instacart_1_EDA.ipynb

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      "name": "instacart-1-EDA.ipynb",
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      "authorship_tag": "ABX9TyP2uJq71ZzvcOSiG35vTXQ8",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pedroafleite/instacart-market-basket-analysis/blob/main/instacart_1_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aNUEooVMO26e",
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        },
        "outputId": "286f480d-b3a8-4c19-dff0-cbb9f636cae6"
      },
      "source": [
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "\r\n",
        "from google.colab import drive\r\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6M06VCFlqMmz"
      },
      "source": [
        "aisles = pd.read_csv('/content/drive/MyDrive/instacart/aisles.csv')\r\n",
        "departments = pd.read_csv('/content/drive/MyDrive/instacart/departments.csv')\r\n",
        "order_products_prior = pd.read_csv('/content/drive/MyDrive/instacart/order_products__prior.csv')\r\n",
        "order_products_train = pd.read_csv('/content/drive/MyDrive/instacart/order_products__train.csv')\r\n",
        "orders = pd.read_csv('/content/drive/MyDrive/instacart/orders.csv')\r\n",
        "products = pd.read_csv('/content/drive/MyDrive/instacart/products.csv')\r\n",
        "sample_submission = pd.read_csv('/content/drive/MyDrive/instacart/sample_submission.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "xuItRuQDgrQf",
        "outputId": "c5b3a6e2-69fe-45c8-90c6-10bc412782c9"
      },
      "source": [
        "order_products_prior.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32434489, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "eJ19mc6gg90o",
        "outputId": "8eab260b-7a1c-4f53-b30e-46910c62792f"
      },
      "source": [
        "order_products_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1384617, 4)"
            ]
          },
          "metadata": {
            "tags": []
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        "id": "pqJgeTzFhAHz",
        "outputId": "170dd998-4e63-48f4-d2f4-ebb5d388cef8"
      },
      "source": [
        "len(order_products_prior) + len(order_products_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33819106"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 14
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        "outputId": "d9a9bc43-9d24-47ee-a2ae-2e792e539461"
      },
      "source": [
        "aisles.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>aisle_id</th>\n",
              "      <th>aisle</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>prepared soups salads</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>specialty cheeses</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>energy granola bars</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>instant foods</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>marinades meat preparation</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   aisle_id                       aisle\n",
              "0         1       prepared soups salads\n",
              "1         2           specialty cheeses\n",
              "2         3         energy granola bars\n",
              "3         4               instant foods\n",
              "4         5  marinades meat preparation"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
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        },
        "id": "elgKFOCGha1l",
        "outputId": "c5d8f8c0-796a-4ab1-c7a1-6131ce5d5326"
      },
      "source": [
        "departments.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>department_id</th>\n",
              "      <th>department</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>frozen</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>other</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>bakery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>produce</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>alcohol</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   department_id department\n",
              "0              1     frozen\n",
              "1              2      other\n",
              "2              3     bakery\n",
              "3              4    produce\n",
              "4              5    alcohol"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 17
        }
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    {
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        "id": "_koMa_NOheRO",
        "outputId": "0312bf48-e989-48d5-89d9-6416da6cc37e"
      },
      "source": [
        "orders.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>eval_set</th>\n",
              "      <th>order_number</th>\n",
              "      <th>order_dow</th>\n",
              "      <th>order_hour_of_day</th>\n",
              "      <th>days_since_prior_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2539329</td>\n",
              "      <td>1</td>\n",
              "      <td>prior</td>\n",
              "      <td>1</td>\n",
              "      <td>Week days</td>\n",
              "      <td>8</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2398795</td>\n",
              "      <td>1</td>\n",
              "      <td>prior</td>\n",
              "      <td>2</td>\n",
              "      <td>Week days</td>\n",
              "      <td>7</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>473747</td>\n",
              "      <td>1</td>\n",
              "      <td>prior</td>\n",
              "      <td>3</td>\n",
              "      <td>Week days</td>\n",
              "      <td>12</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2254736</td>\n",
              "      <td>1</td>\n",
              "      <td>prior</td>\n",
              "      <td>4</td>\n",
              "      <td>Week days</td>\n",
              "      <td>7</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>431534</td>\n",
              "      <td>1</td>\n",
              "      <td>prior</td>\n",
              "      <td>5</td>\n",
              "      <td>Week days</td>\n",
              "      <td>15</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   order_id  user_id  ... order_hour_of_day  days_since_prior_order\n",
              "0   2539329        1  ...                 8                     NaN\n",
              "1   2398795        1  ...                 7                    15.0\n",
              "2    473747        1  ...                12                    21.0\n",
              "3   2254736        1  ...                 7                    29.0\n",
              "4    431534        1  ...                15                    28.0\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "U1sODnw-hoYp",
        "outputId": "f47082fc-447d-448a-a505-beaa1899d379"
      },
      "source": [
        "products.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>product_id</th>\n",
              "      <th>product_name</th>\n",
              "      <th>aisle_id</th>\n",
              "      <th>department_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Chocolate Sandwich Cookies</td>\n",
              "      <td>61</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>All-Seasons Salt</td>\n",
              "      <td>104</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Robust Golden Unsweetened Oolong Tea</td>\n",
              "      <td>94</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Smart Ones Classic Favorites Mini Rigatoni Wit...</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Green Chile Anytime Sauce</td>\n",
              "      <td>5</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   product_id  ... department_id\n",
              "0           1  ...            19\n",
              "1           2  ...            13\n",
              "2           3  ...             7\n",
              "3           4  ...             1\n",
              "4           5  ...            13\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "h_j2CDDKhvTO",
        "outputId": "8ab9f842-631f-4521-8c23-c922927c2a44"
      },
      "source": [
        "order_products_prior.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>add_to_cart_order</th>\n",
              "      <th>reordered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>33120</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>28985</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>9327</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>45918</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>30035</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   order_id  product_id  add_to_cart_order  reordered\n",
              "0         2       33120                  1          1\n",
              "1         2       28985                  2          1\n",
              "2         2        9327                  3          0\n",
              "3         2       45918                  4          1\n",
              "4         2       30035                  5          0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
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        "outputId": "efd854e7-df2e-4659-f805-1a6058ead8c7"
      },
      "source": [
        "order_products_train.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>add_to_cart_order</th>\n",
              "      <th>reordered</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>49302</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>11109</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>10246</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>49683</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>43633</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   order_id  product_id  add_to_cart_order  reordered\n",
              "0         1       49302                  1          1\n",
              "1         1       11109                  2          1\n",
              "2         1       10246                  3          0\n",
              "3         1       49683                  4          0\n",
              "4         1       43633                  5          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZyXAPbM88oed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "outputId": "e4dd68d6-b7c7-43be-d6b5-e5bb4a30bbe0"
      },
      "source": [
        "#2) Data exploration\r\n",
        "orders.columns #seems to be good for exploring the time in which purchases were made\r\n",
        "orders.dtypes\r\n",
        "#Day of the week\r\n",
        "orders.groupby('order_dow')['order_id'].count() #dow meaning 'day of the week'\r\n",
        "#values go from 0 to 6, but no documentation available regarding that\r\n",
        "#I would say that it starts on a saturday, since saturday and sunday should have the largest numbers (0 and 1 in this case)\r\n",
        "#I argue that supported from experience of all else\r\n",
        "days = {0:'Saturday', 1:'Sunday', 2:'Monday', 3:'Tuesday', \r\n",
        "        4:'Wednesday', 5:'Thursday', 6:'Friday'}\r\n",
        "orders['order_dow'] = orders['order_dow'].map(days) \r\n",
        "\r\n",
        "orders.groupby('order_dow')['order_id'].count().plot(kind=\"bar\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ce7cf1ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-c0Piyi8syk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "dca2bf99-4d6d-4a05-b12a-384d4b5e567b"
      },
      "source": [
        "#Reorder days of the week as shown in plot\r\n",
        "dow = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', \r\n",
        "                    'Friday', 'Saturday', 'Sunday']\r\n",
        "mapping = {order_dow: i for i, order_dow in enumerate(dow)}\r\n",
        "key = orders['order_dow'].map(mapping)\r\n",
        "orders = orders.iloc[key.argsort()]\r\n",
        "\r\n",
        "m1 = orders.groupby('order_dow', sort=False)['order_id'].count().plot(kind=\"bar\")\r\n",
        "plt.ylabel('Quantity of orders')\r\n",
        "plt.xlabel('')\r\n",
        "plt.yticks(rotation=0)\r\n",
        "plt.xticks(rotation=20)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1oSrLsuW9zFK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "b4e35de3-7091-449a-94b6-89a2599c7b63"
      },
      "source": [
        "m2 = orders.groupby('order_hour_of_day')['order_id'].count().plot(kind=\"bar\")\r\n",
        "plt.ylabel('Quantity of orders')\r\n",
        "plt.xlabel('Hour of the day')\r\n",
        "plt.yticks(rotation=0)\r\n",
        "plt.xticks(rotation=0)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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InwEdkj5fuTwiLqwSZmZmVve0WP/0+uoqyzycipmZ1VTvtNj/TbO3RMQf8sskvbulrTIzsx6tSFfkHxUsMzMzA+pfc3kn8C5gUMU1l53JBpE0MzOrqt41l35k11v6Aq/JlT/LhqHuzczMNlLvmssdwB2SLo+IR9vYJjMz6+GKjC22vaTJQEd+/Yg4tFWNMjOznq1IcvkFcAnwE2B9a5tjZma9QZHksi4iJrW8JWZm1msU6Yr8K0lnSBosadfuqeUtMzOzHqvIkcu49PrFXFkAry+/OWZm1hs0TC4RMbwdDTEzs96jyJELkvYBRgI7dJdFxBWtapSZmfVsDZOLpHOBQ8iSywzgSOAuwMnFzMyqKnJBfyxwGPB4RJwGvA3YpaWtMjOzHq1IcvmfiHgZWCdpZ2A1MKy1zTIzs56syDWXOZIGAJcCc4Hngbtb2iozM+vRivQWOyPNXiLpd8DOEbGwtc0yM7OerMgF/YOrlUXEna1pkpmZ9XRFTovlb57cAdif7PRY3YErJU0BPgisjoh9UtmuwDVkg2AuBz4SEU9JEvBfwFHAC8CpETEvxYwDvpo2+62ImJrK9wMuB3Yk68V2VkRErToKfE4zs5bqmPibmsuWn390G1vSeg0v6EfEh3LTB4B9gCJf1pcDoyvKJgKzI2IEMDu9h6x784g0jQcmwT+T0bnAAWRJ7VxJA1PMJOCTubjRDeowM7M2KdJbrNIK4M2NVkqnzdZWFI8Bpqb5qcCxufIrInMPMEDSYOAIYFZErE1HH7OA0WnZzhFxT0QE2T03xzaow8zM2qTINZcfkY0lBlkyGgXM28T69oiIVWn+cWCPND8EeCy33opUVq98RZXyenVsRNJ4siMl9txzz2Y/i5mZ1VCoK3Jufh1wdUT8YXMrTtdHovGarasjIiYDkwE6Oztb2hYzs21J0YeFvSHNPxQRL25GfU9IGhwRq9KprdWpfCWvvDFzaCpbSTb0TL789lQ+tMr69eowM7M2qXnNRdJ2kv6T7LTUT8ku0C+TNDEtH7UJ9U1nwxD+44CbcuWnKHMg8Ew6tTUTOFzSwHQh/3BgZlr2rKQDU0+zUyq2Va0OMzNrk3pHLt8HdgI6IuI5gDT8y/ckTSLrnVVzOH5JV5MddewuaQVZr6/zgWslnQ48CnwkrT6DrBtyF1lX5NMAImKtpG8C96f1vhER3Z0EzmBDV+Tfpok6dZiZWZvUSy5HASNSbywAIuJZSZ8G/krWfbimiDixxqLDqqwbwIQa25kCTKlSPoesW3Rl+ZPV6jAzs/apl1xezieWbhGxXtKa1GXYrGW2pRvOzHqbeve5LJF0SmWhpI8BS1vXJDMz6+nqHblMAG6Q9HGy4V4AOsmucRzX6oaZmVnPVTO5RMRK4ABJhwJvScUzImJ2W1pmZmY9VpEh928Fbm1DW8zMrJfYlLHFzMzM6nJyMTOz0jm5mJlZ6ZxczMysdE4uZmZWuiKjIpuZ2RbUE0er8JGLmZmVzsnFzMxK5+RiZmalc3IxM7PSObmYmVnpnFzMzKx0Ti5mZlY6JxczMyudk4uZmZXOycXMzErn5GJmZqXz2GJmZr3UlhyTzEcuZmZWOicXMzMrnZOLmZmVzsnFzMxKt0WSi6Tlkh6UNF/SnFS2q6RZkh5OrwNTuST9UFKXpIWS9s1tZ1xa/2FJ43Ll+6Xtd6VYtf9Tmpltu7bkkcv7ImJURHSm9xOB2RExApid3gMcCYxI03hgEmTJCDgXOADYHzi3OyGldT6Zixvd+o9jZmbdtqbTYmOAqWl+KnBsrvyKyNwDDJA0GDgCmBURayPiKWAWMDot2zki7omIAK7IbcvMzNpgSyWXAG6WNFfS+FS2R0SsSvOPA3uk+SHAY7nYFamsXvmKKuUbkTRe0hxJc9asWbM5n8fMzHK21E2UB0XESkmvBWZJ+mN+YUSEpGh1IyJiMjAZoLOzs+X1mZltK7bIkUtErEyvq4Ebya6ZPJFOaZFeV6fVVwLDcuFDU1m98qFVys3MrE3anlwk9Zf0mu554HBgETAd6O7xNQ64Kc1PB05JvcYOBJ5Jp89mAodLGpgu5B8OzEzLnpV0YOoldkpuW2Zm1gZb4rTYHsCNqXdwX+DnEfE7SfcD10o6HXgU+EhafwZwFNAFvACcBhARayV9E7g/rfeNiFib5s8ALgd2BH6bJjMza5O2J5eIWAa8rUr5k8BhVcoDmFBjW1OAKVXK5wD7bHZjzcxsk2xNXZHNzKyX8JD7Zmb2CmUM1e8jFzMzK52Ti5mZlc7JxczMSufkYmZmpXNyMTOz0jm5mJlZ6dwV2VqujG6NZtaz+MjFzMxK5+RiZmalc3IxM7PSObmYmVnpfEG/Cl+ANjPbPD5yMTOz0jm5mJlZ6ZxczMysdE4uZmZWOicXMzMrnZOLmZmVzsnFzMxK5/tcSuT7Y8zMMj5yMTOz0vnIxQrzkZmZFeXkshXwl7aZ9TY+LWZmZqXzkUsPtqlHPD5SMrNW67VHLpJGS3pIUpekiVu6PWZm25JemVwk9QF+DBwJjAROlDRyy7bKzGzb0SuTC7A/0BURyyLiJWAaMGYLt8nMbJuhiNjSbSidpLHA6Ij4RHp/MnBARJxZsd54YHx6+0bgoRqb3B346yY0xXE9N64ntNFxjtsa4v41IgZtVBoRvW4CxgI/yb0/GbhoM7Y3x3HbVlxPaKPjHLc1x/XW02IrgWG590NTmZmZtUFvTS73AyMkDZfUDzgBmL6F22Rmts3olfe5RMQ6SWcCM4E+wJSIWLwZm5zsuG0urie00XGO22rjeuUFfTMz27J662kxMzPbgpxczMysdE4udWzqEDKSpkhaLWlREzHDJN0maYmkxZLOKhi3g6T7JC1IcV8vWmeK7yPpAUm/biJmuaQHJc2XNKeJuAGSrpP0R0lLJb2zQMwbUz3d07OSPlewvrPTz2SRpKsl7VAw7qwUs7heXdV+z5J2lTRL0sPpdWDBuONTfS9L6myivu+mn+dCSTdKGlAw7pspZr6kmyW9rkhcbtkXJIWk3QvWd56klbnf41FF65P0mfQZF0v6TsH6rsnVtVzS/IJxoyTd071vS9q/YNzbJN2d/i5+JWnnipiqf9+N9pc6cXX3lzpxdfeXOnEN95eNbEqf521hIusI8Gfg9UA/YAEwsmDswcC+wKIm6hsM7JvmXwP8qUh9gIBXp/ntgHuBA5uo9/PAz4FfNxGzHNh9E36mU4FPpPl+wIBN+J08TnbTVqN1hwCPADum99cCpxaI2wdYBOxE1uHlFuANRX/PwHeAiWl+InBBwbg3k93IezvQ2UR9hwN90/wFTdS3c27+s8AlRfdjsm7+M4FHq+0HNeo7D/jfDX721eLel34H26f3ry3aztzy7wNfK1jfzcCRaf4o4PaCcfcD703zHwe+WRFT9e+70f5SJ67u/lInru7+Uieu4f5SOfnIpbZNHkImIu4E1jZTWUSsioh5af45YCnZF2SjuIiI59Pb7dJUqJeGpKHA0cBPmmnrppC0C9kf5WUAEfFSRDzd5GYOA/4cEY8WXL8vsKOkvmTJ4v8ViHkzcG9EvBAR64A7gA9XW7HG73kMWRIlvR5bJC4ilkZErREi6sXdnNoJcA/ZPV1F4p7Nve1PlX2mzn78A+D/VItpEFdXjbhPA+dHxItpndXN1CdJwEeAqwvGBdB91LELVfaZGnF7A3em+VnAv1fE1Pr7rru/1IprtL/Uiau7v9SJa7i/VHJyqW0I8Fju/QoKfNmXQVIH8Hayo5Ai6/dJh/2rgVkRUSgO+E+yL4mXm2xiADdLmqtsCJ0ihgNrgJ8qOw33E0n9m6z3BKp8SVRtYMRK4HvAX4BVwDMRcXOB0EXAeyTtJmknsv9ehzWIydsjIlal+ceBPZqI3VwfB35bdGVJ35b0GHAS8LWCMWOAlRGxYBPad2Y6tTKl2unCGvYm+33cK+kOSe9oss73AE9ExMMF1/8c8N30c/kecE7BuMVs+OfzeOrsMxV/34X3l2a/FwrE1d1fKuOa3V+cXLYykl4NXA98ruK/hZoiYn1EjCL7L2R/SfsUqOeDwOqImLsJzTwoIvYlG3V6gqSDC8T0JTuVMCki3g78jew0QCHKboY9BvhFwfUHkv2xDwdeB/SX9LFGcRGxlOx0wc3A74D5wPqi7azYVlDwKHJzSfoKsA64qmhMRHwlIoalmDMbrZ+S7ZcpmIgqTAL2AkaRJfvvF4zrC+wKHAh8Ebg2HY0UdSIF/yFJPg2cnX4uZ5OOtAv4OHCGpLlkp5NeqrZSvb/vevvLpnwv1ItrtL9Ui2t2f3Fyqa3tQ8hI2o7sF3pVRNzQbHw6zXQbMLrA6u8GjpG0nOyU36GSflawnpXpdTVwI9kpxEZWACtyR1XXkSWboo4E5kXEEwXXfz/wSESsiYh/ADcA7yoSGBGXRcR+EXEw8BTZeeeinpA0GCC9bnQap2ySTgU+CJyUvqCadRUVp3Fq2IssWS9I+81QYJ6kf2kUGBFPpH+CXgYupdg+A9l+c0M6/Xsf2VH2Rp0IqkmnQz8MXFOwLoBxZPsKZP/IFGpnRPwxIg6PiP3Iktmfq7Sn2t93w/1lU78XasU12l8K1Fdof3Fyqa2tQ8ik/8YuA5ZGxIVNxA3q7vEhaUfgA8AfG8VFxDkRMTQiOsg+260R0fA/e0n9Jb2me57sAmHDXnER8TjwmKQ3pqLDgCWN4nKa/Q/0L8CBknZKP9vDyM4fNyTptel1T7Ivp583Ue90si8o0utNTcQ2TdJoslObx0TEC03Ejci9HUOxfebBiHhtRHSk/WYF2cXfxwvUNzj39jgK7DPJL8ku6iNpb7KOIEVH9X0/8MeIWFFwfciusbw3zR8KFDqdlttnXgV8FbikYnmtv++6+8tmfC9UjWu0v9SJa3p/qXu1f1ufyM63/4nsv5CvNBF3Ndmh/z/I/gBPLxBzENkh8UKyUzHzgaMKxL0VeCDFLaJKr5gC2ziEgr3FyHrPLUjT4iZ/LqOAOamtvwQGFozrDzwJ7NLk5/p6+iNYBFxJ6nFUIO73ZIlvAXBYM79nYDdgNtmX0i3ArgXjjkvzLwJPADMLxnWRXRvs3meq9fqqFnd9+rksBH5FdtG2qf2YGr0Ga9R3JfBgqm86MLhgXD/gZ6mt84BDi7YTuBz4VJO/v4OAuel3fy+wX8G4s8i+K/4EnE8a/aTR33ej/aVOXN39pU5c3f2lTlzD/aVy8vAvZmZWOp8WMzOz0jm5mJlZ6ZxczMysdE4uZmZWOicXMzMrnZOLWSLp+Yr3p0q6qM1tOF7ZiNG3VZR3SPpoK9qWtl14BG+zIpxczFos3Sle1OnAJyPifRXlHcBHN17dbOvk5GJWQPrv/tY08OLsdPc+ki6XNDa33vPp9RBJv5c0nSojEUg6UdmzPxZJuiCVfY3sJrbLJH23IuR8sgEc50s6O5W9TtLvlD0L5Du5bR+u7Nki8yT9Io0TVVn/fsqeAbQAmFDxOX+fYudJelcqv0LSsbn1rlI2iKVZdc3c8ezJU2+eyAaonJ+b/gJclJb9ChiX5j8O/DLNXw6MzW3j+fR6CNngnMOr1PO6tO1BZAMz3gocm5bdTvXncxxCbhQF4FRgGdmw8DuQPVtlGNm4W3cC/dN6X6L6s0wWAgen+e+Snk1C9miCHdL8CGBOmn9v7jPvQvasnL5b+nfmaeudmjlcN+vt/iey0aWBfw7w1/2Uv3ey4bkuV5I95KmR+yLikSrl7yB7CNWaVM9VZM+6+WWT7Z0dEc+kbSwB/hUYQPZwpz+kwYP7AXfng9JYdAMiey5J9+c5Ms1vB1wkaRRZst0bICLukHSxpEFkgxZeHxueC2K2EScXs82zjnR6OQ1a2C+37G8trvvF3Px6sr9nkT3T58RN3ObZZGNVvY3sc/09t+wK4GNkA52etonbt22Er7mYFfPfZF+qkD0s6fdpfjmwX5o/huw//0buA94raXdJfchGfL6jQcxzZM8JaeQe4N2S3gD/HMV67/wKkT2a4WlJB6Wik3KLdwFWRTY0/slkj5budjnZw7SIiGZGtLZtkJOLWTGfAU6TtJDsS/esVH4pWaJYQHbqrOHRSmRPHpxI9uydBcDciGg0NP9CYH26CH92rbzgDTYAAAB7SURBVJXSqbZTgatTW+8G3lRl1dOAHyt7gmn+4VsXA+PS53lT/vNE9iydpcBPG7TVzKMim1kxyp5E+SDZM1ye2dLtsa2bj1zMrCFJ7yc7avmRE4sV4SMXMzMrnY9czMysdE4uZmZWOicXMzMrnZOLmZmVzsnFzMxK9/8BwOXBCVM5BckAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysH1R3k79hZo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "outputId": "41606f2c-499b-441c-855b-5d328fec3021"
      },
      "source": [
        "#Days since prior order\r\n",
        "m3 = orders.groupby('days_since_prior_order')['order_id'].count().plot(kind=\"bar\")\r\n",
        "plt.ylabel('Quantity of orders')\r\n",
        "plt.xlabel('Days since prior order')\r\n",
        "plt.yticks(rotation=0)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jegnu-xD94SB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "b78703f4-a7be-48a3-cc4f-92a5b8a46578"
      },
      "source": [
        "#Compare hour of the day by each day of the week to check for overlaps\r\n",
        "orders = pd.read_csv('/content/drive/MyDrive/instacart/orders.csv') #reload csv\r\n",
        "days = {0:'Saturday', 1:'Sunday', 2:'Monday', 3:'Tuesday', \r\n",
        "        4:'Wednesday', 5:'Thursday', 6:'Friday'}\r\n",
        "orders['order_dow'] = orders['order_dow'].map(days) \r\n",
        "compare_days = {'Saturday':'Weekends', 'Sunday':'Weekends', \r\n",
        "                'Monday': 'Week days', 'Tuesday':'Week days', \r\n",
        "                'Wednesday':'Week days', 'Thursday':'Week days', \r\n",
        "                'Friday':'Week days'}\r\n",
        "orders['order_dow'] = (orders['order_dow'].map(compare_days))\r\n",
        "\r\n",
        "m4 = orders['order_hour_of_day'].hist(by=orders['order_dow']) #roughly the same distribution"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MucKnj2V-HNp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "outputId": "862b9913-b2d0-4a46-f651-f7960d06beea"
      },
      "source": [
        "#Create merge of orders and products\r\n",
        "order1 = order_products_train.merge(products, how='inner')\r\n",
        "order1 = order1.sort_values(by=['order_id','add_to_cart_order'])\r\n",
        "\r\n",
        "order1['product_name'].value_counts().head(20).plot(\r\n",
        "    kind='barh', figsize=(10,5), fontsize=12)\r\n",
        "plt.title('Most sold products', fontsize = 20)\r\n",
        "plt.ylabel('Product')\r\n",
        "plt.xlabel('Number')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}