Shipments_prueba.ipynb

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  "metadata": {
    "colab": {
      "name": "Shipments_prueba.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mgobeaalcoba/Exploration_Data_Analyst/blob/main/Shipments_prueba.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RCMj84Tve9vS"
      },
      "source": [
        "import pandas as pd "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shipment_data = '/content/e1c422b3-89a6-11ec-bf4e-0242ac130004.csv'"
      ],
      "metadata": {
        "id": "qHQkvxbkcnci"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Euv-P9-9Ed2q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d9ae9625-a966-4e1b-9cca-780684919153"
      },
      "source": [
        "shipments_df = pd.read_csv(shipment_data)\n",
        "shipments_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2882: DtypeWarning: Columns (43,44,45,46,50,51,52) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        },
        {
          "output_type": "execute_result",
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              "      <th></th>\n",
              "      <th>Shipment ID</th>\n",
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              "      <td>ab949ww</td>\n",
              "      <td>2.230626e+15</td>\n",
              "      <td>2.230715e+15</td>\n",
              "      <td>01/02/2022 06:58:53</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dispatched</td>\n",
              "      <td>NaN</td>\n",
              "      <td>01/02/2022 13:39:05</td>\n",
              "      <td>01/02/2022 13:45:19</td>\n",
              "      <td>01/02/2022 14:02:19</td>\n",
              "      <td>loginter</td>\n",
              "      <td>ME.AR.ARIVEROS(173030)</td>\n",
              "      <td>1010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>01/02/2022 13:45:20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>878.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>30.0</td>\n",
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              "      <td>Urbano</td>\n",
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              "      <td>urb167</td>\n",
              "      <td>DVPQ01010000000027188742</td>\n",
              "      <td>01/02/2022 04:39:09</td>\n",
              "      <td>01/02/2022 14:30:30</td>\n",
              "      <td>01/02/2022 15:54:53</td>\n",
              "      <td>[WMS.FACUNDOSIL(10506), CAC.PABLOGARAY(35075),...</td>\n",
              "      <td>CAC.ALEXEVI(10369)</td>\n",
              "      <td>132D</td>\n",
              "      <td>01/02/2022 23:30:14</td>\n",
              "      <td>01/02/2022 23:30:02</td>\n",
              "      <td>AC735WA</td>\n",
              "      <td>WMS.LEOGUZ(9602)</td>\n",
              "      <td>25182</td>\n",
              "      <td>True</td>\n",
              "      <td>NPyM</td>\n",
              "      <td>180712342.0</td>\n",
              "      <td>195456640.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>01:00</td>\n",
              "      <td>01/02/2022 01:00:00</td>\n",
              "      <td>02/02/2022 01:00:00</td>\n",
              "      <td>cross_docking</td>\n",
              "      <td>NaN</td>\n",
              "      <td>delivered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39449</th>\n",
              "      <td>41148897081</td>\n",
              "      <td>792970ef-a076-4c62-b23b-35f571e60b58</td>\n",
              "      <td>ae230wr</td>\n",
              "      <td>2.230590e+15</td>\n",
              "      <td>2.230609e+14</td>\n",
              "      <td>30/01/2022 22:58:17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dispatched</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 14:32:48</td>\n",
              "      <td>31/01/2022 15:00:53</td>\n",
              "      <td>31/01/2022 15:16:54</td>\n",
              "      <td>loginter</td>\n",
              "      <td>ME.AR.AROMANO(109444)</td>\n",
              "      <td>4022</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 15:00:54</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>2725.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>Punto HOP</td>\n",
              "      <td>145041</td>\n",
              "      <td>Retiro en una sucursal</td>\n",
              "      <td>hop300</td>\n",
              "      <td>3507a7</td>\n",
              "      <td>31/01/2022 21:16:30</td>\n",
              "      <td>31/01/2022 23:13:56</td>\n",
              "      <td>01/02/2022 01:38:13</td>\n",
              "      <td>[WMS.CAMILAMARTI(9547), WMS.AILENNU(11367), ME...</td>\n",
              "      <td>ME.AR.MPERE(75923)</td>\n",
              "      <td>108C</td>\n",
              "      <td>01/02/2022 01:52:33</td>\n",
              "      <td>01/02/2022 01:51:50</td>\n",
              "      <td>GKA701</td>\n",
              "      <td>ME.AR.ROVILLEGAS(93791)</td>\n",
              "      <td>25174</td>\n",
              "      <td>True</td>\n",
              "      <td>NPyM</td>\n",
              "      <td>152667538.0</td>\n",
              "      <td>120499623.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>02:00</td>\n",
              "      <td>31/01/2022 02:00:00</td>\n",
              "      <td>01/02/2022 02:00:00</td>\n",
              "      <td>cross_docking</td>\n",
              "      <td>NaN</td>\n",
              "      <td>delivered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39450</th>\n",
              "      <td>41145976099</td>\n",
              "      <td>69ceca9d-3fcb-4275-89f3-1c39c3f1ebb8</td>\n",
              "      <td>ae993xw</td>\n",
              "      <td>2.230585e+15</td>\n",
              "      <td>2.230605e+15</td>\n",
              "      <td>31/01/2022 11:14:04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dispatched</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 19:09:04</td>\n",
              "      <td>31/01/2022 19:13:05</td>\n",
              "      <td>31/01/2022 19:38:21</td>\n",
              "      <td>punto hop</td>\n",
              "      <td>ME.AR.MVALIENTE(118114)</td>\n",
              "      <td>1010</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 19:13:07</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>1138.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>Urbano</td>\n",
              "      <td>144772</td>\n",
              "      <td>Prioritario</td>\n",
              "      <td>urb164</td>\n",
              "      <td>DVPQ01010000000027134335</td>\n",
              "      <td>31/01/2022 20:10:07</td>\n",
              "      <td>31/01/2022 20:19:06</td>\n",
              "      <td>31/01/2022 21:59:50</td>\n",
              "      <td>[ME.AR.SLUGO(133750), ME.AR.EFERREIRA(196283),...</td>\n",
              "      <td>ME.AR.SLUGO(133750)</td>\n",
              "      <td>132C</td>\n",
              "      <td>01/02/2022 00:44:34</td>\n",
              "      <td>01/02/2022 00:44:24</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WMS.ALEJOFLEY(9522)</td>\n",
              "      <td>25181</td>\n",
              "      <td>True</td>\n",
              "      <td>NPyM</td>\n",
              "      <td>396874149.0</td>\n",
              "      <td>180523845.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>01:00</td>\n",
              "      <td>31/01/2022 01:00:00</td>\n",
              "      <td>01/02/2022 01:00:00</td>\n",
              "      <td>xd_drop_off</td>\n",
              "      <td>NaN</td>\n",
              "      <td>delivered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39451</th>\n",
              "      <td>41146868256</td>\n",
              "      <td>3146da32-479a-4613-aa58-4981aa9bff6c</td>\n",
              "      <td>nzg247</td>\n",
              "      <td>2.230658e+15</td>\n",
              "      <td>2.230658e+15</td>\n",
              "      <td>31/01/2022 08:10:56</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dispatched</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 17:24:17</td>\n",
              "      <td>31/01/2022 17:50:41</td>\n",
              "      <td>31/01/2022 18:12:27</td>\n",
              "      <td>OCASA Logistics</td>\n",
              "      <td>CAC.CAMILAVELAZQUEZ(10145)</td>\n",
              "      <td>1006</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 17:50:42</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>150.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>OCASA</td>\n",
              "      <td>801</td>\n",
              "      <td>Colecta Retiro Sucursal</td>\n",
              "      <td>pck390</td>\n",
              "      <td>EC2DX26820222</td>\n",
              "      <td>01/02/2022 11:11:28</td>\n",
              "      <td>01/02/2022 11:11:32</td>\n",
              "      <td>01/02/2022 11:42:37</td>\n",
              "      <td>[WMS.TATISOTE(55794), ME.AR.PASEN(211276)]</td>\n",
              "      <td>ME.AR.PASEN(211276)</td>\n",
              "      <td>802Z</td>\n",
              "      <td>01/02/2022 11:53:51</td>\n",
              "      <td>01/02/2022 11:53:49</td>\n",
              "      <td>GEH188</td>\n",
              "      <td>CAC.FRANCOGRE(9785)</td>\n",
              "      <td>25178</td>\n",
              "      <td>True</td>\n",
              "      <td>NPyM</td>\n",
              "      <td>85812242.0</td>\n",
              "      <td>57604962.0</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>none</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>True</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>12:00</td>\n",
              "      <td>31/01/2022 12:00:00</td>\n",
              "      <td>01/02/2022 12:00:00</td>\n",
              "      <td>cross_docking</td>\n",
              "      <td>NaN</td>\n",
              "      <td>delivered</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39452</th>\n",
              "      <td>41144191697</td>\n",
              "      <td>7200aa07-b84c-4ef8-8749-31cad8200fac</td>\n",
              "      <td>ad929px</td>\n",
              "      <td>2.230571e+15</td>\n",
              "      <td>2.230637e+15</td>\n",
              "      <td>28/01/2022 11:22:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>dispatched</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 17:27:33</td>\n",
              "      <td>31/01/2022 17:33:46</td>\n",
              "      <td>31/01/2022 17:57:14</td>\n",
              "      <td>loginter</td>\n",
              "      <td>CAC.FIORELLABAIZ(11244)</td>\n",
              "      <td>1018</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31/01/2022 17:33:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>7860.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>Andreani</td>\n",
              "      <td>345328</td>\n",
              "      <td>Andreani Prioritario</td>\n",
              "      <td>and026</td>\n",
              "      <td>360000401055680</td>\n",
              "      <td>31/01/2022 17:23:03</td>\n",
              "      <td>31/01/2022 23:05:10</td>\n",
              "      <td>01/02/2022 00:56:51</td>\n",
              "      <td>[CAC.ROCIOALEGRE(9894), ME.AR.ELCABRERA9(20811...</td>\n",
              "      <td>ME.AR.JMVILELA(103449)</td>\n",
              "      <td>802F</td>\n",
              "      <td>01/02/2022 07:32:59</td>\n",
              "      <td>01/02/2022 07:32:51</td>\n",
              "      <td>KYS631</td>\n",
              "      <td>WMS.SERGIOCOR(11020)</td>\n",
              "      <td>25172</td>\n",
              "      <td>True</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>39453 rows × 59 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5348a561-a962-45d1-9da3-90901a70ac56')\"\n",
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            ],
            "text/plain": [
              "       Shipment ID  ...   Status Description\n",
              "0      41145786437  ...  returning_to_sender\n",
              "1      41149782687  ...               in_hub\n",
              "2      41145394143  ...            delivered\n",
              "3      41145494510  ...  returning_to_sender\n",
              "4      41146310697  ...  returning_to_sender\n",
              "...            ...  ...                  ...\n",
              "39448  41149673932  ...            delivered\n",
              "39449  41148897081  ...            delivered\n",
              "39450  41145976099  ...            delivered\n",
              "39451  41146868256  ...            delivered\n",
              "39452  41144191697  ...                  NaN\n",
              "\n",
              "[39453 rows x 59 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fsZRIJUndHjR",
        "outputId": "a02d3127-39ed-4323-cd92-0cdf7579fcf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 39453 entries, 0 to 39452\n",
            "Data columns (total 59 columns):\n",
            " #   Column                       Non-Null Count  Dtype  \n",
            "---  ------                       --------------  -----  \n",
            " 0   Shipment ID                  39453 non-null  int64  \n",
            " 1   Inbound ID                   39453 non-null  object \n",
            " 2   Truck ID                     39453 non-null  object \n",
            " 3   Outbound ID                  39280 non-null  float64\n",
            " 4   Dispatch ID                  39280 non-null  float64\n",
            " 5   Date Created                 39453 non-null  object \n",
            " 6   NFE                          0 non-null      float64\n",
            " 7   Hub Status                   39453 non-null  object \n",
            " 8   Reject Reason                31 non-null     object \n",
            " 9   Inbound Date Opened          39453 non-null  object \n",
            " 10  Inbound Date Included        39453 non-null  object \n",
            " 11  Inbound Date Closed          39084 non-null  object \n",
            " 12  Inbound Carrier Name         39453 non-null  object \n",
            " 13  Inbound User ID              39453 non-null  object \n",
            " 14  Inbound Dock ID              39453 non-null  int64  \n",
            " 15  Labeling Date Printed        1073 non-null   object \n",
            " 16  Labeling Authorization Date  39418 non-null  object \n",
            " 17  Labeling Last Print User     1073 non-null   object \n",
            " 18  Labeling Workstation ID      39453 non-null  int64  \n",
            " 19  Weight                       39453 non-null  float64\n",
            " 20  Height                       39453 non-null  float64\n",
            " 21  Length                       39453 non-null  float64\n",
            " 22  Width                        39453 non-null  float64\n",
            " 23  Labeling Carrier Name        39418 non-null  object \n",
            " 24  Labeling Service Id          39453 non-null  int64  \n",
            " 25  Labeling Service Name        39418 non-null  object \n",
            " 26  Labeling Zone                39420 non-null  object \n",
            " 27  Tracking Number              39451 non-null  object \n",
            " 28  Outbound Date Opened         39280 non-null  object \n",
            " 29  Outbound Included Date       39280 non-null  object \n",
            " 30  Outbound Date Closed         39280 non-null  object \n",
            " 31  Outbound User IDs            39280 non-null  object \n",
            " 32  Outbound Added By            39280 non-null  object \n",
            " 33  Outbound Position            39280 non-null  object \n",
            " 34  Dispatch Included Date       39280 non-null  object \n",
            " 35  Dispatch Date Finished       39280 non-null  object \n",
            " 36  Dispatch Truck ID            37480 non-null  object \n",
            " 37  Dispatch User ID             39280 non-null  object \n",
            " 38  Dispatch Dock ID             39453 non-null  int64  \n",
            " 39  Priority                     39453 non-null  bool   \n",
            " 40  Process Type                 39452 non-null  object \n",
            " 41  Seller ID                    39452 non-null  float64\n",
            " 42  Buyer ID                     39452 non-null  float64\n",
            " 43  Divergence                   39452 non-null  object \n",
            " 44  LastMile                     39452 non-null  object \n",
            " 45  LastMile Reimpressão         39452 non-null  object \n",
            " 46  InBuffer                     39452 non-null  object \n",
            " 47  InBuffer Name                39419 non-null  object \n",
            " 48  InBuffer Promise EDT         39419 non-null  float64\n",
            " 49  InBuffer Promise Shipping    0 non-null      float64\n",
            " 50  Arrived LastMile             39452 non-null  object \n",
            " 51  Arrived Npym                 39452 non-null  object \n",
            " 52  Was Reauthorized             39452 non-null  object \n",
            " 53  Cutoff                       39452 non-null  object \n",
            " 54  Start Priorization           39409 non-null  object \n",
            " 55  End Priorization             39409 non-null  object \n",
            " 56  Arrival Logistic Type        38121 non-null  object \n",
            " 57  Arrival Tracking Number      0 non-null      float64\n",
            " 58  Status Description           39440 non-null  object \n",
            "dtypes: bool(1), float64(12), int64(5), object(41)\n",
            "memory usage: 17.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "shipments_df[\"Ones\"] = np.ones(len(shipments_df[\"Shipment ID\"]))"
      ],
      "metadata": {
        "id": "9a0dqJztdHdb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "id": "cijcmqG4dHZJ",
        "outputId": "c113219f-911c-4363-b4a6-affad073d04c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>[ME.AR.RMCABRERA(208038), ME.AR.NPONCE(196278)...</td>\n",
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              "    <tr>\n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2656e33-b064-4088-8293-4ed7814f72ab')\"\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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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Shipment ID                            Inbound ID  ...   Status Description  Ones\n",
              "0  41145786437  6e31a180-0fb0-459b-980f-04ff475f6317  ...  returning_to_sender   1.0\n",
              "1  41149782687  0decfbad-bc50-40e4-855d-4bc613ac7b3e  ...               in_hub   1.0\n",
              "2  41145394143  9764eabe-e868-4bed-a8c8-c1361fb7d32f  ...            delivered   1.0\n",
              "3  41145494510  3de36351-a5ba-43aa-9684-3da52135d3b0  ...  returning_to_sender   1.0\n",
              "4  41146310697  3de36351-a5ba-43aa-9684-3da52135d3b0  ...  returning_to_sender   1.0\n",
              "\n",
              "[5 rows x 60 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# plt.bar()\n",
        "# (shipments_df[\"Hub Status\"],shipments_df[\"Shipment ID\"],)"
      ],
      "metadata": {
        "id": "gh4P41BneL8d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "QOOgvUgkeMr3",
        "outputId": "b425354f-732e-4bfe-bf6e-843030c93d85"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <tbody>\n",
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              "      <td>340.781740</td>\n",
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              "      <td>10.723828</td>\n",
              "      <td>16.458042</td>\n",
              "      <td>11.071859</td>\n",
              "      <td>119934.005648</td>\n",
              "      <td>11209.436981</td>\n",
              "      <td>1.778109e+08</td>\n",
              "      <td>2.245339e+08</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>min</th>\n",
              "      <td>4.089896e+10</td>\n",
              "      <td>2.230712e+12</td>\n",
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              "      <td>1001.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>2.231388e+15</td>\n",
              "      <td>2.231442e+15</td>\n",
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              "      <td>4035.000000</td>\n",
              "      <td>3046.000000</td>\n",
              "      <td>30000.000000</td>\n",
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              "      <td>150.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>345328.000000</td>\n",
              "      <td>71196.000000</td>\n",
              "      <td>1.052870e+09</td>\n",
              "      <td>1.068771e+09</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "          const element = document.querySelector('#df-cb47a160-be51-4bfe-a968-d84411d4e606');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        Shipment ID   Outbound ID  ...  Arrival Tracking Number     Ones\n",
              "count  3.945300e+04  3.928000e+04  ...                      0.0  39453.0\n",
              "mean   4.115144e+10  2.043056e+15  ...                      NaN      1.0\n",
              "std    5.920715e+06  5.882871e+14  ...                      NaN      0.0\n",
              "min    4.089896e+10  2.230712e+12  ...                      NaN      1.0\n",
              "25%    4.114808e+10  2.230575e+15  ...                      NaN      1.0\n",
              "50%    4.115154e+10  2.230698e+15  ...                      NaN      1.0\n",
              "75%    4.115530e+10  2.230882e+15  ...                      NaN      1.0\n",
              "max    4.116199e+10  2.231388e+15  ...                      NaN      1.0\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.set_option('display.float_format', lambda x: '%.2f' % x) # script para eliminar la notación cientifica en pandas"
      ],
      "metadata": {
        "id": "9Oqj9lFoeMpL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "naxFLcWheMk_",
        "outputId": "7d68a8d8-caa3-44d7-95d5-f27bd839cb79"
      },
      "execution_count": null,
      "outputs": [
        {
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              "      <th>Seller ID</th>\n",
              "      <th>Buyer ID</th>\n",
              "      <th>InBuffer Promise EDT</th>\n",
              "      <th>InBuffer Promise Shipping</th>\n",
              "      <th>Arrival Tracking Number</th>\n",
              "      <th>Ones</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>39453.00</td>\n",
              "      <td>39280.00</td>\n",
              "      <td>39280.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39453.00</td>\n",
              "      <td>39452.00</td>\n",
              "      <td>39452.00</td>\n",
              "      <td>39419.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>39453.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>41151440622.85</td>\n",
              "      <td>2043056259758583.00</td>\n",
              "      <td>2008184396580183.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2142.69</td>\n",
              "      <td>56.59</td>\n",
              "      <td>2168.75</td>\n",
              "      <td>13.69</td>\n",
              "      <td>33.24</td>\n",
              "      <td>23.49</td>\n",
              "      <td>114987.77</td>\n",
              "      <td>28270.60</td>\n",
              "      <td>211030116.26</td>\n",
              "      <td>259267917.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5920715.13</td>\n",
              "      <td>588287143055512.88</td>\n",
              "      <td>636103406210792.25</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1453.53</td>\n",
              "      <td>340.78</td>\n",
              "      <td>3708.64</td>\n",
              "      <td>10.72</td>\n",
              "      <td>16.46</td>\n",
              "      <td>11.07</td>\n",
              "      <td>119934.01</td>\n",
              "      <td>11209.44</td>\n",
              "      <td>177810891.22</td>\n",
              "      <td>224533851.84</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>40898958958.00</td>\n",
              "      <td>2230711693020.00</td>\n",
              "      <td>22306079628186.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1001.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1276.00</td>\n",
              "      <td>10117.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>41148080613.00</td>\n",
              "      <td>2230574859006559.00</td>\n",
              "      <td>2230614162018255.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1009.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>344.00</td>\n",
              "      <td>7.00</td>\n",
              "      <td>22.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>351.00</td>\n",
              "      <td>25169.00</td>\n",
              "      <td>79483130.50</td>\n",
              "      <td>91767415.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>41151539408.00</td>\n",
              "      <td>2230697968449286.00</td>\n",
              "      <td>2230731838904395.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1017.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>800.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>21.00</td>\n",
              "      <td>144772.00</td>\n",
              "      <td>25174.00</td>\n",
              "      <td>172177242.00</td>\n",
              "      <td>190306775.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>41155297743.00</td>\n",
              "      <td>2230881995842207.00</td>\n",
              "      <td>2230951355825531.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4009.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2100.00</td>\n",
              "      <td>17.00</td>\n",
              "      <td>42.00</td>\n",
              "      <td>30.00</td>\n",
              "      <td>158662.00</td>\n",
              "      <td>25179.00</td>\n",
              "      <td>272178759.25</td>\n",
              "      <td>357224476.50</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41161988872.00</td>\n",
              "      <td>2231387503941390.00</td>\n",
              "      <td>2231441990593275.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4035.00</td>\n",
              "      <td>3046.00</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>88.00</td>\n",
              "      <td>150.00</td>\n",
              "      <td>85.00</td>\n",
              "      <td>345328.00</td>\n",
              "      <td>71196.00</td>\n",
              "      <td>1052870116.00</td>\n",
              "      <td>1068771443.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ],
            "text/plain": [
              "         Shipment ID         Outbound ID  ...  Arrival Tracking Number     Ones\n",
              "count       39453.00            39280.00  ...                     0.00 39453.00\n",
              "mean  41151440622.85 2043056259758583.00  ...                      NaN     1.00\n",
              "std       5920715.13  588287143055512.88  ...                      NaN     0.00\n",
              "min   40898958958.00    2230711693020.00  ...                      NaN     1.00\n",
              "25%   41148080613.00 2230574859006559.00  ...                      NaN     1.00\n",
              "50%   41151539408.00 2230697968449286.00  ...                      NaN     1.00\n",
              "75%   41155297743.00 2230881995842207.00  ...                      NaN     1.00\n",
              "max   41161988872.00 2231387503941390.00  ...                      NaN     1.00\n",
              "\n",
              "[8 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  shipments_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AL7zv_GMeMdc",
        "outputId": "c5c323b0-416b-416d-e1a0-387da5380357"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Shipment ID', 'Inbound ID', 'Truck ID', 'Outbound ID', 'Dispatch ID',\n",
              "       'Date Created', 'NFE', 'Hub Status', 'Reject Reason',\n",
              "       'Inbound Date Opened', 'Inbound Date Included', 'Inbound Date Closed',\n",
              "       'Inbound Carrier Name', 'Inbound User ID', 'Inbound Dock ID',\n",
              "       'Labeling Date Printed', 'Labeling Authorization Date',\n",
              "       'Labeling Last Print User', 'Labeling Workstation ID', 'Weight',\n",
              "       'Height', 'Length', 'Width', 'Labeling Carrier Name',\n",
              "       'Labeling Service Id', 'Labeling Service Name', 'Labeling Zone',\n",
              "       'Tracking Number', 'Outbound Date Opened', 'Outbound Included Date',\n",
              "       'Outbound Date Closed', 'Outbound User IDs', 'Outbound Added By',\n",
              "       'Outbound Position', 'Dispatch Included Date', 'Dispatch Date Finished',\n",
              "       'Dispatch Truck ID', 'Dispatch User ID', 'Dispatch Dock ID', 'Priority',\n",
              "       'Process Type', 'Seller ID', 'Buyer ID', 'Divergence', 'LastMile',\n",
              "       'LastMile Reimpressão', 'InBuffer', 'InBuffer Name',\n",
              "       'InBuffer Promise EDT', 'InBuffer Promise Shipping', 'Arrived LastMile',\n",
              "       'Arrived Npym', 'Was Reauthorized', 'Cutoff', 'Start Priorization',\n",
              "       'End Priorization', 'Arrival Logistic Type', 'Arrival Tracking Number',\n",
              "       'Status Description', 'Ones'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGe46RsfdHRR",
        "outputId": "bd24fc3a-8720-49c5-eaad-c3a6bf03a3d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Shipment ID                      int64\n",
              "Inbound ID                      object\n",
              "Truck ID                        object\n",
              "Outbound ID                    float64\n",
              "Dispatch ID                    float64\n",
              "Date Created                    object\n",
              "NFE                            float64\n",
              "Hub Status                      object\n",
              "Reject Reason                   object\n",
              "Inbound Date Opened             object\n",
              "Inbound Date Included           object\n",
              "Inbound Date Closed             object\n",
              "Inbound Carrier Name            object\n",
              "Inbound User ID                 object\n",
              "Inbound Dock ID                  int64\n",
              "Labeling Date Printed           object\n",
              "Labeling Authorization Date     object\n",
              "Labeling Last Print User        object\n",
              "Labeling Workstation ID          int64\n",
              "Weight                         float64\n",
              "Height                         float64\n",
              "Length                         float64\n",
              "Width                          float64\n",
              "Labeling Carrier Name           object\n",
              "Labeling Service Id              int64\n",
              "Labeling Service Name           object\n",
              "Labeling Zone                   object\n",
              "Tracking Number                 object\n",
              "Outbound Date Opened            object\n",
              "Outbound Included Date          object\n",
              "Outbound Date Closed            object\n",
              "Outbound User IDs               object\n",
              "Outbound Added By               object\n",
              "Outbound Position               object\n",
              "Dispatch Included Date          object\n",
              "Dispatch Date Finished          object\n",
              "Dispatch Truck ID               object\n",
              "Dispatch User ID                object\n",
              "Dispatch Dock ID                 int64\n",
              "Priority                          bool\n",
              "Process Type                    object\n",
              "Seller ID                      float64\n",
              "Buyer ID                       float64\n",
              "Divergence                      object\n",
              "LastMile                        object\n",
              "LastMile Reimpressão            object\n",
              "InBuffer                        object\n",
              "InBuffer Name                   object\n",
              "InBuffer Promise EDT           float64\n",
              "InBuffer Promise Shipping      float64\n",
              "Arrived LastMile                object\n",
              "Arrived Npym                    object\n",
              "Was Reauthorized                object\n",
              "Cutoff                          object\n",
              "Start Priorization              object\n",
              "End Priorization                object\n",
              "Arrival Logistic Type           object\n",
              "Arrival Tracking Number        float64\n",
              "Status Description              object\n",
              "Ones                           float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shipments_df[\"Shipment ID\"].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGOnaGaH1Cur",
        "outputId": "7d78a89e-af3f-46f5-df7f-4a6efb39f18c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "41161988872"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_group = shipments_df.groupby(\"Labeling Carrier Name\")"
      ],
      "metadata": {
        "id": "xUhjQa3N1h8X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "carrier_counts = shipments_df.groupby('Labeling Carrier Name')['Shipment ID'].count()\n",
        "carrier_counts.plot(kind=\"bar\",)\n",
        "plt.grid()\n",
        "plt.xlabel(\"Carriers Last Mile\")\n",
        "plt.ylabel(\"Q shipments\")\n",
        "plt.title(\"Cantidad de envíos por carrier\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        },
        "id": "z5aSBe651h-5",
        "outputId": "333590bc-6ef0-4bce-eb4f-2474ac11c99b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cantidad de envíos por carrier')"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "status_counts = shipments_df.groupby('Hub Status')['Shipment ID'].count()\n",
        "status_counts.plot(kind=\"bar\",color=\"r\")\n",
        "plt.grid()\n",
        "plt.xlabel(\"Hub status\")\n",
        "plt.ylabel(\"Q shipments\")\n",
        "plt.title(\"Cantidad de envíos por hub status\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "2vg2-ZAO1iBX",
        "outputId": "41968341-6255-4190-cf00-e7ef7d0766c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Cantidad de envíos por hub status')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}