4_Customer_segmentation.ipynb

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  "metadata": {
    "colab": {
      "name": "B1.5.4 Customer segmentation - teacher's sheet.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dxymBWFoRZq8"
      },
      "source": [
        "# 4) Customer categories"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVB026XO9-iw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d6afb22-5077-4422-cdc1-b422c96db81b"
      },
      "source": [
        "from google.colab import drive\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": "4JUxXHt4RuzX"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "relative_path = '/content/drive/My Drive/Course v2/B1. Machine Learning Deep Learning/B1.5 Customer Segmentation/Data/'\n",
        "df_cleaned = pd.read_csv(relative_path + 'data_cleaned.csv', encoding='latin', dtype={'CustomerID': str}) \n",
        "X = pd.read_csv(relative_path + 'Matrix_X.csv') \n",
        "\n",
        "unique_products = df_cleaned['Description'].unique()\n",
        "clusters_tmp = pd.read_csv(relative_path + 'Matrix_X_clusters.csv') \n",
        "clusters = clusters_tmp.to_numpy()[:,1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kGA5ejmoUtdk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "17ad99d7-d56c-49a9-c294-f5c646228919"
      },
      "source": [
        "df_cleaned"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Unnamed: 0.1</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>QuantityCanceled</th>\n",
              "      <th>TotalPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "      <td>15.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "      <td>22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "      <td>20.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392852</th>\n",
              "      <td>401599</td>\n",
              "      <td>541904</td>\n",
              "      <td>581587</td>\n",
              "      <td>22613</td>\n",
              "      <td>PACK OF 20 SPACEBOY NAPKINS</td>\n",
              "      <td>12</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>0.85</td>\n",
              "      <td>12680</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>10.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392853</th>\n",
              "      <td>401600</td>\n",
              "      <td>541905</td>\n",
              "      <td>581587</td>\n",
              "      <td>22899</td>\n",
              "      <td>CHILDREN'S APRON DOLLY GIRL</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>12680</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>12.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392854</th>\n",
              "      <td>401601</td>\n",
              "      <td>541906</td>\n",
              "      <td>581587</td>\n",
              "      <td>23254</td>\n",
              "      <td>CHILDRENS CUTLERY DOLLY GIRL</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>16.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392855</th>\n",
              "      <td>401602</td>\n",
              "      <td>541907</td>\n",
              "      <td>581587</td>\n",
              "      <td>23255</td>\n",
              "      <td>CHILDRENS CUTLERY CIRCUS PARADE</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.15</td>\n",
              "      <td>12680</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>16.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>392856</th>\n",
              "      <td>401603</td>\n",
              "      <td>541908</td>\n",
              "      <td>581587</td>\n",
              "      <td>22138</td>\n",
              "      <td>BAKING SET 9 PIECE RETROSPOT</td>\n",
              "      <td>3</td>\n",
              "      <td>2011-12-09 12:50:00</td>\n",
              "      <td>4.95</td>\n",
              "      <td>12680</td>\n",
              "      <td>France</td>\n",
              "      <td>0</td>\n",
              "      <td>14.85</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>392857 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0  Unnamed: 0.1  ... QuantityCanceled TotalPrice\n",
              "0                0             0  ...                0      15.30\n",
              "1                1             1  ...                0      20.34\n",
              "2                2             2  ...                0      22.00\n",
              "3                3             3  ...                0      20.34\n",
              "4                4             4  ...                0      20.34\n",
              "...            ...           ...  ...              ...        ...\n",
              "392852      401599        541904  ...                0      10.20\n",
              "392853      401600        541905  ...                0      12.60\n",
              "392854      401601        541906  ...                0      16.60\n",
              "392855      401602        541907  ...                0      16.60\n",
              "392856      401603        541908  ...                0      14.85\n",
              "\n",
              "[392857 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmJ4txXGRb_t"
      },
      "source": [
        "## 4.1) New feature\n",
        "\n",
        "Add new feature: `categ_product` into `df_cleaned` dataframe. Use information, obtained in previous part: `clusters` and `unique_procudts`.\n",
        "\n",
        "Use `map` and `zip` if you want."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ykVLnyHRfD3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ecedd3d-d0f2-4953-d5b8-ffce6971b0e4"
      },
      "source": [
        "print(unique_products.shape)\n",
        "print(clusters.shape)\n",
        "corresp = dict()\n",
        "for key, val in zip(unique_products, clusters):\n",
        "    corresp[key] = val \n",
        "\n",
        "df_cleaned['categ_product'] = df_cleaned.loc[:, 'Description'].map(corresp)\n",
        "#df_cleaned.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(3878,)\n",
            "(3878,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "99_ikHkZWmwW"
      },
      "source": [
        "## 4.2) Money spend for categories\n",
        "Add 5 new features with names `categ_0` to `categ_4` to `df_cleaned` dataset, which indicate the money spent on each product category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaaKDMQ2RodR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "a5628251-1a28-4376-a87a-8c62c092a058"
      },
      "source": [
        "# add 5 variables - the money spent on each of the 5 categories\n",
        "for i in range(5):\n",
        "    col = 'categ_{}'.format(i)        \n",
        "    df_temp = df_cleaned[df_cleaned['categ_product'] == i]\n",
        "    price_temp = df_temp['TotalPrice']\n",
        "    price_temp = price_temp.apply(lambda x:x if x > 0 else 0)\n",
        "    df_cleaned.loc[:, col] = price_temp\n",
        "    df_cleaned[col].fillna(0, inplace = True)\n",
        "\n",
        "df_cleaned[['CustomerID', 'InvoiceNo', 'Description', 'categ_product', 'categ_0', 'categ_1', 'categ_2', 'categ_3','categ_4']][:5]\n",
        "#df_cleaned"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>Description</th>\n",
              "      <th>categ_product</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17850</td>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17850</td>\n",
              "      <td>536365</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.34</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17850</td>\n",
              "      <td>536365</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>22.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17850</td>\n",
              "      <td>536365</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.34</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17850</td>\n",
              "      <td>536365</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.34</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID InvoiceNo  ... categ_3  categ_4\n",
              "0      17850    536365  ...     0.0     15.3\n",
              "1      17850    536365  ...     0.0      0.0\n",
              "2      17850    536365  ...     0.0      0.0\n",
              "3      17850    536365  ...     0.0      0.0\n",
              "4      17850    536365  ...     0.0      0.0\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6WXIrucVW1cW"
      },
      "source": [
        "## 4.3) Basket price with categories\n",
        "\n",
        "We then create a dataframe where each row is a purchase (unique `InvoiceNo`) with total amount of money, and the amount spent on each product category.\n",
        "Read and understand the following code.\n",
        "\n",
        "The features we have here are the following:\n",
        "`CustomerID`, `InvoiceNo`, `Basket Price`, `categ_0` to `categ_4` and `InvoiceDate`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RketHP5xngsE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "cd40e7ec-4f76-42b8-a883-c9f2170304e6"
      },
      "source": [
        "# sum the commands for each order\n",
        "temp = df_cleaned.groupby(by=['CustomerID', 'InvoiceNo'], as_index=False)['TotalPrice'].sum()\n",
        "basket_price = temp.rename(columns = {'TotalPrice':'Basket Price'})\n",
        "basket_price.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>Basket Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>541431</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>537626</td>\n",
              "      <td>711.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12347</td>\n",
              "      <td>542237</td>\n",
              "      <td>475.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12347</td>\n",
              "      <td>549222</td>\n",
              "      <td>636.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12347</td>\n",
              "      <td>556201</td>\n",
              "      <td>382.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID InvoiceNo  Basket Price\n",
              "0      12346    541431          0.00\n",
              "1      12347    537626        711.79\n",
              "2      12347    542237        475.39\n",
              "3      12347    549222        636.25\n",
              "4      12347    556201        382.52"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zOCa_ImDoEr_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "f2fcdd49-e9e7-4f5b-a93d-72fa993027c8"
      },
      "source": [
        "# percentage of the price of the order / product category\n",
        "for i in range(5):\n",
        "    col = 'categ_{}'.format(i) \n",
        "    temp = df_cleaned.groupby(by=['CustomerID', 'InvoiceNo'], as_index=False)[col].sum()\n",
        "    basket_price.loc[:, col] = temp\n",
        "\n",
        "basket_price.head() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>Basket Price</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346</td>\n",
              "      <td>541431</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>537626</td>\n",
              "      <td>711.79</td>\n",
              "      <td>187.2</td>\n",
              "      <td>124.44</td>\n",
              "      <td>293.35</td>\n",
              "      <td>23.40</td>\n",
              "      <td>83.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12347</td>\n",
              "      <td>542237</td>\n",
              "      <td>475.39</td>\n",
              "      <td>130.5</td>\n",
              "      <td>38.25</td>\n",
              "      <td>169.20</td>\n",
              "      <td>84.34</td>\n",
              "      <td>53.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12347</td>\n",
              "      <td>549222</td>\n",
              "      <td>636.25</td>\n",
              "      <td>330.9</td>\n",
              "      <td>38.25</td>\n",
              "      <td>115.00</td>\n",
              "      <td>81.00</td>\n",
              "      <td>71.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12347</td>\n",
              "      <td>556201</td>\n",
              "      <td>382.52</td>\n",
              "      <td>74.4</td>\n",
              "      <td>19.90</td>\n",
              "      <td>168.76</td>\n",
              "      <td>41.40</td>\n",
              "      <td>78.06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID InvoiceNo  Basket Price  ...  categ_2  categ_3  categ_4\n",
              "0      12346    541431          0.00  ...     0.00     0.00     0.00\n",
              "1      12347    537626        711.79  ...   293.35    23.40    83.40\n",
              "2      12347    542237        475.39  ...   169.20    84.34    53.10\n",
              "3      12347    549222        636.25  ...   115.00    81.00    71.10\n",
              "4      12347    556201        382.52  ...   168.76    41.40    78.06\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QHlh01N1odaw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fbe49f0-a14c-4e5d-8244-7bedde74a352"
      },
      "source": [
        "basket_price.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(18632, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9nwaH8zXoPNe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mFGaVMUsoXhM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "f588ec92-db2c-4992-8116-28e5a7c1dcaf"
      },
      "source": [
        "# command date\n",
        "df_cleaned['InvoiceDate'] = pd.to_datetime(df_cleaned['InvoiceDate'])\n",
        "df_cleaned['InvoiceDate_int'] = df_cleaned['InvoiceDate'].astype('int64')\n",
        "temp = df_cleaned.groupby(by=['CustomerID', 'InvoiceNo'], as_index=False)['InvoiceDate_int'].mean()\n",
        "df_cleaned.drop('InvoiceDate_int', axis = 1, inplace = True)\n",
        "basket_price.loc[:, 'InvoiceDate'] = pd.to_datetime(temp['InvoiceDate_int'])\n",
        "basket_price[20:49] "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>Basket Price</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "      <th>InvoiceDate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>12352</td>\n",
              "      <td>568699</td>\n",
              "      <td>266.25</td>\n",
              "      <td>19.80</td>\n",
              "      <td>117.30</td>\n",
              "      <td>129.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2011-09-28 14:58:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>12352</td>\n",
              "      <td>574275</td>\n",
              "      <td>311.73</td>\n",
              "      <td>107.30</td>\n",
              "      <td>133.90</td>\n",
              "      <td>22.50</td>\n",
              "      <td>20.80</td>\n",
              "      <td>27.23</td>\n",
              "      <td>2011-11-03 14:36:59.999999744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>12353</td>\n",
              "      <td>553900</td>\n",
              "      <td>89.00</td>\n",
              "      <td>11.60</td>\n",
              "      <td>59.70</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>17.70</td>\n",
              "      <td>2011-05-19 17:47:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>12354</td>\n",
              "      <td>550911</td>\n",
              "      <td>1079.40</td>\n",
              "      <td>336.52</td>\n",
              "      <td>252.00</td>\n",
              "      <td>212.65</td>\n",
              "      <td>161.38</td>\n",
              "      <td>116.85</td>\n",
              "      <td>2011-04-21 13:10:59.999998720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>12355</td>\n",
              "      <td>552449</td>\n",
              "      <td>459.40</td>\n",
              "      <td>244.80</td>\n",
              "      <td>45.40</td>\n",
              "      <td>116.10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>53.10</td>\n",
              "      <td>2011-05-09 13:49:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>12356</td>\n",
              "      <td>541430</td>\n",
              "      <td>2271.62</td>\n",
              "      <td>670.84</td>\n",
              "      <td>913.68</td>\n",
              "      <td>204.00</td>\n",
              "      <td>223.60</td>\n",
              "      <td>259.50</td>\n",
              "      <td>2011-01-18 09:49:59.999998976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>12356</td>\n",
              "      <td>549435</td>\n",
              "      <td>481.46</td>\n",
              "      <td>118.11</td>\n",
              "      <td>94.80</td>\n",
              "      <td>0.00</td>\n",
              "      <td>174.15</td>\n",
              "      <td>94.40</td>\n",
              "      <td>2011-04-08 12:33:00.000000512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>12356</td>\n",
              "      <td>576895</td>\n",
              "      <td>58.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>58.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2011-11-17 08:40:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>12357</td>\n",
              "      <td>574740</td>\n",
              "      <td>6207.67</td>\n",
              "      <td>1146.90</td>\n",
              "      <td>2073.35</td>\n",
              "      <td>1759.88</td>\n",
              "      <td>315.96</td>\n",
              "      <td>911.58</td>\n",
              "      <td>2011-11-06 16:07:00.000002816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>12358</td>\n",
              "      <td>559707</td>\n",
              "      <td>484.86</td>\n",
              "      <td>50.76</td>\n",
              "      <td>401.40</td>\n",
              "      <td>15.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>17.70</td>\n",
              "      <td>2011-07-12 10:04:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>12358</td>\n",
              "      <td>581232</td>\n",
              "      <td>683.20</td>\n",
              "      <td>23.40</td>\n",
              "      <td>659.80</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2011-12-08 10:26:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>12359</td>\n",
              "      <td>540946</td>\n",
              "      <td>547.50</td>\n",
              "      <td>16.80</td>\n",
              "      <td>393.10</td>\n",
              "      <td>76.50</td>\n",
              "      <td>8.40</td>\n",
              "      <td>52.70</td>\n",
              "      <td>2011-01-12 12:43:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>12359</td>\n",
              "      <td>543370</td>\n",
              "      <td>1803.11</td>\n",
              "      <td>146.70</td>\n",
              "      <td>1148.30</td>\n",
              "      <td>271.85</td>\n",
              "      <td>97.56</td>\n",
              "      <td>138.70</td>\n",
              "      <td>2011-02-07 14:50:59.999998464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>12359</td>\n",
              "      <td>555477</td>\n",
              "      <td>1109.32</td>\n",
              "      <td>171.20</td>\n",
              "      <td>525.85</td>\n",
              "      <td>168.45</td>\n",
              "      <td>40.32</td>\n",
              "      <td>203.50</td>\n",
              "      <td>2011-06-03 12:26:00.000001536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>12359</td>\n",
              "      <td>571034</td>\n",
              "      <td>2680.55</td>\n",
              "      <td>316.84</td>\n",
              "      <td>1388.95</td>\n",
              "      <td>647.50</td>\n",
              "      <td>10.08</td>\n",
              "      <td>317.18</td>\n",
              "      <td>2011-10-13 12:47:00.000002048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>12360</td>\n",
              "      <td>554132</td>\n",
              "      <td>1083.58</td>\n",
              "      <td>262.46</td>\n",
              "      <td>429.10</td>\n",
              "      <td>102.80</td>\n",
              "      <td>116.96</td>\n",
              "      <td>172.26</td>\n",
              "      <td>2011-05-23 09:42:59.999998720</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>12360</td>\n",
              "      <td>563749</td>\n",
              "      <td>534.70</td>\n",
              "      <td>67.04</td>\n",
              "      <td>147.80</td>\n",
              "      <td>49.80</td>\n",
              "      <td>47.36</td>\n",
              "      <td>222.70</td>\n",
              "      <td>2011-08-19 10:10:00.000000256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>12360</td>\n",
              "      <td>571705</td>\n",
              "      <td>1043.78</td>\n",
              "      <td>190.32</td>\n",
              "      <td>233.45</td>\n",
              "      <td>181.95</td>\n",
              "      <td>141.24</td>\n",
              "      <td>296.82</td>\n",
              "      <td>2011-10-18 15:22:00.000002048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>12361</td>\n",
              "      <td>545017</td>\n",
              "      <td>189.90</td>\n",
              "      <td>139.50</td>\n",
              "      <td>15.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>35.40</td>\n",
              "      <td>2011-02-25 13:51:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>12362</td>\n",
              "      <td>544203</td>\n",
              "      <td>474.20</td>\n",
              "      <td>232.35</td>\n",
              "      <td>45.00</td>\n",
              "      <td>49.80</td>\n",
              "      <td>10.20</td>\n",
              "      <td>136.85</td>\n",
              "      <td>2011-02-17 10:30:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>12362</td>\n",
              "      <td>551346</td>\n",
              "      <td>495.24</td>\n",
              "      <td>219.50</td>\n",
              "      <td>60.90</td>\n",
              "      <td>115.00</td>\n",
              "      <td>38.04</td>\n",
              "      <td>61.80</td>\n",
              "      <td>2011-04-28 09:12:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>12362</td>\n",
              "      <td>559295</td>\n",
              "      <td>303.76</td>\n",
              "      <td>94.20</td>\n",
              "      <td>86.65</td>\n",
              "      <td>49.65</td>\n",
              "      <td>20.16</td>\n",
              "      <td>53.10</td>\n",
              "      <td>2011-07-07 12:32:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>12362</td>\n",
              "      <td>563037</td>\n",
              "      <td>451.35</td>\n",
              "      <td>278.45</td>\n",
              "      <td>45.00</td>\n",
              "      <td>8.50</td>\n",
              "      <td>51.00</td>\n",
              "      <td>68.40</td>\n",
              "      <td>2011-08-11 15:01:59.999999744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>12362</td>\n",
              "      <td>568651</td>\n",
              "      <td>829.99</td>\n",
              "      <td>86.10</td>\n",
              "      <td>252.95</td>\n",
              "      <td>264.65</td>\n",
              "      <td>28.44</td>\n",
              "      <td>197.85</td>\n",
              "      <td>2011-09-28 12:04:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>12362</td>\n",
              "      <td>570667</td>\n",
              "      <td>355.80</td>\n",
              "      <td>209.10</td>\n",
              "      <td>30.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>116.70</td>\n",
              "      <td>2011-10-11 14:32:59.999999744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>12362</td>\n",
              "      <td>572887</td>\n",
              "      <td>786.95</td>\n",
              "      <td>101.40</td>\n",
              "      <td>359.25</td>\n",
              "      <td>235.50</td>\n",
              "      <td>20.40</td>\n",
              "      <td>70.40</td>\n",
              "      <td>2011-10-26 13:46:59.999998976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>12362</td>\n",
              "      <td>573173</td>\n",
              "      <td>450.31</td>\n",
              "      <td>19.80</td>\n",
              "      <td>172.85</td>\n",
              "      <td>133.94</td>\n",
              "      <td>26.52</td>\n",
              "      <td>97.20</td>\n",
              "      <td>2011-10-28 10:10:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>12362</td>\n",
              "      <td>574329</td>\n",
              "      <td>477.94</td>\n",
              "      <td>81.90</td>\n",
              "      <td>60.00</td>\n",
              "      <td>98.50</td>\n",
              "      <td>51.00</td>\n",
              "      <td>186.54</td>\n",
              "      <td>2011-11-04 09:07:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>12362</td>\n",
              "      <td>580979</td>\n",
              "      <td>529.04</td>\n",
              "      <td>84.10</td>\n",
              "      <td>85.80</td>\n",
              "      <td>185.80</td>\n",
              "      <td>33.94</td>\n",
              "      <td>139.40</td>\n",
              "      <td>2011-12-06 15:40:00.000000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CustomerID InvoiceNo  ...  categ_4                   InvoiceDate\n",
              "20      12352    568699  ...     0.00 2011-09-28 14:58:00.000000000\n",
              "21      12352    574275  ...    27.23 2011-11-03 14:36:59.999999744\n",
              "22      12353    553900  ...    17.70 2011-05-19 17:47:00.000000000\n",
              "23      12354    550911  ...   116.85 2011-04-21 13:10:59.999998720\n",
              "24      12355    552449  ...    53.10 2011-05-09 13:49:00.000000000\n",
              "25      12356    541430  ...   259.50 2011-01-18 09:49:59.999998976\n",
              "26      12356    549435  ...    94.40 2011-04-08 12:33:00.000000512\n",
              "27      12356    576895  ...     0.00 2011-11-17 08:40:00.000000000\n",
              "28      12357    574740  ...   911.58 2011-11-06 16:07:00.000002816\n",
              "29      12358    559707  ...    17.70 2011-07-12 10:04:00.000000000\n",
              "30      12358    581232  ...     0.00 2011-12-08 10:26:00.000000000\n",
              "31      12359    540946  ...    52.70 2011-01-12 12:43:00.000000000\n",
              "32      12359    543370  ...   138.70 2011-02-07 14:50:59.999998464\n",
              "33      12359    555477  ...   203.50 2011-06-03 12:26:00.000001536\n",
              "34      12359    571034  ...   317.18 2011-10-13 12:47:00.000002048\n",
              "35      12360    554132  ...   172.26 2011-05-23 09:42:59.999998720\n",
              "36      12360    563749  ...   222.70 2011-08-19 10:10:00.000000256\n",
              "37      12360    571705  ...   296.82 2011-10-18 15:22:00.000002048\n",
              "38      12361    545017  ...    35.40 2011-02-25 13:51:00.000000000\n",
              "39      12362    544203  ...   136.85 2011-02-17 10:30:00.000000000\n",
              "40      12362    551346  ...    61.80 2011-04-28 09:12:00.000000000\n",
              "41      12362    559295  ...    53.10 2011-07-07 12:32:00.000000000\n",
              "42      12362    563037  ...    68.40 2011-08-11 15:01:59.999999744\n",
              "43      12362    568651  ...   197.85 2011-09-28 12:04:00.000000000\n",
              "44      12362    570667  ...   116.70 2011-10-11 14:32:59.999999744\n",
              "45      12362    572887  ...    70.40 2011-10-26 13:46:59.999998976\n",
              "46      12362    573173  ...    97.20 2011-10-28 10:10:00.000000000\n",
              "47      12362    574329  ...   186.54 2011-11-04 09:07:00.000000000\n",
              "48      12362    580979  ...   139.40 2011-12-06 15:40:00.000000000\n",
              "\n",
              "[29 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T2jrfqeWpv7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "e21903a9-b3b9-4dd7-b7a3-f37ce9dbb296"
      },
      "source": [
        "# keep only positive purchases\n",
        "basket_price = basket_price[basket_price['Basket Price'] > 0]\n",
        "basket_price.sort_values('CustomerID', ascending = True)[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>Basket Price</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "      <th>InvoiceDate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12347</td>\n",
              "      <td>537626</td>\n",
              "      <td>711.79</td>\n",
              "      <td>187.2</td>\n",
              "      <td>124.44</td>\n",
              "      <td>293.35</td>\n",
              "      <td>23.40</td>\n",
              "      <td>83.40</td>\n",
              "      <td>2010-12-07 14:57:00.000001024</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12347</td>\n",
              "      <td>542237</td>\n",
              "      <td>475.39</td>\n",
              "      <td>130.5</td>\n",
              "      <td>38.25</td>\n",
              "      <td>169.20</td>\n",
              "      <td>84.34</td>\n",
              "      <td>53.10</td>\n",
              "      <td>2011-01-26 14:29:59.999999744</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12347</td>\n",
              "      <td>549222</td>\n",
              "      <td>636.25</td>\n",
              "      <td>330.9</td>\n",
              "      <td>38.25</td>\n",
              "      <td>115.00</td>\n",
              "      <td>81.00</td>\n",
              "      <td>71.10</td>\n",
              "      <td>2011-04-07 10:42:59.999999232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12347</td>\n",
              "      <td>556201</td>\n",
              "      <td>382.52</td>\n",
              "      <td>74.4</td>\n",
              "      <td>19.90</td>\n",
              "      <td>168.76</td>\n",
              "      <td>41.40</td>\n",
              "      <td>78.06</td>\n",
              "      <td>2011-06-09 13:01:00.000000256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12347</td>\n",
              "      <td>562032</td>\n",
              "      <td>584.91</td>\n",
              "      <td>109.7</td>\n",
              "      <td>136.05</td>\n",
              "      <td>158.16</td>\n",
              "      <td>61.30</td>\n",
              "      <td>119.70</td>\n",
              "      <td>2011-08-02 08:48:00.000000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID InvoiceNo  ...  categ_4                   InvoiceDate\n",
              "1      12347    537626  ...    83.40 2010-12-07 14:57:00.000001024\n",
              "2      12347    542237  ...    53.10 2011-01-26 14:29:59.999999744\n",
              "3      12347    549222  ...    71.10 2011-04-07 10:42:59.999999232\n",
              "4      12347    556201  ...    78.06 2011-06-09 13:01:00.000000256\n",
              "5      12347    562032  ...   119.70 2011-08-02 08:48:00.000000000\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KnJmv9fVo_WL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9f5a4401-1396-4c0f-f014-7a83b814ae48"
      },
      "source": [
        "len(basket_price)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18398"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tljfkLIUZzg7"
      },
      "source": [
        "## 4.4) Customer data\n",
        "\n",
        "We create a dataframe where each row correspond to a customer. The columns are CustomerID, number of transactions, min, max, mean, sum of spent money, and percentage of money for each category.\n",
        "Call this new dataframe `transactions_per_user`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I6jMhwf9Zhln",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "e51f8d40-b74e-4c81-9c51-9fa82177be03"
      },
      "source": [
        "transactions_per_user = basket_price.groupby(by=['CustomerID'])['Basket Price'].agg(['count','min','max','mean','sum'])\n",
        "for i in range(5):\n",
        "    col = 'categ_{}'.format(i)\n",
        "    transactions_per_user.loc[:,col] = basket_price.groupby(by=['CustomerID'])[col].sum() /\\\n",
        "                                            transactions_per_user['sum']*100\n",
        "\n",
        "transactions_per_user.reset_index(drop = False, inplace = True)\n",
        "basket_price.groupby(by=['CustomerID'])['categ_0'].sum()\n",
        "transactions_per_user.sort_values('CustomerID', ascending = True)[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>CustomerID</th>\n",
              "      <th>count</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347</td>\n",
              "      <td>7</td>\n",
              "      <td>224.82</td>\n",
              "      <td>1294.32</td>\n",
              "      <td>615.714286</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>26.375870</td>\n",
              "      <td>12.041531</td>\n",
              "      <td>29.540371</td>\n",
              "      <td>11.237123</td>\n",
              "      <td>20.805104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348</td>\n",
              "      <td>4</td>\n",
              "      <td>227.44</td>\n",
              "      <td>892.80</td>\n",
              "      <td>449.310000</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>41.953217</td>\n",
              "      <td>20.030714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.016069</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349</td>\n",
              "      <td>1</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>1757.550000</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>26.506216</td>\n",
              "      <td>46.021450</td>\n",
              "      <td>10.713778</td>\n",
              "      <td>4.513101</td>\n",
              "      <td>12.245455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350</td>\n",
              "      <td>1</td>\n",
              "      <td>334.40</td>\n",
              "      <td>334.40</td>\n",
              "      <td>334.400000</td>\n",
              "      <td>334.40</td>\n",
              "      <td>48.444976</td>\n",
              "      <td>11.961722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.692584</td>\n",
              "      <td>27.900718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352</td>\n",
              "      <td>7</td>\n",
              "      <td>144.35</td>\n",
              "      <td>840.30</td>\n",
              "      <td>340.815714</td>\n",
              "      <td>2385.71</td>\n",
              "      <td>15.705178</td>\n",
              "      <td>64.322571</td>\n",
              "      <td>14.601523</td>\n",
              "      <td>1.299404</td>\n",
              "      <td>4.071325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  count      min  ...    categ_2    categ_3    categ_4\n",
              "0      12347      7   224.82  ...  29.540371  11.237123  20.805104\n",
              "1      12348      4   227.44  ...   0.000000  38.016069   0.000000\n",
              "2      12349      1  1757.55  ...  10.713778   4.513101  12.245455\n",
              "3      12350      1   334.40  ...   0.000000  11.692584  27.900718\n",
              "4      12352      7   144.35  ...  14.601523   1.299404   4.071325\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oa_SLCtobC5g"
      },
      "source": [
        "**Task** : find the number and the percentage of customers who purchased only once."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsXmeSGKaEmh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec4452c8-a906-4ed7-9e4a-166948254ce3"
      },
      "source": [
        "# Customers purchase only once\n",
        "n1 = transactions_per_user[transactions_per_user['count'] == 1].shape[0]\n",
        "n2 = transactions_per_user.shape[0]\n",
        "print(\"number of clients with only one purchase: {:<2}/{:<5} ({:<2.2f}%)\".format(n1,n2,n1/n2*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of clients with only one purchase: 1489/4327  (34.41%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0QDml6wTdYhB"
      },
      "source": [
        "## 4.5) Creation of customers categories\n",
        "Choose features: `count`,`min`,`max`,`mean`,`categ_0`,`categ_1`,`categ_2`,`categ_3`,`categ_4` and create new matrix `matrix_customers`. Use standard scaler and call the result as `scaled_matrix_customers`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfYHmvVBbJMo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "16eae663-7b52-4e8e-b8da-216a1f11cd81"
      },
      "source": [
        "# Use the features below\n",
        "list_cols = ['count','min','max','mean','categ_0','categ_1','categ_2','categ_3','categ_4']\n",
        "\n",
        "selected_customers = transactions_per_user.copy(deep = True)\n",
        "matrix_customers = selected_customers[list_cols].values \n",
        "selected_customers.head()\n",
        "#matrix_customers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\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>CustomerID</th>\n",
              "      <th>count</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12347</td>\n",
              "      <td>7</td>\n",
              "      <td>224.82</td>\n",
              "      <td>1294.32</td>\n",
              "      <td>615.714286</td>\n",
              "      <td>4310.00</td>\n",
              "      <td>26.375870</td>\n",
              "      <td>12.041531</td>\n",
              "      <td>29.540371</td>\n",
              "      <td>11.237123</td>\n",
              "      <td>20.805104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12348</td>\n",
              "      <td>4</td>\n",
              "      <td>227.44</td>\n",
              "      <td>892.80</td>\n",
              "      <td>449.310000</td>\n",
              "      <td>1797.24</td>\n",
              "      <td>41.953217</td>\n",
              "      <td>20.030714</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.016069</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12349</td>\n",
              "      <td>1</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>1757.550000</td>\n",
              "      <td>1757.55</td>\n",
              "      <td>26.506216</td>\n",
              "      <td>46.021450</td>\n",
              "      <td>10.713778</td>\n",
              "      <td>4.513101</td>\n",
              "      <td>12.245455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12350</td>\n",
              "      <td>1</td>\n",
              "      <td>334.40</td>\n",
              "      <td>334.40</td>\n",
              "      <td>334.400000</td>\n",
              "      <td>334.40</td>\n",
              "      <td>48.444976</td>\n",
              "      <td>11.961722</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>11.692584</td>\n",
              "      <td>27.900718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12352</td>\n",
              "      <td>7</td>\n",
              "      <td>144.35</td>\n",
              "      <td>840.30</td>\n",
              "      <td>340.815714</td>\n",
              "      <td>2385.71</td>\n",
              "      <td>15.705178</td>\n",
              "      <td>64.322571</td>\n",
              "      <td>14.601523</td>\n",
              "      <td>1.299404</td>\n",
              "      <td>4.071325</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  count      min  ...    categ_2    categ_3    categ_4\n",
              "0      12347      7   224.82  ...  29.540371  11.237123  20.805104\n",
              "1      12348      4   227.44  ...   0.000000  38.016069   0.000000\n",
              "2      12349      1  1757.55  ...  10.713778   4.513101  12.245455\n",
              "3      12350      1   334.40  ...   0.000000  11.692584  27.900718\n",
              "4      12352      7   144.35  ...  14.601523   1.299404   4.071325\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qV85BT-nIhZu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vG4xDqMgdmDL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f6bf38f-de91-4ceb-97ee-1daef0cf85d3"
      },
      "source": [
        "# Standardization\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(matrix_customers)\n",
        "print('variables mean values: \\n' + 90*'-' + '\\n' , scaler.mean_)\n",
        "\n",
        "scaled_matrix_customers = scaler.transform(matrix_customers)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "variables mean values: \n",
            "------------------------------------------------------------------------------------------\n",
            " [  4.25190663 241.38253571 578.87676959 372.25705475  25.30761412\n",
            "  22.00477215  16.97264402  14.09365548  21.62911535]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Wu2aUcnssVT"
      },
      "source": [
        "## 4.6) K-Means again - for customers\n",
        "**Task:** Apply KMeans to `scaled_matrix_customers` with numbers of clusters from 3 to 14 included. Compute the silhouetter score for each number. Print number of clients in each category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wqHYvwyUds2V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6330476-e6ae-4673-c6c6-76f90a28dbd0"
      },
      "source": [
        "# Try some numbers of clusters\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "for n_clusters in range(3,15):\n",
        "    kmeans = KMeans(init='k-means++', n_clusters = n_clusters, n_init=100)\n",
        "    kmeans.fit(scaled_matrix_customers)\n",
        "    clusters = kmeans.predict(scaled_matrix_customers)\n",
        "    print(pd.DataFrame(pd.Series(clusters).value_counts(), columns = ['nb. of clients']).T)\n",
        "    silhouette_avg = silhouette_score(scaled_matrix_customers, clusters)\n",
        "    print(\"For n_clusters =\", n_clusters, \"The average silhouette_score is :\", silhouette_avg)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                   0     1   2\n",
            "nb. of clients  2558  1705  64\n",
            "For n_clusters = 3 The average silhouette_score is : 0.15681191246164491\n",
            "                   1     0    2   3\n",
            "nb. of clients  2080  1372  838  37\n",
            "For n_clusters = 4 The average silhouette_score is : 0.15382737482907696\n",
            "                   2     3    0    1   4\n",
            "nb. of clients  1402  1363  854  677  31\n",
            "For n_clusters = 5 The average silhouette_score is : 0.16574378634467926\n",
            "                   3     0    2    1   5   4\n",
            "nb. of clients  1413  1216  920  702  47  29\n",
            "For n_clusters = 6 The average silhouette_score is : 0.1699293956431382\n",
            "                   3     5    2    6    1   0   4\n",
            "nb. of clients  1286  1137  835  647  346  47  29\n",
            "For n_clusters = 7 The average silhouette_score is : 0.18376988234658756\n",
            "                   1    3    0    4    7    5   2   6\n",
            "nb. of clients  1997  597  578  385  364  330  47  29\n",
            "For n_clusters = 8 The average silhouette_score is : 0.20360951878960545\n",
            "                   6    1    2    7    0    8    3   5   4\n",
            "nb. of clients  1902  598  586  365  315  311  208  30  12\n",
            "For n_clusters = 9 The average silhouette_score is : 0.21390493306734373\n",
            "                   5    3    2    0    1    6    8   7   4  9\n",
            "nb. of clients  1879  591  579  363  359  311  208  18  12  7\n",
            "For n_clusters = 10 The average silhouette_score is : 0.21363016488463651\n",
            "                  6    10   5    0    3    1    2    8   7   4   9 \n",
            "nb. of clients  1865  571  527  327  306  305  204  186  17  12   7\n",
            "For n_clusters = 11 The average silhouette_score is : 0.22279484262381627\n",
            "                  8     1    6    4    7    0    10   2    11  5   3   9 \n",
            "nb. of clients  1324  1058  402  301  297  292  256  187  174  17  12   7\n",
            "For n_clusters = 12 The average silhouette_score is : 0.18443901630428555\n",
            "                  5     9    1    3    0    4    2    11   8   6   7   10  12\n",
            "nb. of clients  1301  1056  401  299  295  291  246  200  190  23  13   7   5\n",
            "For n_clusters = 13 The average silhouette_score is : 0.18711568175134627\n",
            "                  0    6    3    2    13   7   ...   10   8   12  4   5   9 \n",
            "nb. of clients  1104  971  579  375  290  281  ...  175  101  24  10   7   5\n",
            "\n",
            "[1 rows x 14 columns]\n",
            "For n_clusters = 14 The average silhouette_score is : 0.18721260376579735\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qHQjiPxPs-dn"
      },
      "source": [
        "Based on the silhouette score we use 8 clusters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXXkLu8is1rl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2f01601-86a8-44ef-ccdb-4dbd88ced5bf"
      },
      "source": [
        "# 8 clusters\n",
        "n_clusters = 8\n",
        "kmeans = KMeans(init='k-means++', n_clusters = n_clusters, n_init=1000)\n",
        "kmeans.fit(scaled_matrix_customers)\n",
        "clusters_clients = kmeans.predict(scaled_matrix_customers)\n",
        "silhouette_avg = silhouette_score(scaled_matrix_customers, clusters_clients)\n",
        "print('score of silhouette: {:<.3f}'.format(silhouette_avg))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "score of silhouette: 0.202\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUdjvcWmtV3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 76
        },
        "outputId": "26ad6625-6520-4d39-8f90-7ef88ba757aa"
      },
      "source": [
        "# nb of customers per cluster\n",
        "pd.DataFrame(pd.Series(clusters_clients).value_counts(), columns = ['nb. of clients']).T"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>2</th>\n",
              "      <th>1</th>\n",
              "      <th>7</th>\n",
              "      <th>0</th>\n",
              "      <th>6</th>\n",
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              "      <th>4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>nb. of clients</th>\n",
              "      <td>1977</td>\n",
              "      <td>620</td>\n",
              "      <td>566</td>\n",
              "      <td>393</td>\n",
              "      <td>368</td>\n",
              "      <td>327</td>\n",
              "      <td>47</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   2    1    7    0    6    5   3   4\n",
              "nb. of clients  1977  620  566  393  368  327  47  29"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tx2luQ1tw1C"
      },
      "source": [
        "## 4.7) Customers morphotype\n",
        "\n",
        "In this section, we compute the statistics for each group of customers and visulize them. Create new `merged_df` dataset with the same features as `transactions_per_user` dataframe, but with `n_clusters` rows: for each cluster - use mean of values. So you should have dataframe with sizes `n_cluster` $\\times$ 12."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aBw8_r3ptjFl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "outputId": "540d3dad-e0a8-4bad-cfca-bbec6b7f8d25"
      },
      "source": [
        "# Customer clusters\n",
        "selected_customers.loc[:, 'cluster'] = clusters_clients\n",
        "\n",
        "merged_df = pd.DataFrame()\n",
        "for i in range(n_clusters):\n",
        "    test = pd.DataFrame(selected_customers[selected_customers['cluster'] == i].mean())\n",
        "    test = test.T.set_index('cluster', drop = True)\n",
        "    test['size'] = selected_customers[selected_customers['cluster'] == i].shape[0]\n",
        "    merged_df = pd.concat([merged_df, test])\n",
        "\n",
        "merged_df.drop('CustomerID', axis = 1, inplace = True)\n",
        "print('number of customers:', merged_df['size'].sum())\n",
        "\n",
        "merged_df = merged_df.sort_values('sum')\n",
        "\n",
        "merged_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of customers: 4327\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "      <th>mean</th>\n",
              "      <th>sum</th>\n",
              "      <th>categ_0</th>\n",
              "      <th>categ_1</th>\n",
              "      <th>categ_2</th>\n",
              "      <th>categ_3</th>\n",
              "      <th>categ_4</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6.0</th>\n",
              "      <td>2.595109</td>\n",
              "      <td>185.364076</td>\n",
              "      <td>330.573505</td>\n",
              "      <td>246.296149</td>\n",
              "      <td>712.924402</td>\n",
              "      <td>20.879883</td>\n",
              "      <td>8.469838</td>\n",
              "      <td>7.634581</td>\n",
              "      <td>50.493944</td>\n",
              "      <td>12.522818</td>\n",
              "      <td>368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>2.725806</td>\n",
              "      <td>217.657645</td>\n",
              "      <td>368.403097</td>\n",
              "      <td>285.541251</td>\n",
              "      <td>811.728744</td>\n",
              "      <td>54.528862</td>\n",
              "      <td>8.956022</td>\n",
              "      <td>8.778305</td>\n",
              "      <td>13.166665</td>\n",
              "      <td>14.582050</td>\n",
              "      <td>620</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7.0</th>\n",
              "      <td>3.231449</td>\n",
              "      <td>201.029931</td>\n",
              "      <td>370.132456</td>\n",
              "      <td>271.646408</td>\n",
              "      <td>916.564737</td>\n",
              "      <td>11.427526</td>\n",
              "      <td>61.064607</td>\n",
              "      <td>11.423976</td>\n",
              "      <td>4.982819</td>\n",
              "      <td>11.111218</td>\n",
              "      <td>566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>2.687023</td>\n",
              "      <td>214.238957</td>\n",
              "      <td>393.167913</td>\n",
              "      <td>291.144989</td>\n",
              "      <td>960.299466</td>\n",
              "      <td>14.231839</td>\n",
              "      <td>14.829260</td>\n",
              "      <td>49.668579</td>\n",
              "      <td>7.599772</td>\n",
              "      <td>13.696167</td>\n",
              "      <td>393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5.0</th>\n",
              "      <td>3.354740</td>\n",
              "      <td>203.450183</td>\n",
              "      <td>416.931957</td>\n",
              "      <td>301.838621</td>\n",
              "      <td>1117.250917</td>\n",
              "      <td>14.114067</td>\n",
              "      <td>8.866478</td>\n",
              "      <td>7.586403</td>\n",
              "      <td>6.908671</td>\n",
              "      <td>62.524381</td>\n",
              "      <td>327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>5.063733</td>\n",
              "      <td>236.004239</td>\n",
              "      <td>616.853127</td>\n",
              "      <td>397.904242</td>\n",
              "      <td>2020.116814</td>\n",
              "      <td>25.204673</td>\n",
              "      <td>20.914552</td>\n",
              "      <td>17.896790</td>\n",
              "      <td>12.777936</td>\n",
              "      <td>23.211086</td>\n",
              "      <td>1977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1.638298</td>\n",
              "      <td>2317.574681</td>\n",
              "      <td>3466.916170</td>\n",
              "      <td>2861.176454</td>\n",
              "      <td>4996.679149</td>\n",
              "      <td>18.968099</td>\n",
              "      <td>26.395909</td>\n",
              "      <td>18.342729</td>\n",
              "      <td>12.794189</td>\n",
              "      <td>23.500511</td>\n",
              "      <td>47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>58.034483</td>\n",
              "      <td>44.381379</td>\n",
              "      <td>9376.841724</td>\n",
              "      <td>1899.264177</td>\n",
              "      <td>73791.414483</td>\n",
              "      <td>21.269789</td>\n",
              "      <td>22.983912</td>\n",
              "      <td>16.483369</td>\n",
              "      <td>10.645415</td>\n",
              "      <td>28.622631</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count          min          max  ...    categ_3    categ_4  size\n",
              "cluster                                       ...                            \n",
              "6.0       2.595109   185.364076   330.573505  ...  50.493944  12.522818   368\n",
              "1.0       2.725806   217.657645   368.403097  ...  13.166665  14.582050   620\n",
              "7.0       3.231449   201.029931   370.132456  ...   4.982819  11.111218   566\n",
              "0.0       2.687023   214.238957   393.167913  ...   7.599772  13.696167   393\n",
              "5.0       3.354740   203.450183   416.931957  ...   6.908671  62.524381   327\n",
              "2.0       5.063733   236.004239   616.853127  ...  12.777936  23.211086  1977\n",
              "3.0       1.638298  2317.574681  3466.916170  ...  12.794189  23.500511    47\n",
              "4.0      58.034483    44.381379  9376.841724  ...  10.645415  28.622631    29\n",
              "\n",
              "[8 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKO7OG_KuweZ"
      },
      "source": [
        "## 4.8) Data visualisation\n",
        "Just run the code to get plots."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Drn27rIEt2SP"
      },
      "source": [
        "# function to plot\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def _scale_data(data, ranges):\n",
        "    (x1, x2) = ranges[0]\n",
        "    d = data[0]\n",
        "    return [(d - y1) / (y2 - y1) * (x2 - x1) + x1 for d, (y1, y2) in zip(data, ranges)]\n",
        "\n",
        "class RadarChart():\n",
        "    def __init__(self, fig, location, sizes, variables, ranges, n_ordinate_levels = 6):\n",
        "\n",
        "        angles = np.arange(0, 360, 360./len(variables))\n",
        "\n",
        "        ix, iy = location[:] ; size_x, size_y = sizes[:]\n",
        "        \n",
        "        axes = [fig.add_axes([ix, iy, size_x, size_y], polar = True, \n",
        "        label = \"axes{}\".format(i)) for i in range(len(variables))]\n",
        "\n",
        "        _, text = axes[0].set_thetagrids(angles, labels = variables)\n",
        "        \n",
        "        for txt, angle in zip(text, angles):\n",
        "            if angle > -1 and angle < 181:\n",
        "                txt.set_rotation(angle - 90)\n",
        "            else:\n",
        "                txt.set_rotation(angle - 270)\n",
        "        \n",
        "        for ax in axes[1:]:\n",
        "            ax.patch.set_visible(False)\n",
        "            ax.xaxis.set_visible(False)\n",
        "            ax.grid(\"off\")\n",
        "        \n",
        "        for i, ax in enumerate(axes):\n",
        "            grid = np.linspace(*ranges[i],num = n_ordinate_levels)\n",
        "            grid_label = [\"\"]+[\"{:.0f}\".format(x) for x in grid[1:-1]]\n",
        "            ax.set_rgrids(grid, labels = grid_label, angle = angles[i])\n",
        "            ax.set_ylim(*ranges[i])\n",
        "        \n",
        "        self.angle = np.deg2rad(np.r_[angles, angles[0]])\n",
        "        self.ranges = ranges\n",
        "        self.ax = axes[0]\n",
        "                \n",
        "    def plot(self, data, *args, **kw):\n",
        "        sdata = _scale_data(data, self.ranges)\n",
        "        self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw)\n",
        "\n",
        "    def fill(self, data, *args, **kw):\n",
        "        sdata = _scale_data(data, self.ranges)\n",
        "        self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw)\n",
        "\n",
        "    def legend(self, *args, **kw):\n",
        "        self.ax.legend(*args, **kw)\n",
        "        \n",
        "    def title(self, title, *args, **kw):\n",
        "        self.ax.text(0.9, 1, title, transform = self.ax.transAxes, *args, **kw)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZFkstBduzol",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "945d1a44-ac32-473b-c68c-35b4b1e725e6"
      },
      "source": [
        "# display\n",
        "fig = plt.figure(figsize=(10,12))\n",
        "\n",
        "attributes = ['count', 'mean', 'sum', 'categ_0', 'categ_1', 'categ_2', 'categ_3', 'categ_4']\n",
        "ranges = [[0.01, 10], [0.01, 1500], [0.01, 10000], [0.01, 75], [0.01, 75], [0.01, 75], [0.01, 75], [0.01, 75]]\n",
        "index  = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n",
        "\n",
        "n_groups = n_clusters ; i_cols = 3\n",
        "i_rows = n_groups//i_cols\n",
        "size_x, size_y = (1/i_cols), (1/i_rows)\n",
        "\n",
        "for ind in range(n_clusters):\n",
        "    ix = ind%3 ; iy = i_rows - ind//3\n",
        "    pos_x = ix*(size_x + 0.05) ; pos_y = iy*(size_y + 0.05)            \n",
        "    location = [pos_x, pos_y]  ; sizes = [size_x, size_y] \n",
        "    \n",
        "    #\n",
        "    data = np.array(merged_df.loc[index[ind], attributes])    \n",
        "    radar = RadarChart(fig, location, sizes, attributes, ranges)\n",
        "    radar.plot(data, color = 'b', linewidth=2.0)\n",
        "    radar.fill(data, alpha = 0.2, color = 'b')\n",
        "    radar.title(title = 'cluster nº{}'.format(index[ind]), color = 'r')\n",
        "    ind += 1 "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x864 with 64 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ma6t9IxkwsWq"
      },
      "source": [
        "## 4.9) Save data\n",
        "Save selected_customers data to the file `data_selected_customers.csv`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NNM6hiZvAS0"
      },
      "source": [
        "selected_customers.to_csv(relative_path + 'data_selected_customers.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vvxB4cgOxWPr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}