2_Customer_segmentation.ipynb

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  "metadata": {
    "colab": {
      "name": "B1.5.2 Customer segmentation - teacher's sheet.ipynb",
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    },
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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xX1VYE4L-v71"
      },
      "source": [
        "# 2) Exploration\n",
        "\n",
        "As you can see in the description of the data, it contains 8 variables:\n",
        "\n",
        "**InvoiceNo**: Invoice number. Nominal, a 6-digit integral number uniquely assigned to each transaction. If this code starts with letter 'c', it indicates a cancellation.\n",
        "\n",
        "**StockCode**: Product (item) code. Nominal, a 5-digit integral number uniquely assigned to each distinct product.\n",
        "\n",
        "**Description**: Product (item) name. Nominal.\n",
        "\n",
        "**Quantity**: The quantities of each product (item) per transaction. Numeric.\n",
        "\n",
        "**InvoiceDate**: Invoice Date and time. Numeric, the day and time when each transaction was generated.\n",
        "\n",
        "**UnitPrice**: Unit price. Numeric, Product price per unit in sterling.\n",
        "\n",
        "**CustomerID**: Customer number. Nominal, a 5-digit integral number uniquely assigned to each customer.\n",
        "\n",
        "**Country**: Country name. Nominal, the name of the country where each customer resides."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4v5Zy0-u-0uk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "9e384c7b-2501-4598-9cba-d370a5679070"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "relative_path = '/content/drive/My Drive/Course v2/B1. Machine Learning Deep Learning/B1.5 Customer Segmentation/Data/'\n",
        "\n",
        "df_dropna = pd.read_csv(relative_path + 'data_dropna.csv', encoding='latin', dtype={'CustomerID': str}) \n",
        "df = pd.read_csv(relative_path + 'data.csv', encoding='latin', dtype={'CustomerID': str}) \n",
        "df_dropna.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        },
        {
          "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>Unnamed: 0</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>401604.000000</td>\n",
              "      <td>401604.000000</td>\n",
              "      <td>401604.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>277729.795869</td>\n",
              "      <td>12.183273</td>\n",
              "      <td>3.474064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>152507.474480</td>\n",
              "      <td>250.283037</td>\n",
              "      <td>69.764035</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>147762.750000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>283870.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>1.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>408888.250000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>541908.000000</td>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 0       Quantity      UnitPrice\n",
              "count  401604.000000  401604.000000  401604.000000\n",
              "mean   277729.795869      12.183273       3.474064\n",
              "std    152507.474480     250.283037      69.764035\n",
              "min         0.000000  -80995.000000       0.000000\n",
              "25%    147762.750000       2.000000       1.250000\n",
              "50%    283870.500000       5.000000       1.950000\n",
              "75%    408888.250000      12.000000       3.750000\n",
              "max    541908.000000   80995.000000   38970.000000"
            ]
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          "metadata": {
            "tags": []
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          "execution_count": 3
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yScaF9K_Wr_"
      },
      "source": [
        "## 2.1) Number of customers (/products/countries)\n",
        "\n",
        "**Task:** Find the numbers of customers, products and transaction (let's say each transaction corresponds to a unique InvoiceNo).\n",
        "\n",
        "**Task:** Find top 10 countries with the largest numbers of purchases sorted in descreasing order.  Use `.groupby` and `.unique` functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PCXSYqJB_XBh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164
        },
        "outputId": "63654a20-de76-4d8d-f0de-c7a935927d21"
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      "source": [
        "# As we can see below on the row \"unique\", there are 22190 transactions, 3684 products and 4372 customers.\n",
        "df_dropna[['InvoiceNo', 'StockCode', 'Description', 'InvoiceDate', 'CustomerID', 'Country']].describe()"
      ],
      "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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>401604</td>\n",
              "      <td>401604</td>\n",
              "      <td>401604</td>\n",
              "      <td>401604</td>\n",
              "      <td>401604</td>\n",
              "      <td>401604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>22190</td>\n",
              "      <td>3684</td>\n",
              "      <td>3896</td>\n",
              "      <td>20460</td>\n",
              "      <td>4372</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>576339</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>2011-11-14 15:27:00</td>\n",
              "      <td>17841</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>542</td>\n",
              "      <td>2065</td>\n",
              "      <td>2058</td>\n",
              "      <td>543</td>\n",
              "      <td>7812</td>\n",
              "      <td>356728</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       InvoiceNo StockCode  ... CustomerID         Country\n",
              "count     401604    401604  ...     401604          401604\n",
              "unique     22190      3684  ...       4372              37\n",
              "top       576339    85123A  ...      17841  United Kingdom\n",
              "freq         542      2065  ...       7812          356728\n",
              "\n",
              "[4 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "SV7Q9DKL_mAk",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b0ef2d0-642c-43c5-b42b-c494bf87cbe1"
      },
      "source": [
        "df_dropna['Country'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
              "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
              "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
              "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',\n",
              "       'Israel', 'Finland', 'Greece', 'Singapore', 'Lebanon',\n",
              "       'United Arab Emirates', 'Saudi Arabia', 'Czech Republic', 'Canada',\n",
              "       'Unspecified', 'Brazil', 'USA', 'European Community', 'Bahrain',\n",
              "       'Malta', 'RSA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S8TKICodBM7L",
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          "height": 370
        },
        "outputId": "6cc0115d-ad3f-4753-ffca-a2063a373769"
      },
      "source": [
        "df_dropna[['Country', 'InvoiceNo']].groupby('Country').count()\\\n",
        ".sort_values(by='InvoiceNo', ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>United Kingdom</th>\n",
              "      <td>356728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Germany</th>\n",
              "      <td>9480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>France</th>\n",
              "      <td>8475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EIRE</th>\n",
              "      <td>7475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spain</th>\n",
              "      <td>2528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Netherlands</th>\n",
              "      <td>2371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Belgium</th>\n",
              "      <td>2069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Switzerland</th>\n",
              "      <td>1877</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portugal</th>\n",
              "      <td>1471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>1258</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "                InvoiceNo\n",
              "Country                  \n",
              "United Kingdom     356728\n",
              "Germany              9480\n",
              "France               8475\n",
              "EIRE                 7475\n",
              "Spain                2528\n",
              "Netherlands          2371\n",
              "Belgium              2069\n",
              "Switzerland          1877\n",
              "Portugal             1471\n",
              "Australia            1258"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5GuiwbEBemn"
      },
      "source": [
        "## 2.2) Products per transaction\n",
        "\n",
        "**Task:** Find the numbers of products in each transaction, then show the top 10, use `.groupby`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7PXcBivbXnP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "outputId": "1cf22382-0d01-4c3e-be0f-d4a2cb5b95d1"
      },
      "source": [
        "df_dropna[['InvoiceNo','StockCode']]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401599</th>\n",
              "      <td>581587</td>\n",
              "      <td>22613</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401600</th>\n",
              "      <td>581587</td>\n",
              "      <td>22899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401601</th>\n",
              "      <td>581587</td>\n",
              "      <td>23254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401602</th>\n",
              "      <td>581587</td>\n",
              "      <td>23255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>401603</th>\n",
              "      <td>581587</td>\n",
              "      <td>22138</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>401604 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       InvoiceNo StockCode\n",
              "0         536365    85123A\n",
              "1         536365     71053\n",
              "2         536365    84406B\n",
              "3         536365    84029G\n",
              "4         536365    84029E\n",
              "...          ...       ...\n",
              "401599    581587     22613\n",
              "401600    581587     22899\n",
              "401601    581587     23254\n",
              "401602    581587     23255\n",
              "401603    581587     22138\n",
              "\n",
              "[401604 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5UUOdbKxBe4U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "outputId": "868ef9ed-1662-4123-c356-7a4a393cb656"
      },
      "source": [
        "# number of products per transaction\n",
        "df_dropna[['InvoiceNo','StockCode']].groupby(['InvoiceNo']).count()\\\n",
        ".rename(columns = {'StockCode':'Number of products'})\\\n",
        ".sort_values(by='Number of products', ascending=False).head(10)"
      ],
      "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>Number of products</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>576339</th>\n",
              "      <td>542</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>579196</th>\n",
              "      <td>533</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>580727</th>\n",
              "      <td>529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>578270</th>\n",
              "      <td>442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>573576</th>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567656</th>\n",
              "      <td>421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567183</th>\n",
              "      <td>392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>575607</th>\n",
              "      <td>377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>571441</th>\n",
              "      <td>364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>570488</th>\n",
              "      <td>353</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Number of products\n",
              "InvoiceNo                    \n",
              "576339                    542\n",
              "579196                    533\n",
              "580727                    529\n",
              "578270                    442\n",
              "573576                    435\n",
              "567656                    421\n",
              "567183                    392\n",
              "575607                    377\n",
              "571441                    364\n",
              "570488                    353"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wvfesr10Bt5k"
      },
      "source": [
        "## 2.3) Transactions per country\n",
        "\n",
        "Find the numbers of unique invoice numbers per country, using `.groupby` and `.nunique`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JYw5pN8iZvOW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5744aa1b-be9d-4443-ed32-c201f1250e6a"
      },
      "source": [
        "# transactions per country\n",
        "#transPerCountry = df_dropna[['InvoiceNo', 'Country']].groupby(['InvoiceNo', 'Country']).count().reset_index(drop = False)\\\n",
        "#['Country'].value_counts()\n",
        "#transPerCountry\n",
        "\n",
        "transPerCountry = df_dropna.groupby(['Country'])['InvoiceNo'].nunique()\n",
        "transPerCountry.sort_values(ascending=False).head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United Kingdom    19857\n",
              "Germany             603\n",
              "France              458\n",
              "EIRE                319\n",
              "Belgium             119\n",
              "Spain               105\n",
              "Netherlands         101\n",
              "Switzerland          71\n",
              "Portugal             70\n",
              "Australia            69\n",
              "Name: InvoiceNo, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-GJmmffgck_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f53921b3-f087-446c-a37e-61023e261651"
      },
      "source": [
        "type(transPerCountry)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.series.Series"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIBaxYrrB_dN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "74e1f4df-14e5-4811-f1f8-03aacf999388"
      },
      "source": [
        "# choropleth map\n",
        "import plotly.graph_objs as go\n",
        "from plotly.offline import init_notebook_mode,iplot\n",
        "\n",
        "data = dict(type='choropleth',\n",
        "locations = transPerCountry.index,\n",
        "locationmode = 'country names', z = transPerCountry,\n",
        "text = transPerCountry.index, colorbar = {'title':'Order nb.'},\n",
        "colorscale=[[0, 'rgb(224,255,255)'],\n",
        "            [0.01, 'rgb(166,206,227)'], [0.02, 'rgb(31,120,180)'],\n",
        "            [0.03, 'rgb(178,223,138)'], [0.05, 'rgb(51,160,44)'],\n",
        "            [0.10, 'rgb(251,154,153)'], [0.20, 'rgb(255,255,0)'],\n",
        "            [1, 'rgb(227,26,28)']],    \n",
        "reversescale = False)\n",
        "\n",
        "layout = dict(title='Number of transactions per country',\n",
        "geo = dict(showframe = True, projection={'type':'mercator'}))\n",
        "\n",
        "choromap = go.Figure(data = [data], layout = layout)\n",
        "iplot(choromap, validate=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TP4_K5YHCVju"
      },
      "source": [
        "## 2.4) Cancelling orders\n",
        "\n",
        "Among observations, there are those with negative quantities. They correspond to cancelling orders (transactions).\n",
        "\n",
        "**Task:** Compute the number of cancelled transactions (cancelled transactions' InvoiceNo contain letter 'C'). What percentage does it represents?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYjsfnnWuEBQ"
      },
      "source": [
        "#orders[2100:2200]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "11MtPwrLCcYe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e08010da-b3f6-4bc9-9a48-c94319ba22aa"
      },
      "source": [
        "orders = df_dropna.InvoiceNo.unique()\n",
        "n_orders = orders.shape[0]\n",
        "n_cancelled_orders = pd.Series(orders).apply(lambda x: 'C' in x).sum()\n",
        "\n",
        "print('Number of cancelled transactions: {}/{} ({:0.2f}%)'.format(n_cancelled_orders, n_orders,\\\n",
        "n_cancelled_orders/n_orders*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of cancelled transactions: 3654/22190 (16.47%)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VXfeS-kEEkYc",
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          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a3cf5b3f-f3c9-4618-b98e-7dd5a95ee4c3"
      },
      "source": [
        "df.loc[df.InvoiceNo.apply(lambda x: 'C' in x)].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9288.000000</td>\n",
              "      <td>9288.000000</td>\n",
              "      <td>9288.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>257872.809216</td>\n",
              "      <td>-29.885228</td>\n",
              "      <td>48.393661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>150143.638467</td>\n",
              "      <td>1145.786965</td>\n",
              "      <td>666.600430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>141.000000</td>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>0.010000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>127557.750000</td>\n",
              "      <td>-6.000000</td>\n",
              "      <td>1.450000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>255476.500000</td>\n",
              "      <td>-2.000000</td>\n",
              "      <td>2.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>383071.250000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>5.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>541717.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Unnamed: 0      Quantity     UnitPrice\n",
              "count    9288.000000   9288.000000   9288.000000\n",
              "mean   257872.809216    -29.885228     48.393661\n",
              "std    150143.638467   1145.786965    666.600430\n",
              "min       141.000000 -80995.000000      0.010000\n",
              "25%    127557.750000     -6.000000      1.450000\n",
              "50%    255476.500000     -2.000000      2.950000\n",
              "75%    383071.250000     -1.000000      5.950000\n",
              "max    541717.000000     -1.000000  38970.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bhaj6ULTExzO"
      },
      "source": [
        "Cancelling orders occur due to customer wishes or product discounts:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cz-enJzuEqbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "3dc69cdd-3a50-4285-810c-8c8113ec1407"
      },
      "source": [
        "df.loc[df.InvoiceNo.apply(lambda x: 'C' in x)].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>Unnamed: 0</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>141</th>\n",
              "      <td>141</td>\n",
              "      <td>C536379</td>\n",
              "      <td>D</td>\n",
              "      <td>Discount</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:41:00</td>\n",
              "      <td>27.50</td>\n",
              "      <td>14527</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>154</td>\n",
              "      <td>C536383</td>\n",
              "      <td>35004C</td>\n",
              "      <td>SET OF 3 COLOURED  FLYING DUCKS</td>\n",
              "      <td>-1</td>\n",
              "      <td>2010-12-01 09:49:00</td>\n",
              "      <td>4.65</td>\n",
              "      <td>15311</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>235</td>\n",
              "      <td>C536391</td>\n",
              "      <td>22556</td>\n",
              "      <td>PLASTERS IN TIN CIRCUS PARADE</td>\n",
              "      <td>-12</td>\n",
              "      <td>2010-12-01 10:24:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>17548</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>236</td>\n",
              "      <td>C536391</td>\n",
              "      <td>21984</td>\n",
              "      <td>PACK OF 12 PINK PAISLEY TISSUES</td>\n",
              "      <td>-24</td>\n",
              "      <td>2010-12-01 10:24:00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>17548</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>237</td>\n",
              "      <td>C536391</td>\n",
              "      <td>21983</td>\n",
              "      <td>PACK OF 12 BLUE PAISLEY TISSUES</td>\n",
              "      <td>-24</td>\n",
              "      <td>2010-12-01 10:24:00</td>\n",
              "      <td>0.29</td>\n",
              "      <td>17548</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Unnamed: 0 InvoiceNo StockCode  ... UnitPrice  CustomerID         Country\n",
              "141         141   C536379         D  ...     27.50       14527  United Kingdom\n",
              "154         154   C536383    35004C  ...      4.65       15311  United Kingdom\n",
              "235         235   C536391     22556  ...      1.65       17548  United Kingdom\n",
              "236         236   C536391     21984  ...      0.29       17548  United Kingdom\n",
              "237         237   C536391     21983  ...      0.29       17548  United Kingdom\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aswvk0SqE4ZF"
      },
      "source": [
        "When there is a cancelling order, it's often the case that part of the command has been canceled. There is a counterpart which is a another purchase order with the same CustomerID, Description and UnitPrice, but different InvoiceID and date.\n",
        "\n",
        "Cancellations without a counterpart problaly due to the fact that the purchase commands happen before the beginning date of the data set, or they are discounts.\n",
        "\n",
        "We will create a new variable `QuantityCanceled` in the dataframe that indicate if part of the command has been canceled. \n",
        "\n",
        "We will also check the two cases:\n",
        "\n",
        "* a cancel order exists without counterpart\n",
        "* there's at least one counterpart \n",
        "\n",
        "The index of the corresponding cancel orders are respectively kept in the `doubtfull_entry` and `entry_to_remove` lists."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1JX1lJ2C2p_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1adcf1fe-a00c-4117-e701-04914215a48a"
      },
      "source": [
        "idx = pd.IndexSlice\n",
        "df_customer_stockcode = df_dropna.set_index(['CustomerID', 'StockCode'], append=True)\n",
        "\n",
        "import timeit\n",
        "\n",
        "start = timeit.default_timer()\n",
        "\n",
        "# Note: we don't consider discount entries\n",
        "# doubtfull_entry: cancel entries with no counterpart\n",
        "# entry_to_remove: cancel entries with counterparts and there is at least one entry with higher quantity!\n",
        "\n",
        "df_cleaned = df_dropna.copy(deep = True)\n",
        "df_cleaned['QuantityCanceled'] = 0\n",
        "\n",
        "entry_to_remove = [] ; doubtfull_entry = []\n",
        "\n",
        "for index, col in  df_dropna.iterrows():\n",
        "\n",
        "    if (col['Quantity'] > 0) or col['Description'] == 'Discount': continue\n",
        "    df_test = df_customer_stockcode.loc[idx[:, col['CustomerID'], col['StockCode']], :]\n",
        "    df_test = df_test[(df_test['InvoiceDate'] < col['InvoiceDate']) & (df_test['Quantity']   > 0)]\n",
        "    \n",
        "    # Cancelation WITHOUT counterpart\n",
        "    if (df_test.shape[0] == 0): \n",
        "        doubtfull_entry.append(index)\n",
        "    \n",
        "    # Cancelation WITH only one counterpart\n",
        "    elif (df_test.shape[0] == 1): \n",
        "        index_order = df_test.index[0][0]\n",
        "        df_cleaned.loc[index_order, 'QuantityCanceled'] = -col['Quantity']\n",
        "        entry_to_remove.append(index)        \n",
        "    \n",
        "    # Various counterparts exist in orders: we delete the last one\n",
        "    elif (df_test.shape[0] > 1): \n",
        "        df_test.sort_index(axis=0, ascending=False, inplace = True)        \n",
        "        for ind, val in df_test.iterrows():\n",
        "            if val['Quantity'] < -col['Quantity']: continue\n",
        "            df_cleaned.loc[ind[0], 'QuantityCanceled'] = -col['Quantity']\n",
        "            entry_to_remove.append(index) \n",
        "            break\n",
        "\n",
        "stop = timeit.default_timer()\n",
        "\n",
        "print('Time: ', stop - start)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time:  139.15823313899998\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9xxX-Hp48VW"
      },
      "source": [
        "#df_cleaned.head(100)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fn1ONTKlFTwT"
      },
      "source": [
        "**Task**: evaluate lengths of `entry_to_remove` and `doubtfull_entry`. Delete corresponding rows from `df_cleaned`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0bxmdoeiFAty",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3668eb83-3f7d-4289-a86b-7549b268fd53"
      },
      "source": [
        "print(len(entry_to_remove), len(doubtfull_entry))\n",
        "df_cleaned.drop(entry_to_remove, axis = 0, inplace = True)\n",
        "df_cleaned.drop(doubtfull_entry, axis = 0, inplace = True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7521 1226\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7RH2yeKFW-w"
      },
      "source": [
        "**Task**: show the remaining cancellations not counting discounts:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ZO9NStsFZQy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        },
        "outputId": "1e5d12dd-0d17-4100-9972-bd6dc56d96f6"
      },
      "source": [
        "remaining_entries = df_cleaned[(df_cleaned['Quantity'] < 0) & (df_cleaned['StockCode'] != 'D')]\n",
        "print(\"nb of entries to delete: {}\".format(remaining_entries.shape[0]))\n",
        "remaining_entries[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "nb of entries to delete: 48\n"
          ],
          "name": "stdout"
        },
        {
          "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>Unnamed: 0</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>47994</th>\n",
              "      <td>77598</td>\n",
              "      <td>C542742</td>\n",
              "      <td>84535B</td>\n",
              "      <td>FAIRY CAKES NOTEBOOK A6 SIZE</td>\n",
              "      <td>-94</td>\n",
              "      <td>2011-01-31 16:26:00</td>\n",
              "      <td>0.65</td>\n",
              "      <td>15358</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57194</th>\n",
              "      <td>90444</td>\n",
              "      <td>C544038</td>\n",
              "      <td>22784</td>\n",
              "      <td>LANTERN CREAM GAZEBO</td>\n",
              "      <td>-4</td>\n",
              "      <td>2011-02-15 11:32:00</td>\n",
              "      <td>4.95</td>\n",
              "      <td>14659</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73515</th>\n",
              "      <td>111968</td>\n",
              "      <td>C545852</td>\n",
              "      <td>22464</td>\n",
              "      <td>HANGING METAL HEART LANTERN</td>\n",
              "      <td>-5</td>\n",
              "      <td>2011-03-07 13:49:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14048</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76454</th>\n",
              "      <td>116064</td>\n",
              "      <td>C546191</td>\n",
              "      <td>47566B</td>\n",
              "      <td>TEA TIME PARTY BUNTING</td>\n",
              "      <td>-35</td>\n",
              "      <td>2011-03-10 10:57:00</td>\n",
              "      <td>0.70</td>\n",
              "      <td>16422</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88876</th>\n",
              "      <td>132642</td>\n",
              "      <td>C547675</td>\n",
              "      <td>22263</td>\n",
              "      <td>FELT EGG COSY LADYBIRD</td>\n",
              "      <td>-49</td>\n",
              "      <td>2011-03-24 14:07:00</td>\n",
              "      <td>0.66</td>\n",
              "      <td>17754</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0 InvoiceNo  ...         Country QuantityCanceled\n",
              "47994       77598   C542742  ...  United Kingdom                0\n",
              "57194       90444   C544038  ...  United Kingdom                0\n",
              "73515      111968   C545852  ...  United Kingdom                0\n",
              "76454      116064   C546191  ...  United Kingdom                0\n",
              "88876      132642   C547675  ...  United Kingdom                0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8RTEhmQOFeVt"
      },
      "source": [
        "We can see that there is cases where the amount of cancelation is greater than the sum of previous purchases. However, we skip this and leave it like this."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XebkvVXJFgYJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 135
        },
        "outputId": "4f698051-c828-4ea1-e092-4425d53eff5e"
      },
      "source": [
        "df_cleaned[(df_cleaned['CustomerID'] == '14048') & (df_cleaned['StockCode'] == '22464')]\n",
        "#print(len(df_cleaned))"
      ],
      "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>Unnamed: 0</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52975</th>\n",
              "      <td>84247</td>\n",
              "      <td>543371</td>\n",
              "      <td>22464</td>\n",
              "      <td>HANGING METAL HEART LANTERN</td>\n",
              "      <td>3</td>\n",
              "      <td>2011-02-07 14:53:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14048</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52987</th>\n",
              "      <td>84259</td>\n",
              "      <td>543371</td>\n",
              "      <td>22464</td>\n",
              "      <td>HANGING METAL HEART LANTERN</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-02-07 14:53:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14048</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73515</th>\n",
              "      <td>111968</td>\n",
              "      <td>C545852</td>\n",
              "      <td>22464</td>\n",
              "      <td>HANGING METAL HEART LANTERN</td>\n",
              "      <td>-5</td>\n",
              "      <td>2011-03-07 13:49:00</td>\n",
              "      <td>1.65</td>\n",
              "      <td>14048</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Unnamed: 0 InvoiceNo  ...         Country QuantityCanceled\n",
              "52975       84247    543371  ...  United Kingdom                0\n",
              "52987       84259    543371  ...  United Kingdom                0\n",
              "73515      111968   C545852  ...  United Kingdom                0\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T0d5wtOE6AlX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "2906e39d-1f62-4f39-dc85-653b9819ae9b"
      },
      "source": [
        "len(df_cleaned)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "392857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehwfVjj6Fmb4"
      },
      "source": [
        "## 2.5) StockCode with letters\n",
        "\n",
        "As we have seen there are stockCode values which aren't numbers like 'D' for discount. \n",
        "Use regular expressions to investigate what other codes we have. \n",
        "Investigate dependence between these codes and `Description` feature."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XvBCi2ID-QK9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c5443d7-bb58-45ff-f375-c3b78d28bc1d"
      },
      "source": [
        "StockCodesAll = df_cleaned['StockCode'].unique()\n",
        "print(StockCodesAll[-100:-1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['23463' '23464' '23457' '23455' '23568' '23566' '23467' '23479' '23481'\n",
            " '23458' '23579' '23702' '23558' '23556' '23554' '23557' '23555' '23553'\n",
            " '23489' '23491' '23492' '23477' '23478' '23476' '23486' '23480' '23473'\n",
            " '23460' '23462' '23499' '23485' '90025F' '23570' '23454' '23571' '23569'\n",
            " '23305' '23474' '23582' '23583' '23581' '79157B' '23303' '23475' '23497'\n",
            " '23304' '23495' '23496' '47016' '23691' '90181A' '90199B' '90069'\n",
            " '90176A' '90176B' '23681' '72783' '90162A' '79161A' '23692' '23695'\n",
            " '23697' '90065B' '23598' '23597' '23664' '17191A' '35598C' '23694'\n",
            " '90083' '23442' '23449' '35598A' '23428' '84206B' '23430' '23429' '23441'\n",
            " '22125' '90130A' '90011A' '23440' '23552' '23498' '84550' '23578' '23580'\n",
            " '23575' '23560' '23576' '23562' '23561' '90214F' '90214O' '90214T'\n",
            " '90214U' '90214W' '90214Z' '90089']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jLvZU42WFvLc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "753df726-1165-4c17-c32b-62575e040fee"
      },
      "source": [
        "list_special_codes = df_cleaned[df_cleaned['StockCode'].str.contains('^[a-zA-Z]+', regex=True)]['StockCode'].unique()\n",
        "list_special_codes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['POST', 'D', 'C2', 'M', 'BANK CHARGES', 'PADS', 'DOT'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KpMBpFOEF2P8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2b5b0220-5fb8-49d4-b962-d009b88a45b8"
      },
      "source": [
        "for code in list_special_codes:\n",
        "    print(f\"{code} -> {df_cleaned[df_cleaned['StockCode'] == code]['Description'].unique()[0]}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "POST -> POSTAGE\n",
            "D -> Discount\n",
            "C2 -> CARRIAGE\n",
            "M -> Manual\n",
            "BANK CHARGES -> Bank Charges\n",
            "PADS -> PADS TO MATCH ALL CUSHIONS\n",
            "DOT -> DOTCOM POSTAGE\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kO_L9jvzF7HX"
      },
      "source": [
        "## 2.6) Basket prices\n",
        "\n",
        "**Task**: create a variable `TotalPrice` for the dataframe, which is the total price for each line. Use `UnitPrice`, `Quantity` and `QuantityCanceled` features for this"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2aiARTk0F7WH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "outputId": "2d96940e-a9b0-49bb-d9f8-ee0ca697ab46"
      },
      "source": [
        "# price for each line\n",
        "df_cleaned['TotalPrice'] = df_cleaned['UnitPrice'] * (df_cleaned['Quantity'] - df_cleaned['QuantityCanceled'])\n",
        "df_cleaned.sort_values('CustomerID')[: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>Unnamed: 0</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>37511</th>\n",
              "      <td>61619</td>\n",
              "      <td>541431</td>\n",
              "      <td>23166</td>\n",
              "      <td>MEDIUM CERAMIC TOP STORAGE JAR</td>\n",
              "      <td>74215</td>\n",
              "      <td>2011-01-18 10:01:00</td>\n",
              "      <td>1.04</td>\n",
              "      <td>12346</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>74215</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100693</th>\n",
              "      <td>148288</td>\n",
              "      <td>549222</td>\n",
              "      <td>22375</td>\n",
              "      <td>AIRLINE BAG VINTAGE JET SET BROWN</td>\n",
              "      <td>4</td>\n",
              "      <td>2011-04-07 10:43:00</td>\n",
              "      <td>4.25</td>\n",
              "      <td>12347</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>0</td>\n",
              "      <td>17.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318798</th>\n",
              "      <td>428971</td>\n",
              "      <td>573511</td>\n",
              "      <td>22698</td>\n",
              "      <td>PINK REGENCY TEACUP AND SAUCER</td>\n",
              "      <td>12</td>\n",
              "      <td>2011-10-31 12:25:00</td>\n",
              "      <td>2.95</td>\n",
              "      <td>12347</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>0</td>\n",
              "      <td>35.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318797</th>\n",
              "      <td>428970</td>\n",
              "      <td>573511</td>\n",
              "      <td>47559B</td>\n",
              "      <td>TEA TIME OVEN GLOVE</td>\n",
              "      <td>10</td>\n",
              "      <td>2011-10-31 12:25:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>12347</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>0</td>\n",
              "      <td>12.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318796</th>\n",
              "      <td>428969</td>\n",
              "      <td>573511</td>\n",
              "      <td>47567B</td>\n",
              "      <td>TEA TIME KITCHEN APRON</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-10-31 12:25:00</td>\n",
              "      <td>5.95</td>\n",
              "      <td>12347</td>\n",
              "      <td>Iceland</td>\n",
              "      <td>0</td>\n",
              "      <td>35.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Unnamed: 0 InvoiceNo  ... QuantityCanceled TotalPrice\n",
              "37511        61619    541431  ...            74215        0.0\n",
              "100693      148288    549222  ...                0       17.0\n",
              "318798      428971    573511  ...                0       35.4\n",
              "318797      428970    573511  ...                0       12.5\n",
              "318796      428969    573511  ...                0       35.7\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6vI6-IQk0rq"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuOHcJUQHw3w"
      },
      "source": [
        "Each entry of the dataframe indicates prizes for a single kind of product, so an order are split on several lines. We collect all the commands during a single order (same InvoiceNo) to recover the total order prize. \n",
        "\n",
        "**Task**: save result in a dataframe `basket_price`. It should have features: `CustomerID`, `InvoiceNo`, `Basket Price` and `InvoiceDate`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_tblaLhHsZm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "outputId": "187de5ea-9488-48ac-98ce-cc23e5cd2c79"
      },
      "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",
        "\n",
        "# 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",
        "\n",
        "# keep only positive purchases\n",
        "basket_price = basket_price[basket_price['Basket Price'] > 0]\n",
        "basket_price.sort_values('CustomerID')[:6]"
      ],
      "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>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>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>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>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>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>2011-08-02 08:48:00.000000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>12347</td>\n",
              "      <td>573511</td>\n",
              "      <td>1294.32</td>\n",
              "      <td>2011-10-31 12:25:00.000001280</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID InvoiceNo  Basket Price                   InvoiceDate\n",
              "1      12347    537626        711.79 2010-12-07 14:57:00.000001024\n",
              "2      12347    542237        475.39 2011-01-26 14:29:59.999999744\n",
              "3      12347    549222        636.25 2011-04-07 10:42:59.999999232\n",
              "4      12347    556201        382.52 2011-06-09 13:01:00.000000256\n",
              "5      12347    562032        584.91 2011-08-02 08:48:00.000000000\n",
              "6      12347    573511       1294.32 2011-10-31 12:25:00.000001280"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5Fo9cgfJlTQ"
      },
      "source": [
        "Purchases can be divided according to total prizes, run the code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dj8IOR31JlmC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "outputId": "60aa5181-c049-482f-e41d-1a300067bdb3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define price ranges\n",
        "price_range = [0, 50, 100, 200, 500, 1000, 5000, 50000]\n",
        "count_price = []\n",
        "for i, price in enumerate(price_range):\n",
        "    if i == 0: continue\n",
        "    val = basket_price[(basket_price['Basket Price'] < price) &\n",
        "                       (basket_price['Basket Price'] > price_range[i-1])]['Basket Price'].count()\n",
        "    count_price.append(val)\n",
        "\n",
        "# Distribution of order amounts    \n",
        "plt.rc('font', weight='bold')\n",
        "f, ax = plt.subplots(figsize=(11, 6))\n",
        "colors = ['yellowgreen', 'gold', 'wheat', 'c', 'violet', 'royalblue','firebrick']\n",
        "labels = [ '{}<.<{}'.format(price_range[i-1], s) for i,s in enumerate(price_range) if i != 0]\n",
        "sizes  = count_price\n",
        "explode = [0.0 if sizes[i] < 100 else 0.0 for i in range(len(sizes))]\n",
        "ax.pie(sizes, explode = explode, labels=labels, colors = colors,\n",
        "       autopct = lambda x:'{:1.0f}%'.format(x) if x > 1 else '',\n",
        "       shadow = False, startangle=0)\n",
        "ax.axis('equal')\n",
        "f.text(0.5, 1.01, \"Distribution of order amounts\", ha='center', fontsize = 18);\n",
        "\n",
        "# Distribution of order amounts    \n",
        "plt.rc('font', weight='bold')\n",
        "f, ax = plt.subplots(figsize=(11, 6))\n",
        "ax.hist(basket_price['Basket Price'][basket_price['Basket Price']<5000].values, bins=500)\n",
        "ax.axvline(x=basket_price['Basket Price'].mean(), color='k', linestyle='--', label='Average price')\n",
        "ax.legend()\n",
        "f.text(0.5, 1.01, \"Distribution of order amounts\", ha='center', fontsize = 18);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQMsACwlJq2J"
      },
      "source": [
        "## 2.7) Save data\n",
        "Save preprocessed data to the file `data_cleaned.csv` and `basket_price.csv`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyBebCWcJ1tC"
      },
      "source": [
        "df_cleaned.to_csv(relative_path + 'data_cleaned.csv')\n",
        "basket_price.to_csv(relative_path + 'basket_price.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLah8FQwI01M"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}