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>weight</th>\n",
              "      <th>material_handling</th>\n",
              "      <th>weight_class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>5</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-372c8502-b83f-4dcb-9226-67b41663728d')\"\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",
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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",
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              "      </button>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    }\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-372c8502-b83f-4dcb-9226-67b41663728d 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-372c8502-b83f-4dcb-9226-67b41663728d');\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",
              "  "
            ],
            "text/plain": [
              "  product_id       weight  material_handling weight_class\n",
              "0          1  1303.297927                  5            5"
            ]
          },
          "execution_count": 389,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2 = pd.DataFrame({'product_id': [\"1\"],\n",
        "                    'weight': [weight_av],\n",
        "                    'material_handling': [moda],\n",
        "                    'weight_class': [av_weight_class]})\n",
        "df2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 390,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nzV4AEq_51DD",
        "outputId": "247aad86-1cab-40c1-ec44-aca88b45bba0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "product_id            object\n",
              "weight               float64\n",
              "material_handling      int64\n",
              "weight_class          object\n",
              "dtype: object"
            ]
          },
          "execution_count": 390,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df2.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 391,
      "metadata": {
        "id": "O-x4xPGC1Urp"
      },
      "outputs": [],
      "source": [
        "df_merge_product = df_merge_product.append(df2, ignore_index = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 392,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnqc67JJ11Bn",
        "outputId": "7cbc3db5-809b-477e-be82-5cfa99cd7670"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "product_id            object\n",
              "weight               float64\n",
              "material_handling      int64\n",
              "weight_class          object\n",
              "dtype: object"
            ]
          },
          "execution_count": 392,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_merge_product.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 393,
      "metadata": {
        "id": "ZhLQzO-83eMN"
      },
      "outputs": [],
      "source": [
        "df_merge_product['product_id'] = df_merge_product['product_id'].astype('str') "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 394,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoMl4brH3n44",
        "outputId": "6504b71e-cd0d-4501-fa2e-b6d90eb4e4ba"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "product_id            object\n",
              "weight               float64\n",
              "material_handling      int64\n",
              "weight_class          object\n",
              "dtype: object"
            ]
          },
          "execution_count": 394,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_merge_product.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 395,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "cEsY5P1rvwAY",
        "outputId": "9e485d3f-9c5c-47c8-9815-99b5356a749b"
      },
      "outputs": [
        {
          "data": {
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              "       width=\"24px\">\n",
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              "      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",
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              "      height: 32px;\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",
              "      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-67d008ef-26ca-4f25-b728-ef6aa4adf130 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-67d008ef-26ca-4f25-b728-ef6aa4adf130');\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",
              "  "
            ],
            "text/plain": [
              "    product_id       weight  material_handling weight_class\n",
              "768    1683403   161.000000                  1            0\n",
              "769    1699799   153.000000                  3            0\n",
              "770    1699768   139.000000                  5            0\n",
              "771    1660578   136.000000                  5            0\n",
              "772          1  1303.297927                  5            5"
            ]
          },
          "execution_count": 395,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_merge_product.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 396,
      "metadata": {
        "id": "PhRuM9-GrNsR"
      },
      "outputs": [],
      "source": [
        "df_orders_copy = df_orders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 397,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AxV5-iCA305T",
        "outputId": "f9edd206-4c4b-412e-f6af-396d11293e54"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "order_id              object\n",
              "origin_port           object\n",
              "3pl                   object\n",
              "customs_procedures    object\n",
              "logistic_hub          object\n",
              "customer              object\n",
              "product_id             int64\n",
              "units                  int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 397,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders_copy.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 398,
      "metadata": {
        "id": "Z6md_W1D3-YC"
      },
      "outputs": [],
      "source": [
        "df_orders_copy['product_id'] = df_orders_copy['product_id'].astype(str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 399,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "re59blT54D8t",
        "outputId": "c53868e3-ca39-47a9-fa9e-eb4aacaf653d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "order_id              object\n",
              "origin_port           object\n",
              "3pl                   object\n",
              "customs_procedures    object\n",
              "logistic_hub          object\n",
              "customer              object\n",
              "product_id            object\n",
              "units                  int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 399,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders_copy.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 400,
      "metadata": {
        "id": "lv3PJJmUxw6N"
      },
      "outputs": [],
      "source": [
        "for index, row in df_orders_copy.iterrows():\n",
        "  if(row[\"product_id\"] == \"-1\"):\n",
        "    df_orders_copy.at[index, \"product_id\"] = \"1\"\n",
        "  elif(not (row[\"product_id\"] in df_merge_product[\"product_id\"].values)):\n",
        "    #print(row)\n",
        "    df_orders_copy.at[index, \"product_id\"] = \"1\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 401,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pX-r_wI43FJ_",
        "outputId": "4ffa5266-7ffb-4249-a82b-a7c5906cb71d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "city_from_name:  ['1687644' '1699689' '1652615' '1669740' '1695138' '1690627' '1672714'\n",
            " '1660579' '1644308' '1681883' '1685211' '1672344' '1615677' '1699666'\n",
            " '1692948' '1696747' '1651366' '1660563' '1660580' '1649002' '1613321'\n",
            " '1699345' '1650715' '1669701' '1683392' '1683388' '1684808' '1699586'\n",
            " '1699135' '1683403' '1695139' '1670215' '1681379' '1690632' '1657437'\n",
            " '1683407' '1699242' '1652887' '1676566' '1682799' '1699694' '1662238'\n",
            " '1676565' '1620510' '1694456' '1650880' '1663337' '1666959' '1660897'\n",
            " '1622685' '1681377' '1682745' '1657063' '1685952' '1700118' '1650904'\n",
            " '1699022' '1688600' '1672345' '1680915' '1687521' '1699325' '1672706'\n",
            " '1697083' '1690621' '1683419' '1697085' '1687661' '1651377' '1671834'\n",
            " '1683634' '1699424' '1696799' '1679995' '1683700' '1662551' '1689457'\n",
            " '1689032' '1700139' '1692737' '1698913' '1683199' '1693618' '1684796'\n",
            " '1682681' '1683354' '1650905' '1699540' '1674635' '1672088' '1644050'\n",
            " '1669584' '1676564' '1676699' '1686762' '1666524' '1701405' '1678648'\n",
            " '1699312' '1680781' '1657474' '1700140' '1693001' '1688290' '1685760'\n",
            " '1699231' '1696456' '1684170' '1692731' '1692115' '1696757' '1657013'\n",
            " '1676592' '1655982' '1688629' '1676942' '1699841' '1644200' '1663341'\n",
            " '1644062' '1680738' '1689469' '1689146' '1689543' '1650571' '1690617'\n",
            " '1699248' '1656348' '1699570' '1691546' '1656969' '1700094' '1667126'\n",
            " '1682742' '1692955' '1684703' '1702654' '1677884' '1682983' '1689551'\n",
            " '1693626' '1688294' '1677887' '1697081' '1699353' '1675655' '1666317'\n",
            " '1690001' '1651933' '1678493' '1683935' '1682850' '1689780' '1692333'\n",
            " '1650404' '1681885' '1672651' '1637055' '1681688' '1680561' '1668421'\n",
            " '1699318' '1682757' '1686764' '1682739' '1689601' '1652538' '1653237'\n",
            " '1689012' '1663347' '1699798' '1681882' '1682802' '1700362' '1700382'\n",
            " '1689298' '1682760' '1689550' '1688285' '1693117' '1654623' '1685332'\n",
            " '1699564' '1696533' '1685213' '1689553' '1700097' '1689547' '1682210'\n",
            " '1696756' '1690004' '1685963' '1668127' '1678000' '1677692' '1655818'\n",
            " '1656347' '1652080' '1680562' '1699184' '1671426' '1682982' '1686215'\n",
            " '1699949' '1681878' '1666957' '1674380' '1655373' '1646392' '1686463'\n",
            " '1660885' '1682832' '1680210' '1681890' '1699553' '1692997' '1683409'\n",
            " '1668344' '1699524' '1650685' '1666396' '1687353' '1685329' '1692946'\n",
            " '1701462' '1677877' '1700137' '1660673' '1696476' '1660585' '1682914'\n",
            " '1658532' '1690294' '1696482' '1689784' '1696746' '1684709' '1660578' '1'\n",
            " '1682754' '1699557' '1699361' '1699241' '1662552' '1700141' '1699372'\n",
            " '1699799' '1693700' '1683635' '1683293' '1700557' '1699836' '1666990'\n",
            " '1690628' '1649007' '1682341' '1674598' '1698898' '1662991' '1688621'\n",
            " '1674853' '1692899' '1672117' '1693105' '1660153' '1683560' '1636236'\n",
            " '1681687' '1672006' '1699700' '1668099' '1699567' '1668555' '1687384'\n",
            " '1693740' '1692108' '1699314' '1655985' '1700035' '1680966' '1654251'\n",
            " '1646909' '1666958' '1690614' '1655754' '1649008' '1699283' '1693122'\n",
            " '1671832' '1681888' '1692950' '1692708' '1682829' '1683405' '1656246'\n",
            " '1699690' '1668343' '1660668' '1636782' '1666295' '1685212' '1676851'\n",
            " '1700958' '1696073' '1664067' '1693104' '1667817' '1654621' '1699591'\n",
            " '1688587' '1678427' '1688292' '1699378' '1682826' '1698997' '1683387'\n",
            " '1699768' '1667934' '1654497' '1672245' '1691700' '1693019' '1629341'\n",
            " '1688589' '1666961' '1688633' '1699515' '1696075' '1651302' '1695862'\n",
            " '1682784' '1700869' '1639636' '1697306' '1660551' '1699769' '1671461'\n",
            " '1700569' '1666519' '1700570' '1690327' '1672091' '1699844' '1696149'\n",
            " '1699794' '1699552' '1689641' '1689788' '1668362' '1694720' '1683190'\n",
            " '1678494' '1699575' '1696260' '1671833' '1682793' '1699682' '1699417'\n",
            " '1689786' '1668492' '1689549' '1699539' '1696798' '1672707' '1689296'\n",
            " '1683200' '1646393' '1692722' '1684560' '1699166' '1682730' '1691532'\n",
            " '1699425' '1699561' '1696748' '1668612' '1674120' '1693192' '1699239'\n",
            " '1671416' '1689033' '1699371' '1650568' '1677690' '1652499' '1672721'\n",
            " '1664051' '1699418' '1701972' '1682859' '1666466' '1672712' '1658528'\n",
            " '1686435' '1683636' '1689781' '1692956' '1662395' '1699526' '1693230'\n",
            " '1696954' '1656350' '1668557' '1689541' '1699697' '1664061' '1671807'\n",
            " '1672676' '1661099' '1699310' '1699188' '1672347' '1689785' '1623991'\n",
            " '1699623' '1655723' '1692723' '1682814' '1677891' '1690145' '1679639'\n",
            " '1699676' '1683633' '1675662' '1692724' '1661119' '1685330' '1702653'\n",
            " '1654622' '1692106' '1695349' '1693101' '1699665' '1669685' '1673710'\n",
            " '1699770' '1700558' '1682772' '1679638' '1682682' '1670203' '1677229'\n",
            " '1689782' '1672034' '1671942' '1688288' '1681701' '1679991' '1680740'\n",
            " '1671247' '1662554' '1687689' '1687346' '1672000' '1692987' '1651298'\n",
            " '1672986' '1685076' '1663396' '1699574' '1654498' '1692114' '1676563'\n",
            " '1620552' '1699546' '1668607' '1680503' '1668608' '1676853' '1687491'\n",
            " '1686657' '1642993' '1699842' '1699625' '1683558' '1689031' '1681699'\n",
            " '1699948' '1699793' '1690144' '1699172' '1683999' '1680961' '1696488'\n",
            " '1672661' '1699298' '1682908' '1698924' '1696654' '1702224' '1701406'\n",
            " '1686756' '1697444' '1688598' '1660583' '1682805' '1651350' '1693453'\n",
            " '1696262' '1664069' '1668094' '1699326' '1687774' '1699696' '1699555'\n",
            " '1666954' '1699433' '1689544' '1683197' '1668606' '1692402' '1669702'\n",
            " '1699369' '1682790' '1668097' '1692998' '1682651' '1667927' '1652885'\n",
            " '1697072' '1654766' '1671997' '1689556' '1692949' '1690171' '1702016'\n",
            " '1651299' '1668128' '1671951' '1682796' '1699576' '1635536' '1697317'\n",
            " '1662317' '1666507' '1699249' '1700143' '1682865' '1660577' '1699246'\n",
            " '1669409' '1699583' '1695345' '1699795' '1653969' '1668545' '1696210'\n",
            " '1684755' '1702652' '1683401' '1699953' '1689461' '1689546' '1651126'\n",
            " '1672720' '1672722' '1700128' '1699792' '1699337' '1699772' '1643056'\n",
            " '1692277' '1699974' '1689548' '1671836' '1675746' '1697087' '1680249'\n",
            " '1700130' '1689545' '1657064' '1656944' '1699674' '1699525' '1695346'\n",
            " '1699954' '1684559' '1699285' '1643057' '1666443' '1697872' '1699333'\n",
            " '1689554' '1693865' '1621719' '1683432' '1656353' '1696754' '1692109'\n",
            " '1652630' '1699584' '1668345' '1666992' '1678674' '1662274' '1692940'\n",
            " '1699573' '1668347' '1672664' '1699336' '1699548' '1678652' '1682763'\n",
            " '1650586' '1700980' '1672713' '1688610' '1681376' '1691544' '1699315'\n",
            " '1652883' '1668639' '1690634' '1697080' '1621717' '1671246' '1699465'\n",
            " '1668550' '1671248' '1699691' '1674378' '1697781' '1688575' '1685321'\n",
            " '1685214' '1699416' '1668496' '1699834' '1682679' '1682769' '1699952'\n",
            " '1700887' '1653588' '1680246' '1700014' '1695062' '1668547' '1699955'\n",
            " '1641237' '1700142' '1699419' '1683191' '1682736' '1699329' '1652644'\n",
            " '1699268' '1690143' '1660677' '1699993' '1686758' '1683424' '1699265'\n",
            " '1684005' '1700875' '1617826' '1699290' '1688824' '1689147' '1699537'\n",
            " '1700657' '1668346' '1675615' '1682751' '1679687' '1700738' '1699311'\n",
            " '1670498' '1700105' '1696263' '1682853' '1666441' '1699976' '1691548'\n",
            " '1699670' '1697883' '1695817' '1677878' '1689783' '1678759' '1687354'\n",
            " '1692974' '1697230' '1700003' '1682835' '1700366' '1698930' '1693747'\n",
            " '1656839' '1683704' '1689145' '1672110' '1696074' '1650400' '1688678'\n",
            " '1699946' '1690092' '1677889' '1689460' '1685333' '1683426' '1697067'\n",
            " '1699571' '1668716' '1699951' '1699771' '1683203' '1700106' '1700121'\n",
            " '1682594' '1699173' '1695687' '1682709' '1696758' '1697884' '1675670'\n",
            " '1682748' '1690630' '1700138' '1645070' '1689779' '1692656' '1668554'\n",
            " '1695348' '1699766' '1696752' '1671939' '1683430' '1672085' '1698977'\n",
            " '1699220' '1690631' '1699380' '1699308' '1697084' '1699327' '1697071'\n",
            " '1666994' '1684497' '1698988' '1688571' '1685028' '1692113' '1657475'\n",
            " '1672670' '1686401' '1699950' '1692994' '1652611' '1699368' '1699675'\n",
            " '1697874' '1675654' '1699797' '1692984' '1684862' '1699556' '1682959'\n",
            " '1617714' '1683282' '1690006' '1697086' '1652604' '1691393']\n"
          ]
        }
      ],
      "source": [
        "print(\"city_from_name: \", (df_orders_copy[\"product_id\"].unique()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 402,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "0eC8N7iSzQ7D",
        "outputId": "bec8d049-56a6-40f0-f575-bb34c15fd88d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-3f7db1e4-7e50-4715-8bcc-96c5a1c4f908\">\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>order_id</th>\n",
              "      <th>origin_port</th>\n",
              "      <th>3pl</th>\n",
              "      <th>customs_procedures</th>\n",
              "      <th>logistic_hub</th>\n",
              "      <th>customer</th>\n",
              "      <th>product_id</th>\n",
              "      <th>units</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>347</th>\n",
              "      <td>b16e28fd37e5</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Cologne</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>724</th>\n",
              "      <td>b40c2cef3343</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1319</th>\n",
              "      <td>c352edc7c35f</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Bordeaux</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1662</th>\n",
              "      <td>749e4800982e</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Lille</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1697</th>\n",
              "      <td>1193bff2f1cd</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Liege</td>\n",
              "      <td>Valencia</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2506</th>\n",
              "      <td>4ee9f2bdcd18</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Liege</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2654</th>\n",
              "      <td>a774d95031a1</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Budapest</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2714</th>\n",
              "      <td>42ee29d13cc4</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3436</th>\n",
              "      <td>a70fec54f1fe</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Marseille</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3670</th>\n",
              "      <td>33301663d847</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Rome</td>\n",
              "      <td>Milan</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4280</th>\n",
              "      <td>b9b98f5dca7f</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>Helsinki</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>4879</th>\n",
              "      <td>77962d131544</td>\n",
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              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4995</th>\n",
              "      <td>6d323f0919cc</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Lyon</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>6287</th>\n",
              "      <td>23e355409217</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>Turin</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7812</th>\n",
              "      <td>e97a4884682c</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Rome</td>\n",
              "      <td>Copenhagen</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>8044</th>\n",
              "      <td>925764139b80</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Liege</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8715</th>\n",
              "      <td>232bc9eb5ee7</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>Milan</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8940</th>\n",
              "      <td>bfb788d069ec</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
              "      <td>Liege</td>\n",
              "      <td>Bucharest</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9171</th>\n",
              "      <td>24e08d8ce994</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Lille</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9447</th>\n",
              "      <td>85a38e3e01a6</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Vienna</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>10101</th>\n",
              "      <td>4d15444426f4</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Naples</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10300</th>\n",
              "      <td>76e8d7807daf</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
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              "      <td>Barcelona</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>10826</th>\n",
              "      <td>197f55705cd9</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
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              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>11285</th>\n",
              "      <td>618a2a5b5e9a</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>12196</th>\n",
              "      <td>3eecc42ab433</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Malmö</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12493</th>\n",
              "      <td>18e41b59bc30</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Malmö</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12611</th>\n",
              "      <td>aa125be10246</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Madrid</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13953</th>\n",
              "      <td>55c4a134c0c8</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14642</th>\n",
              "      <td>ea851579e207</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Bremen</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14700</th>\n",
              "      <td>e809d08a17da</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Bordeaux</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15297</th>\n",
              "      <td>6be4bee0ebca</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Bratislava</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16201</th>\n",
              "      <td>3d34623b1c63</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Athens</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16757</th>\n",
              "      <td>51e14975ce28</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Lille</td>\n",
              "      <td>Amsterdam</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17668</th>\n",
              "      <td>71e248a497a4</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Marseille</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18073</th>\n",
              "      <td>ecb9856e2479</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Lille</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18378</th>\n",
              "      <td>aa192dcb3c3b</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Milan</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>19051</th>\n",
              "      <td>6728a29451e7</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Lyon</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19804</th>\n",
              "      <td>eba747a523be</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Venlo</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>1</td>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>21201</th>\n",
              "      <td>4be900cd93a2</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>Lisbon</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21839</th>\n",
              "      <td>50ec22fa76ff</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Rome</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23490</th>\n",
              "      <td>3c348aeebebe</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTP</td>\n",
              "      <td>Hamburg</td>\n",
              "      <td>Valencia</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26001</th>\n",
              "      <td>fe33528700a9</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Bratislava</td>\n",
              "      <td>Malmö</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26238</th>\n",
              "      <td>f8f76eb3557e</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Venlo</td>\n",
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              "      <td>1</td>\n",
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              "      <th>26486</th>\n",
              "      <td>6eae55f7eece</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>v_003</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Venlo</td>\n",
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              "      <th>27051</th>\n",
              "      <td>e857686202eb</td>\n",
              "      <td>Barcelona</td>\n",
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              "      <td>Venlo</td>\n",
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              "      <td>1</td>\n",
              "      <td>9</td>\n",
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              "          document.querySelector('#df-3f7db1e4-7e50-4715-8bcc-96c5a1c4f908 button.colab-df-convert');\n",
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              "\n",
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              "          const element = document.querySelector('#df-3f7db1e4-7e50-4715-8bcc-96c5a1c4f908');\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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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "           order_id origin_port    3pl customs_procedures logistic_hub  \\\n",
              "347    b16e28fd37e5   Barcelona  v_003                DTP   Dusseldorf   \n",
              "724    b40c2cef3343   Barcelona  v_003                CRF        Venlo   \n",
              "1319   c352edc7c35f   Barcelona  v_003                DTD        Venlo   \n",
              "1662   749e4800982e   Barcelona  v_003                CRF        Lille   \n",
              "1697   1193bff2f1cd   Barcelona  v_003                DTD        Liege   \n",
              "2506   4ee9f2bdcd18   Barcelona  v_003                CRF        Liege   \n",
              "2654   a774d95031a1   Barcelona  v_003                DTD      Hamburg   \n",
              "2714   42ee29d13cc4   Barcelona  v_003                CRF   Dusseldorf   \n",
              "3436   a70fec54f1fe   Barcelona  v_003                CRF      Hamburg   \n",
              "3670   33301663d847   Barcelona  v_003                CRF         Rome   \n",
              "4280   b9b98f5dca7f   Barcelona  v_003                CRF     Zaragoza   \n",
              "4879   77962d131544   Barcelona  v_003                CRF      Hamburg   \n",
              "4995   6d323f0919cc   Barcelona  v_003                DTD        Venlo   \n",
              "6287   23e355409217   Barcelona  v_003                CRF     Zaragoza   \n",
              "7812   e97a4884682c   Barcelona  v_003                DTD         Rome   \n",
              "8044   925764139b80   Barcelona  v_003                CRF        Liege   \n",
              "8715   232bc9eb5ee7   Barcelona  v_003                DTD     Zaragoza   \n",
              "8940   bfb788d069ec   Barcelona  v_003                DTP        Liege   \n",
              "9171   24e08d8ce994   Barcelona  v_003                CRF        Lille   \n",
              "9447   85a38e3e01a6   Barcelona  v_003                CRF      Hamburg   \n",
              "10101  4d15444426f4   Barcelona  v_003                CRF        Venlo   \n",
              "10300  76e8d7807daf   Barcelona  v_003                CRF       Warsaw   \n",
              "10826  197f55705cd9   Barcelona  v_003                DTP        Venlo   \n",
              "11285  618a2a5b5e9a   Barcelona  v_003                DTD      Hamburg   \n",
              "12196  3eecc42ab433   Barcelona  v_003                DTP      Hamburg   \n",
              "12493  18e41b59bc30   Barcelona  v_003                DTD       Warsaw   \n",
              "12611  aa125be10246   Barcelona  v_003                CRF      Hamburg   \n",
              "13953  55c4a134c0c8   Barcelona  v_003                DTD        Venlo   \n",
              "14642  ea851579e207   Barcelona  v_003                DTD   Dusseldorf   \n",
              "14700  e809d08a17da   Barcelona  v_003                DTD       Warsaw   \n",
              "15297  6be4bee0ebca   Barcelona  v_003                DTD   Bratislava   \n",
              "16201  3d34623b1c63   Barcelona  v_003                DTP        Venlo   \n",
              "16757  51e14975ce28   Barcelona  v_003                DTD        Lille   \n",
              "17668  71e248a497a4   Barcelona  v_003                CRF       Warsaw   \n",
              "18073  ecb9856e2479   Barcelona  v_003                DTD        Lille   \n",
              "18378  aa192dcb3c3b   Barcelona  v_003                CRF      Hamburg   \n",
              "19051  6728a29451e7   Barcelona  v_003                CRF      Hamburg   \n",
              "19804  eba747a523be   Barcelona  v_003                CRF        Venlo   \n",
              "21201  4be900cd93a2   Barcelona  v_003                CRF     Zaragoza   \n",
              "21839  50ec22fa76ff   Barcelona  v_003                CRF       Warsaw   \n",
              "23490  3c348aeebebe   Barcelona  v_003                DTP      Hamburg   \n",
              "26001  fe33528700a9   Barcelona  v_003                CRF   Bratislava   \n",
              "26238  f8f76eb3557e   Barcelona  v_003                DTD        Venlo   \n",
              "26486  6eae55f7eece   Barcelona  v_003                CRF        Venlo   \n",
              "27051  e857686202eb   Barcelona  v_003                DTD        Venlo   \n",
              "\n",
              "         customer product_id  units  \n",
              "347       Cologne          1      7  \n",
              "724        Madrid          1      4  \n",
              "1319     Bordeaux          1      3  \n",
              "1662       Madrid          1      2  \n",
              "1697     Valencia          1      1  \n",
              "2506     Budapest          1      5  \n",
              "2654     Budapest          1      4  \n",
              "2714    Barcelona          1      1  \n",
              "3436    Marseille          1      2  \n",
              "3670        Milan          1      6  \n",
              "4280     Helsinki          1      8  \n",
              "4879       Berlin          1      0  \n",
              "4995         Lyon          1      4  \n",
              "6287        Turin          1      9  \n",
              "7812   Copenhagen          1      5  \n",
              "8044       Madrid          1      6  \n",
              "8715        Milan          1      6  \n",
              "8940    Bucharest          1      3  \n",
              "9171    Barcelona          1      8  \n",
              "9447       Vienna          1      4  \n",
              "10101      Naples          1      5  \n",
              "10300   Barcelona          1      1  \n",
              "10826       Paris          1      8  \n",
              "11285      Madrid          1      9  \n",
              "12196       Malmö          1      8  \n",
              "12493       Malmö          1      9  \n",
              "12611      Madrid          1      7  \n",
              "13953   Amsterdam          1      0  \n",
              "14642      Bremen          1      2  \n",
              "14700    Bordeaux          1      4  \n",
              "15297      Berlin          1      9  \n",
              "16201      Athens          1      7  \n",
              "16757   Amsterdam          1      8  \n",
              "17668   Marseille          1      3  \n",
              "18073   Barcelona          1      6  \n",
              "18378       Milan          1      4  \n",
              "19051        Lyon          1      3  \n",
              "19804      Berlin          1      8  \n",
              "21201      Lisbon          1      1  \n",
              "21839        Rome          1      6  \n",
              "23490    Valencia          1      2  \n",
              "26001       Malmö          1      2  \n",
              "26238      Athens          1      2  \n",
              "26486      Vienna          1      9  \n",
              "27051      Berlin          1      9  "
            ]
          },
          "execution_count": 402,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders_copy.loc[df_orders_copy['product_id'] == \"1\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 403,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "8uruG_Z8rRoq",
        "outputId": "23020ae8-f4d5-4e0f-da4b-406b5654632c"
      },
      "outputs": [
        {
          "data": {
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              "      <th>order_id</th>\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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              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw   \n",
              "1  3ef49bd5a55b   Barcelona  v_002                CRF        Venlo   \n",
              "2  9ab5b9685bd5   Rotterdam  v_004                DTD   Bratislava   \n",
              "3  bfce5b4fc4fa   Rotterdam  v_002                DTD         Rome   \n",
              "4  d94453ec8ec5   Barcelona  v_001                CRF   Bratislava   \n",
              "\n",
              "     customer product_id  units  \n",
              "0      Munich    1687644      4  \n",
              "1      Berlin    1699689      0  \n",
              "2       Paris    1652615      6  \n",
              "3     Hanover    1669740      8  \n",
              "4  Copenhagen    1695138      4  "
            ]
          },
          "execution_count": 403,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders_copy.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 404,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDbZq9gtrHHO",
        "outputId": "b0730b01-5faf-4d24-842f-ee31b14f0cb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(df_orders_copy.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 405,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "mXxUtxhfrXAu",
        "outputId": "18690176-79c0-459c-bb02-5570604333b3"
      },
      "outputs": [
        {
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ac26570-2b68-42a1-a635-b8129ec5026a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\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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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\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-0ac26570-2b68-42a1-a635-b8129ec5026a 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-0ac26570-2b68-42a1-a635-b8129ec5026a');\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",
              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub   customer  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw     Munich   \n",
              "1  6c8c11251114   Rotterdam  v_002                CRF        Venlo  Barcelona   \n",
              "2  81d3be5e1eff      Athens  v_002                DTD       Warsaw       Lyon   \n",
              "3  870a9b65a1b5   Rotterdam  v_001                CRF   Dusseldorf     Berlin   \n",
              "4  eff072956d66   Rotterdam  v_002                CRF         Rome      Milan   \n",
              "\n",
              "  product_id  units  weight  material_handling weight_class  \n",
              "0    1687644      4  1974.0                  5            8  \n",
              "1    1687644      1  1974.0                  5            8  \n",
              "2    1687644      8  1974.0                  5            8  \n",
              "3    1687644      8  1974.0                  5            8  \n",
              "4    1687644      0  1974.0                  5            8  "
            ]
          },
          "execution_count": 405,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_orders_copy = pd.merge(df_orders_copy, df_merge_product, on = \"product_id\")\n",
        "df_orders_copy.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 406,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6AaDHj11ri8J",
        "outputId": "55a69978-5072-4cd9-ec48-1b7be3027e47"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(df_orders_copy.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 407,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "t8ym8-lGrnGj",
        "outputId": "e578dd95-5a7c-4c77-b3ff-45fa8ce6ce42"
      },
      "outputs": [
        {
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>city_from_name</th>\n",
              "      <th>city_to_name</th>\n",
              "      <th>city_from_coord</th>\n",
              "      <th>city_to_coord</th>\n",
              "      <th>distance</th>\n",
              "      <th>weight_class</th>\n",
              "      <th>cost_per_unit</th>\n",
              "      <th>co2_per_unit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>(51.2254018, 6.7763137)</td>\n",
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              "      <td>Dusseldorf</td>\n",
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              "      <td>901.75</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>(51.2254018, 6.7763137)</td>\n",
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              "      <td>515.71</td>\n",
              "      <td>983.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>(51.2254018, 6.7763137)</td>\n",
              "      <td>(41.6521342, -0.8809428)</td>\n",
              "      <td>1214.4772</td>\n",
              "      <td>3</td>\n",
              "      <td>558.69</td>\n",
              "      <td>1065.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>(51.2254018, 6.7763137)</td>\n",
              "      <td>(41.6521342, -0.8809428)</td>\n",
              "      <td>1214.4772</td>\n",
              "      <td>4</td>\n",
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              "          const element = document.querySelector('#df-eec7c0c6-b8ac-4172-a832-3af44ff37cb8');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  city_from_name city_to_name          city_from_coord  \\\n",
              "0     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "1     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "2     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "3     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "4     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "\n",
              "              city_to_coord   distance  weight_class  cost_per_unit  \\\n",
              "0  (41.6521342, -0.8809428)  1214.4772             0         429.76   \n",
              "1  (41.6521342, -0.8809428)  1214.4772             1         472.74   \n",
              "2  (41.6521342, -0.8809428)  1214.4772             2         515.71   \n",
              "3  (41.6521342, -0.8809428)  1214.4772             3         558.69   \n",
              "4  (41.6521342, -0.8809428)  1214.4772             4         601.66   \n",
              "\n",
              "   co2_per_unit  \n",
              "0        819.77  \n",
              "1        901.75  \n",
              "2        983.73  \n",
              "3       1065.70  \n",
              "4       1147.68  "
            ]
          },
          "execution_count": 407,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cities_costs.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 408,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "ZDNxXylju6Te",
        "outputId": "c5cd831b-036a-4231-b24c-4fe94450aa86"
      },
      "outputs": [
        {
          "data": {
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              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>origin_port</th>\n",
              "      <th>3pl</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b0d07bb-00cd-4364-860d-cf591769cf2f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "      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-1b0d07bb-00cd-4364-860d-cf591769cf2f 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-1b0d07bb-00cd-4364-860d-cf591769cf2f');\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",
              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub   customer  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw     Munich   \n",
              "1  6c8c11251114   Rotterdam  v_002                CRF        Venlo  Barcelona   \n",
              "2  81d3be5e1eff      Athens  v_002                DTD       Warsaw       Lyon   \n",
              "3  870a9b65a1b5   Rotterdam  v_001                CRF   Dusseldorf     Berlin   \n",
              "4  eff072956d66   Rotterdam  v_002                CRF         Rome      Milan   \n",
              "\n",
              "  product_id  units  weight  material_handling weight_class  distance  \\\n",
              "0    1687644      4  1974.0                  5            8       NaN   \n",
              "1    1687644      1  1974.0                  5            8       NaN   \n",
              "2    1687644      8  1974.0                  5            8       NaN   \n",
              "3    1687644      8  1974.0                  5            8       NaN   \n",
              "4    1687644      0  1974.0                  5            8       NaN   \n",
              "\n",
              "   cost_per_unit  co2_per_unit  \n",
              "0            NaN           NaN  \n",
              "1            NaN           NaN  \n",
              "2            NaN           NaN  \n",
              "3            NaN           NaN  \n",
              "4            NaN           NaN  "
            ]
          },
          "execution_count": 408,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_copy_2 = df_orders_copy.reindex(columns = df_orders_copy.columns.tolist() + [\"distance\", \"cost_per_unit\", \"co2_per_unit\"])\n",
        "df_copy_2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 409,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B1xHx0YErBwm",
        "outputId": "6014e8a1-8c71-4e29-ef80-18657a393f3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(df_copy_2.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 410,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 161
        },
        "id": "AiA57hbFvW14",
        "outputId": "5d5f16dd-c165-41c7-a2f3-711050c7930d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-a571538b-1ee1-4d74-995c-1d47c259b43a\">\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>city_from_name</th>\n",
              "      <th>city_to_name</th>\n",
              "      <th>city_from_coord</th>\n",
              "      <th>city_to_coord</th>\n",
              "      <th>distance</th>\n",
              "      <th>weight_class</th>\n",
              "      <th>cost_per_unit</th>\n",
              "      <th>co2_per_unit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Zaragoza</td>\n",
              "      <td>(51.2254018, 6.7763137)</td>\n",
              "      <td>(41.6521342, -0.8809428)</td>\n",
              "      <td>1214.4772</td>\n",
              "      <td>0</td>\n",
              "      <td>429.76</td>\n",
              "      <td>819.77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a571538b-1ee1-4d74-995c-1d47c259b43a')\"\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",
              "      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-a571538b-1ee1-4d74-995c-1d47c259b43a 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-a571538b-1ee1-4d74-995c-1d47c259b43a');\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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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "  city_from_name city_to_name          city_from_coord  \\\n",
              "0     Dusseldorf     Zaragoza  (51.2254018, 6.7763137)   \n",
              "\n",
              "              city_to_coord   distance  weight_class  cost_per_unit  \\\n",
              "0  (41.6521342, -0.8809428)  1214.4772             0         429.76   \n",
              "\n",
              "   co2_per_unit  \n",
              "0        819.77  "
            ]
          },
          "execution_count": 410,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#Prova\n",
        "df_prova = df_cities_costs.loc[(df_cities_costs['city_from_name'] == \"Dusseldorf\") & (df_cities_costs['city_to_name'] == \"Zaragoza\") & (df_cities_costs[\"weight_class\"] == 0)]\n",
        "df_prova.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 411,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWkCBh5gv9nA",
        "outputId": "7d2819ff-9a10-4c47-ddd3-3f7f9e9670d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n",
            "1000\n",
            "2000\n",
            "3000\n",
            "4000\n",
            "5000\n",
            "6000\n",
            "7000\n",
            "8000\n",
            "9000\n",
            "10000\n",
            "11000\n",
            "12000\n",
            "13000\n",
            "14000\n",
            "15000\n",
            "16000\n",
            "17000\n",
            "18000\n",
            "19000\n",
            "20000\n",
            "21000\n",
            "22000\n",
            "23000\n",
            "24000\n",
            "25000\n",
            "26000\n",
            "27000\n",
            "28000\n",
            "144\n"
          ]
        }
      ],
      "source": [
        "cont = 0;\n",
        "p = 0\n",
        "for index, row in df_copy_2.iterrows():\n",
        "    if(cont % 1000 == 0):\n",
        "      print(cont)\n",
        "    \n",
        "    if(row[\"logistic_hub\"] == \"-1\"):\n",
        "        df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"customer\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        if(df_1.empty):\n",
        "            df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"customer\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        \n",
        "        if(df_1.empty):\n",
        "          p = p+1\n",
        "        else:\n",
        "          n = df_1[\"distance\"].values\n",
        "          n = n[0]\n",
        "          df_copy_2.at[index, \"distance\"] = n\n",
        "    else:\n",
        "        df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        if(df_1.empty):\n",
        "            df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "  \n",
        "        df_2 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"customer\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        if(df_2.empty):\n",
        "            df_2 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"customer\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        \n",
        "        if(df_1.empty or df_2.empty):\n",
        "          p = p+1\n",
        "        else:\n",
        "          n = df_1[\"distance\"].values\n",
        "          n = n[0]\n",
        "          #print(n)\n",
        "          m = df_2[\"distance\"].values\n",
        "          m = m[0]\n",
        "          #print(m)\n",
        "          df_copy_2.at[index, \"distance\"] = n+m\n",
        "    cont = cont+1\n",
        "\n",
        "df_copy_2.head()\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 412,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "twjc07gp8xkR",
        "outputId": "e21da84c-ab67-4685-eded-07cbc1c41a60"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "city_from_name      object\n",
              "city_to_name        object\n",
              "city_from_coord     object\n",
              "city_to_coord       object\n",
              "distance           float64\n",
              "weight_class         int64\n",
              "cost_per_unit      float64\n",
              "co2_per_unit       float64\n",
              "dtype: object"
            ]
          },
          "execution_count": 412,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cities_costs.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 413,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SQ9CBYuB7A-s",
        "outputId": "ec59e247-deaf-4bbf-d702-7c5bd766fad3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "990\n"
          ]
        }
      ],
      "source": [
        "cont = 0;\n",
        "p = 0\n",
        "for index, row in df_copy_2.iterrows():\n",
        "    if(not np.isnan(row[\"distance\"])):\n",
        "        continue\n",
        "    for prova in (5,4,3,6,2,7,1,8,0,9):\n",
        "      if(np.isnan(row[\"distance\"])):\n",
        "        if(row[\"logistic_hub\"] == \"-1\"):\n",
        "            df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"origin_port\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"customer\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "            if(df_1.empty):\n",
        "                df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"customer\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"origin_port\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "            \n",
        "            if(df_1.empty):\n",
        "              p = p+1\n",
        "            else:\n",
        "              n = df_1[\"distance\"].values\n",
        "              n = n[0]\n",
        "              df_copy_2.at[index, \"distance\"] = n\n",
        "        else:\n",
        "            df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"origin_port\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"logistic_hub\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "            if(df_1.empty):\n",
        "                df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"logistic_hub\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"origin_port\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "      \n",
        "            df_2 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"logistic_hub\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"customer\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "            if(df_2.empty):\n",
        "                df_2 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"customer\"]) & \n",
        "                                          (df_cities_costs['city_to_name'] == row[\"logistic_hub\"]) & \n",
        "                                          (df_cities_costs[\"weight_class\"] == prova)]\n",
        "            \n",
        "            if(df_1.empty or df_2.empty):\n",
        "              p = p+1\n",
        "            else:\n",
        "              n = df_1[\"distance\"].values\n",
        "              n = n[0]\n",
        "              #print(n)\n",
        "              m = df_2[\"distance\"].values\n",
        "              m = m[0]\n",
        "              #print(m)\n",
        "              df_copy_2.at[index, \"distance\"] = n+m\n",
        "      else:\n",
        "        continue\n",
        "    cont = cont+1\n",
        "\n",
        "df_copy_2.head()\n",
        "print(p)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 414,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Uoyk0MunD0IO",
        "outputId": "2455c842-0859-402e-d200-d81a380ca78d"
      },
      "outputs": [
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              "          document.querySelector('#df-35589bd4-eca3-4260-90c6-9ba1f105f418 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-35589bd4-eca3-4260-90c6-9ba1f105f418');\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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub   customer  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw     Munich   \n",
              "1  6c8c11251114   Rotterdam  v_002                CRF        Venlo  Barcelona   \n",
              "2  81d3be5e1eff      Athens  v_002                DTD       Warsaw       Lyon   \n",
              "3  870a9b65a1b5   Rotterdam  v_001                CRF   Dusseldorf     Berlin   \n",
              "4  eff072956d66   Rotterdam  v_002                CRF         Rome      Milan   \n",
              "\n",
              "  product_id  units  weight  material_handling weight_class   distance  \\\n",
              "0    1687644      4  1974.0                  5            8  1951.6946   \n",
              "1    1687644      1  1974.0                  5            8  1283.4637   \n",
              "2    1687644      8  1974.0                  5            8  2982.0120   \n",
              "3    1687644      8  1974.0                  5            8   654.6792   \n",
              "4    1687644      0  1974.0                  5            8  1746.5587   \n",
              "\n",
              "   cost_per_unit  co2_per_unit  \n",
              "0            NaN           NaN  \n",
              "1            NaN           NaN  \n",
              "2            NaN           NaN  \n",
              "3            NaN           NaN  \n",
              "4            NaN           NaN  "
            ]
          },
          "execution_count": 414,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_copy_2.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 415,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "WQvbfyIZEBUF",
        "outputId": "545885b0-5959-43a2-d5a9-5d95ab831d7c"
      },
      "outputs": [
        {
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              "      <th>26487</th>\n",
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              "      <td>Rome</td>\n",
              "      <td>1700980</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>Athens</td>\n",
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              "      <td>1699952</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>41581c779a7b</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>99 rows × 14 columns</p>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "          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",
              "  "
            ],
            "text/plain": [
              "           order_id origin_port    3pl customs_procedures logistic_hub  \\\n",
              "71     df9f2ffbd271   Rotterdam  v_002                CRF         Rome   \n",
              "527    22d9c2dbbcaf   Rotterdam  v_004                CRF         Rome   \n",
              "721    d320bcfd7560      Athens  v_004                DTD         Rome   \n",
              "1414   e15541bfdb2a   Rotterdam  v_004                CRF         Rome   \n",
              "1478   af8757fdab9e   Rotterdam  v_004                DTP         Rome   \n",
              "...             ...         ...    ...                ...          ...   \n",
              "26487  30d2cf2bfdca   Barcelona  v_002                CRF         Rome   \n",
              "26614  ac8f11abbd29   Rotterdam  v_002                CRF         Rome   \n",
              "26672  22f0dea640cb   Barcelona  v_002                CRF         Rome   \n",
              "27263  d08543385067      Athens  v_002                CRF         Rome   \n",
              "28410  41581c779a7b   Rotterdam  v_002                CRF         Rome   \n",
              "\n",
              "      customer product_id  units  weight  material_handling weight_class  \\\n",
              "71        Rome    1669740      1  2072.0                  2            9   \n",
              "527       Rome    1644308      9  1088.0                  3            3   \n",
              "721       Rome    1644308      8  1088.0                  3            3   \n",
              "1414      Rome    1651366      5  1829.0                  5            8   \n",
              "1478      Rome    1660580      8  1889.0                  0            8   \n",
              "...        ...        ...    ...     ...                ...          ...   \n",
              "26487     Rome    1668347      0  1698.0                  5            7   \n",
              "26614     Rome    1650586      5   761.0                  0            1   \n",
              "26672     Rome    1700980      5  1443.0                  2            6   \n",
              "27263     Rome    1699952      9  1551.0                  4            6   \n",
              "28410     Rome    1683430      9  1649.0                  3            7   \n",
              "\n",
              "       distance  cost_per_unit  co2_per_unit  \n",
              "71          NaN            NaN           NaN  \n",
              "527         NaN            NaN           NaN  \n",
              "721         NaN            NaN           NaN  \n",
              "1414        NaN            NaN           NaN  \n",
              "1478        NaN            NaN           NaN  \n",
              "...         ...            ...           ...  \n",
              "26487       NaN            NaN           NaN  \n",
              "26614       NaN            NaN           NaN  \n",
              "26672       NaN            NaN           NaN  \n",
              "27263       NaN            NaN           NaN  \n",
              "28410       NaN            NaN           NaN  \n",
              "\n",
              "[99 rows x 14 columns]"
            ]
          },
          "execution_count": 415,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_copy_2.loc[df_copy_2['distance'].isna()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 416,
      "metadata": {
        "id": "yK6ZspXtKMd5"
      },
      "outputs": [],
      "source": [
        "copy3 = df_copy_2\n",
        "for index, row in copy3.iterrows():\n",
        "    if((row[\"logistic_hub\"] == row[\"customer\"]) & (row[\"logistic_hub\"] != row[\"origin_port\"])):\n",
        "        df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        if(df_1.empty):\n",
        "            df_1 = df_cities_costs.loc[(df_cities_costs['city_from_name'] == row[\"logistic_hub\"]) & \n",
        "                                       (df_cities_costs['city_to_name'] == row[\"origin_port\"]) & \n",
        "                                       (df_cities_costs[\"weight_class\"] == row[\"weight_class\"])]\n",
        "        \n",
        "        if(df_1.empty):\n",
        "          p = p+1\n",
        "        else:\n",
        "          n = df_1[\"distance\"].values\n",
        "          n = n[0]\n",
        "          copy3.at[index, \"distance\"] = n\n",
        "    elif((row[\"logistic_hub\"] == \"-1\") & (row[\"customer\"] == row[\"origin_port\"])):\n",
        "        copy3.at[index, \"distance\"] = 0"
      ]
    },
    {
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      "execution_count": 417,
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        "id": "2PXJFF1WK_DW",
        "outputId": "5250d8b3-f15e-4fe5-f92e-fe9e894f837f"
      },
      "outputs": [
        {
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              "Empty DataFrame\n",
              "Columns: [order_id, origin_port, 3pl, customs_procedures, logistic_hub, customer, product_id, units, weight, material_handling, weight_class, distance, cost_per_unit, co2_per_unit]\n",
              "Index: []"
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        "copy3.loc[copy3['distance'].isna()]"
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5376ada-032c-4315-9024-13cc079af5ca');\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",
              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub   customer  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw     Munich   \n",
              "1  6c8c11251114   Rotterdam  v_002                CRF        Venlo  Barcelona   \n",
              "2  81d3be5e1eff      Athens  v_002                DTD       Warsaw       Lyon   \n",
              "3  870a9b65a1b5   Rotterdam  v_001                CRF   Dusseldorf     Berlin   \n",
              "4  eff072956d66   Rotterdam  v_002                CRF         Rome      Milan   \n",
              "\n",
              "  product_id  units  weight  material_handling weight_class   distance  \\\n",
              "0    1687644      4  1974.0                  5            8  1951.6946   \n",
              "1    1687644      1  1974.0                  5            8  1283.4637   \n",
              "2    1687644      8  1974.0                  5            8  2982.0120   \n",
              "3    1687644      8  1974.0                  5            8   654.6792   \n",
              "4    1687644      0  1974.0                  5            8  1746.5587   \n",
              "\n",
              "   cost_per_unit  co2_per_unit  \n",
              "0            NaN           NaN  \n",
              "1            NaN           NaN  \n",
              "2            NaN           NaN  \n",
              "3            NaN           NaN  \n",
              "4            NaN           NaN  "
            ]
          },
          "execution_count": 418,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "copy3.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 419,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQof7QZiL5eo",
        "outputId": "c956cbbb-ca4e-40db-dd61-4427c518744c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(copy3.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 420,
      "metadata": {
        "id": "-ZT3w1rgMN45"
      },
      "outputs": [],
      "source": [
        "df = copy3.loc[:, (copy3.columns != \"cost_per_unit\") & (copy3.columns != \"co2_per_unit\")]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 421,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "Aj00mR-AMZWb",
        "outputId": "508bfe52-b26d-467a-f755-b553119320ec"
      },
      "outputs": [
        {
          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>origin_port</th>\n",
              "      <th>3pl</th>\n",
              "      <th>customs_procedures</th>\n",
              "      <th>logistic_hub</th>\n",
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              "      <th>material_handling</th>\n",
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              "    <tr>\n",
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              "      <td>81d3be5e1eff</td>\n",
              "      <td>Athens</td>\n",
              "      <td>v_002</td>\n",
              "      <td>DTD</td>\n",
              "      <td>Warsaw</td>\n",
              "      <td>Lyon</td>\n",
              "      <td>1687644</td>\n",
              "      <td>8</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>2982.0120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>870a9b65a1b5</td>\n",
              "      <td>Rotterdam</td>\n",
              "      <td>v_001</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Dusseldorf</td>\n",
              "      <td>Berlin</td>\n",
              "      <td>1687644</td>\n",
              "      <td>8</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>654.6792</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>eff072956d66</td>\n",
              "      <td>Rotterdam</td>\n",
              "      <td>v_002</td>\n",
              "      <td>CRF</td>\n",
              "      <td>Rome</td>\n",
              "      <td>Milan</td>\n",
              "      <td>1687644</td>\n",
              "      <td>0</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>5</td>\n",
              "      <td>8</td>\n",
              "      <td>1746.5587</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f0056d35-59e9-47e1-ae7a-b77d634cb612')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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",
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              "\n",
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              "          document.querySelector('#df-f0056d35-59e9-47e1-ae7a-b77d634cb612 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-f0056d35-59e9-47e1-ae7a-b77d634cb612');\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",
              "  "
            ],
            "text/plain": [
              "       order_id origin_port    3pl customs_procedures logistic_hub   customer  \\\n",
              "0  0e364fa5c795   Rotterdam  v_002                CRF       Warsaw     Munich   \n",
              "1  6c8c11251114   Rotterdam  v_002                CRF        Venlo  Barcelona   \n",
              "2  81d3be5e1eff      Athens  v_002                DTD       Warsaw       Lyon   \n",
              "3  870a9b65a1b5   Rotterdam  v_001                CRF   Dusseldorf     Berlin   \n",
              "4  eff072956d66   Rotterdam  v_002                CRF         Rome      Milan   \n",
              "\n",
              "  product_id  units  weight  material_handling weight_class   distance  \n",
              "0    1687644      4  1974.0                  5            8  1951.6946  \n",
              "1    1687644      1  1974.0                  5            8  1283.4637  \n",
              "2    1687644      8  1974.0                  5            8  2982.0120  \n",
              "3    1687644      8  1974.0                  5            8   654.6792  \n",
              "4    1687644      0  1974.0                  5            8  1746.5587  "
            ]
          },
          "execution_count": 421,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 422,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 113
        },
        "id": "sMjhAhn0Mdbb",
        "outputId": "6ffe5e11-38a3-421f-99a3-bff046988a39"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
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              "    }\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>order_id</th>\n",
              "      <th>origin_port</th>\n",
              "      <th>3pl</th>\n",
              "      <th>customs_procedures</th>\n",
              "      <th>logistic_hub</th>\n",
              "      <th>customer</th>\n",
              "      <th>product_id</th>\n",
              "      <th>units</th>\n",
              "      <th>weight</th>\n",
              "      <th>material_handling</th>\n",
              "      <th>weight_class</th>\n",
              "      <th>distance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a8371a1-7e60-4ff3-8118-bdb230f23f95')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a8371a1-7e60-4ff3-8118-bdb230f23f95 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-9a8371a1-7e60-4ff3-8118-bdb230f23f95');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [order_id, origin_port, 3pl, customs_procedures, logistic_hub, customer, product_id, units, weight, material_handling, weight_class, distance]\n",
              "Index: []"
            ]
          },
          "execution_count": 422,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.loc[df['distance'].isna()]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 424,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lGYF1wUT-KwL",
        "outputId": "29ab96b2-643e-482f-8d9f-24f1aac46cc2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of Rows: 28563\n"
          ]
        }
      ],
      "source": [
        "rows = len(df.axes[0]) \n",
        "  \n",
        "print(\"Number of Rows: \" + str(rows)) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 423,
      "metadata": {
        "id": "jYiG3KAoMf3A"
      },
      "outputs": [],
      "source": [
        "df.to_csv(\"test_dataframe.csv\", index=False)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.11.0 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    },
    "vscode": {
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  "nbformat": 4,
  "nbformat_minor": 0
}