Task 1.ipynb

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      "name": "Task1.ipynb",
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      "source": [
        "#Task 1-Data Preparation and Customer Analytics\r\n",
        "Conduct analysis on your client's transaction dataset and identify customer purchasing behaviours to generate insights and provide commercial recommendations.\r\n",
        "\r\n",
        "**Background information for the task**\r\n",
        "\r\n",
        "We need to present a strategic recommendation to Julia that is supported by data which she can then use for the upcoming category review however to do so we need to analyse the data to understand the current purchasing trends and behaviours. The client is particularly interested in customer segments and their chip purchasing behaviour. Consider what metrics would help describe the customers’ purchasing behaviour. \r\n",
        "\r\n",
        "Main goals of this task are :\r\n",
        "\r\n",
        "\r\n",
        "1.   Examine transaction data - check for missing data, anomalies, outliers and clean them\r\n",
        "2.   Examine customer data - similar to above transaction data\r\n",
        "3.   Data analysis and customer segments - create charts and graphs, note trends and insights\r\n",
        "4.   Deep dive into customer segments - determine which segments should be targetted  \r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XFzNuqYNN0cZ"
      },
      "source": [
        "import pandas as pd\r\n",
        "import seaborn as sns\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "%matplotlib inline\r\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TH30yJksQeW5"
      },
      "source": [
        "tran_data = pd.read_excel(\"/content/QVI_transaction_data.xlsx\")"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "wthswvpFQeUQ",
        "outputId": "bfc80fb0-2f58-4099-ac3b-bf3bf3a9fb0a"
      },
      "source": [
        "tran_data.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_NAME</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>43390</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43599</td>\n",
              "      <td>1</td>\n",
              "      <td>1307</td>\n",
              "      <td>348</td>\n",
              "      <td>66</td>\n",
              "      <td>CCs Nacho Cheese    175g</td>\n",
              "      <td>3</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43605</td>\n",
              "      <td>1</td>\n",
              "      <td>1343</td>\n",
              "      <td>383</td>\n",
              "      <td>61</td>\n",
              "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
              "      <td>2</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43329</td>\n",
              "      <td>2</td>\n",
              "      <td>2373</td>\n",
              "      <td>974</td>\n",
              "      <td>69</td>\n",
              "      <td>Smiths Chip Thinly  S/Cream&amp;Onion 175g</td>\n",
              "      <td>5</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>43330</td>\n",
              "      <td>2</td>\n",
              "      <td>2426</td>\n",
              "      <td>1038</td>\n",
              "      <td>108</td>\n",
              "      <td>Kettle Tortilla ChpsHny&amp;Jlpno Chili 150g</td>\n",
              "      <td>3</td>\n",
              "      <td>13.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    DATE  STORE_NBR  ...  PROD_QTY  TOT_SALES\n",
              "0  43390          1  ...         2        6.0\n",
              "1  43599          1  ...         3        6.3\n",
              "2  43605          1  ...         2        2.9\n",
              "3  43329          2  ...         5       15.0\n",
              "4  43330          2  ...         3       13.8\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
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      "source": [
        "tran_data.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>264836.000000</td>\n",
              "      <td>264836.00000</td>\n",
              "      <td>2.648360e+05</td>\n",
              "      <td>2.648360e+05</td>\n",
              "      <td>264836.000000</td>\n",
              "      <td>264836.000000</td>\n",
              "      <td>264836.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>43464.036260</td>\n",
              "      <td>135.08011</td>\n",
              "      <td>1.355495e+05</td>\n",
              "      <td>1.351583e+05</td>\n",
              "      <td>56.583157</td>\n",
              "      <td>1.907309</td>\n",
              "      <td>7.304200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>105.389282</td>\n",
              "      <td>76.78418</td>\n",
              "      <td>8.057998e+04</td>\n",
              "      <td>7.813303e+04</td>\n",
              "      <td>32.826638</td>\n",
              "      <td>0.643654</td>\n",
              "      <td>3.083226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>43282.000000</td>\n",
              "      <td>1.00000</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>43373.000000</td>\n",
              "      <td>70.00000</td>\n",
              "      <td>7.002100e+04</td>\n",
              "      <td>6.760150e+04</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>43464.000000</td>\n",
              "      <td>130.00000</td>\n",
              "      <td>1.303575e+05</td>\n",
              "      <td>1.351375e+05</td>\n",
              "      <td>56.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>7.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43555.000000</td>\n",
              "      <td>203.00000</td>\n",
              "      <td>2.030942e+05</td>\n",
              "      <td>2.027012e+05</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>9.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>43646.000000</td>\n",
              "      <td>272.00000</td>\n",
              "      <td>2.373711e+06</td>\n",
              "      <td>2.415841e+06</td>\n",
              "      <td>114.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>650.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                DATE     STORE_NBR  ...       PROD_QTY      TOT_SALES\n",
              "count  264836.000000  264836.00000  ...  264836.000000  264836.000000\n",
              "mean    43464.036260     135.08011  ...       1.907309       7.304200\n",
              "std       105.389282      76.78418  ...       0.643654       3.083226\n",
              "min     43282.000000       1.00000  ...       1.000000       1.500000\n",
              "25%     43373.000000      70.00000  ...       2.000000       5.400000\n",
              "50%     43464.000000     130.00000  ...       2.000000       7.400000\n",
              "75%     43555.000000     203.00000  ...       2.000000       9.200000\n",
              "max     43646.000000     272.00000  ...     200.000000     650.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NyE7_gGQeNt"
      },
      "source": [
        "pur_bvr = pd.read_csv(\"/content/QVI_purchase_behaviour.csv\")"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "0clUUZTYQeLT",
        "outputId": "31f94231-8e4e-4492-c6a5-df69a6309344"
      },
      "source": [
        "pur_bvr.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1002</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1003</td>\n",
              "      <td>YOUNG FAMILIES</td>\n",
              "      <td>Budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1004</td>\n",
              "      <td>OLDER SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1005</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   LYLTY_CARD_NBR               LIFESTAGE PREMIUM_CUSTOMER\n",
              "0            1000   YOUNG SINGLES/COUPLES          Premium\n",
              "1            1002   YOUNG SINGLES/COUPLES       Mainstream\n",
              "2            1003          YOUNG FAMILIES           Budget\n",
              "3            1004   OLDER SINGLES/COUPLES       Mainstream\n",
              "4            1005  MIDAGE SINGLES/COUPLES       Mainstream"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "UmtrwhlMQeIR",
        "outputId": "508e9fa3-3e63-429b-e6ca-d98101756b0e"
      },
      "source": [
        "pur_bvr.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7.263700e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.361859e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.989293e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>6.620200e+04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.340400e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.033750e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.373711e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       LYLTY_CARD_NBR\n",
              "count    7.263700e+04\n",
              "mean     1.361859e+05\n",
              "std      8.989293e+04\n",
              "min      1.000000e+03\n",
              "25%      6.620200e+04\n",
              "50%      1.340400e+05\n",
              "75%      2.033750e+05\n",
              "max      2.373711e+06"
            ]
          },
          "metadata": {
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          "execution_count": 16
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
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        },
        "id": "p84qCmbEQeFk",
        "outputId": "cd6e05c3-2bb4-430c-ead6-6c9c6b22211e"
      },
      "source": [
        "tran_data.isnull().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DATE              0\n",
              "STORE_NBR         0\n",
              "LYLTY_CARD_NBR    0\n",
              "TXN_ID            0\n",
              "PROD_NBR          0\n",
              "PROD_NAME         0\n",
              "PROD_QTY          0\n",
              "TOT_SALES         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "387pkv2zQd-2",
        "outputId": "1ba7e737-1ab3-40fc-b968-c13cf3eaab1a"
      },
      "source": [
        "pur_bvr.isnull().sum()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LYLTY_CARD_NBR      0\n",
              "LIFESTAGE           0\n",
              "PREMIUM_CUSTOMER    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_SK-x_5U6uC"
      },
      "source": [
        "**Checking and Removing Outliers**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 196
        },
        "id": "9LFCNmsjQdaU",
        "outputId": "fffdf695-4059-491a-a4e0-082ac8c0a9e9"
      },
      "source": [
        "merged_data = pd.merge(pur_bvr, tran_data, on = 'LYLTY_CARD_NBR', how = 'right')\r\n",
        "merged_data.head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_NAME</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1000</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>43390</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>Natural Chip        Compny SeaSalt175g</td>\n",
              "      <td>2</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1307</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>43599</td>\n",
              "      <td>1</td>\n",
              "      <td>348</td>\n",
              "      <td>66</td>\n",
              "      <td>CCs Nacho Cheese    175g</td>\n",
              "      <td>3</td>\n",
              "      <td>6.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1307</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>43414</td>\n",
              "      <td>1</td>\n",
              "      <td>346</td>\n",
              "      <td>96</td>\n",
              "      <td>WW Original Stacked Chips 160g</td>\n",
              "      <td>2</td>\n",
              "      <td>3.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1307</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>43533</td>\n",
              "      <td>1</td>\n",
              "      <td>347</td>\n",
              "      <td>54</td>\n",
              "      <td>CCs Original 175g</td>\n",
              "      <td>1</td>\n",
              "      <td>2.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1343</td>\n",
              "      <td>MIDAGE SINGLES/COUPLES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>43605</td>\n",
              "      <td>1</td>\n",
              "      <td>383</td>\n",
              "      <td>61</td>\n",
              "      <td>Smiths Crinkle Cut  Chips Chicken 170g</td>\n",
              "      <td>2</td>\n",
              "      <td>2.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   LYLTY_CARD_NBR               LIFESTAGE  ... PROD_QTY  TOT_SALES\n",
              "0            1000   YOUNG SINGLES/COUPLES  ...        2        6.0\n",
              "1            1307  MIDAGE SINGLES/COUPLES  ...        3        6.3\n",
              "2            1307  MIDAGE SINGLES/COUPLES  ...        2        3.8\n",
              "3            1307  MIDAGE SINGLES/COUPLES  ...        1        2.1\n",
              "4            1343  MIDAGE SINGLES/COUPLES  ...        2        2.9\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4MAZYDHLX_FG",
        "outputId": "d86c457d-d886-4ccd-8991-252f6f92b003",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(len(merged_data))\r\n",
        "print(len(tran_data))"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "264836\n",
            "264836\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaVqgYiKYJLu",
        "outputId": "196d6def-d732-452c-b713-deb4599c2c79",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data.info()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 264836 entries, 0 to 264835\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count   Dtype  \n",
            "---  ------            --------------   -----  \n",
            " 0   LYLTY_CARD_NBR    264836 non-null  int64  \n",
            " 1   LIFESTAGE         264836 non-null  object \n",
            " 2   PREMIUM_CUSTOMER  264836 non-null  object \n",
            " 3   DATE              264836 non-null  int64  \n",
            " 4   STORE_NBR         264836 non-null  int64  \n",
            " 5   TXN_ID            264836 non-null  int64  \n",
            " 6   PROD_NBR          264836 non-null  int64  \n",
            " 7   PROD_NAME         264836 non-null  object \n",
            " 8   PROD_QTY          264836 non-null  int64  \n",
            " 9   TOT_SALES         264836 non-null  float64\n",
            "dtypes: float64(1), int64(6), object(3)\n",
            "memory usage: 22.2+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Rz_nxntYPXL"
      },
      "source": [
        "**Date column should be data time format**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e84MafEPYJIN"
      },
      "source": [
        "from datetime import date, timedelta\r\n",
        "start = date(1899, 12, 30)\r\n",
        "new_date_format = []\r\n",
        "for date in merged_data[\"DATE\"]:\r\n",
        "  delta = timedelta(date)\r\n",
        "  new_date_format.append(start + delta)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSdNqiiEYJFV",
        "outputId": "4c9e598a-27ee-4513-dddd-f44906c492a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data[\"DATE\"] = pd.to_datetime(pd.Series(new_date_format))\r\n",
        "print(merged_data[\"DATE\"].dtype)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "datetime64[ns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMGMahfjY-os"
      },
      "source": [
        "**Checking the product name column to make sure all items are chips**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWSASNp-YJCj",
        "outputId": "5e187b30-5cb3-40c5-bfaf-5b0021f95cd1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data[\"PROD_NAME\"].unique()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Natural Chip        Compny SeaSalt175g',\n",
              "       'CCs Nacho Cheese    175g', 'WW Original Stacked Chips 160g',\n",
              "       'CCs Original 175g', 'Smiths Crinkle Cut  Chips Chicken 170g',\n",
              "       'Smiths Chip Thinly  S/Cream&Onion 175g',\n",
              "       'Kettle Tortilla ChpsHny&Jlpno Chili 150g',\n",
              "       'Old El Paso Salsa   Dip Tomato Mild 300g',\n",
              "       'Tyrrells Crisps     Lightly Salted 165g',\n",
              "       'Kettle Tortilla ChpsFeta&Garlic 150g',\n",
              "       'Dorito Corn Chp     Supreme 380g', 'Doritos Mexicana    170g',\n",
              "       'Smiths Crinkle Chips Salt & Vinegar 330g', 'Kettle Original 175g',\n",
              "       'Tyrrells Crisps     Ched & Chives 165g',\n",
              "       'Infuzions BBQ Rib   Prawn Crackers 110g',\n",
              "       'Grain Waves         Sweet Chilli 210g',\n",
              "       'Old El Paso Salsa   Dip Tomato Med 300g',\n",
              "       'Doritos Corn Chip Southern Chicken 150g',\n",
              "       'Thins Potato Chips  Hot & Spicy 175g',\n",
              "       'Doritos Corn Chip Mexican Jalapeno 150g',\n",
              "       'Red Rock Deli Thai  Chilli&Lime 150g',\n",
              "       'GrnWves Plus Btroot & Chilli Jam 180g',\n",
              "       'WW D/Style Chip     Sea Salt 200g',\n",
              "       'Thins Chips Light&  Tangy 175g',\n",
              "       'Grain Waves Sour    Cream&Chives 210G',\n",
              "       'Tostitos Smoked     Chipotle 175g',\n",
              "       'Infuzions Thai SweetChili PotatoMix 110g',\n",
              "       'Kettle Sensations   Siracha Lime 150g',\n",
              "       'Pringles Chicken    Salt Crips 134g',\n",
              "       'Thins Chips Salt &  Vinegar 175g',\n",
              "       'Cobs Popd Swt/Chlli &Sr/Cream Chips 110g',\n",
              "       'Twisties Cheese     270g', 'WW Crinkle Cut      Chicken 175g',\n",
              "       'RRD Sweet Chilli &  Sour Cream 165g',\n",
              "       'Doritos Corn Chips  Cheese Supreme 170g',\n",
              "       'RRD Salt & Vinegar  165g', 'Doritos Corn Chips  Original 170g',\n",
              "       'Smiths Crinkle      Original 330g',\n",
              "       'Infzns Crn Crnchers Tangy Gcamole 110g',\n",
              "       'Kettle Sea Salt     And Vinegar 175g',\n",
              "       'Red Rock Deli Chikn&Garlic Aioli 150g',\n",
              "       'RRD SR Slow Rst     Pork Belly 150g', 'Burger Rings 220g',\n",
              "       'NCC Sour Cream &    Garden Chives 175g',\n",
              "       'Smiths Crinkle Cut  French OnionDip 150g',\n",
              "       'Natural ChipCo Sea  Salt & Vinegr 175g',\n",
              "       'Cheezels Cheese Box 125g', 'CCs Tasty Cheese    175g',\n",
              "       'Smith Crinkle Cut   Bolognese 150g', 'Pringles Slt Vingar 134g',\n",
              "       'WW Sour Cream &OnionStacked Chips 160g',\n",
              "       'Doritos Salsa Mild  300g', 'Pringles Original   Crisps 134g',\n",
              "       'Pringles Sthrn FriedChicken 134g',\n",
              "       'Pringles SourCream  Onion 134g',\n",
              "       'Smiths Crinkle Cut  Chips Barbecue 170g',\n",
              "       'Infuzions Mango     Chutny Papadums 70g',\n",
              "       'RRD Pc Sea Salt     165g', 'Doritos Salsa       Medium 300g',\n",
              "       'Old El Paso Salsa   Dip Chnky Tom Ht300g',\n",
              "       'Smiths Chip Thinly  Cut Original 175g', 'Twisties Chicken270g',\n",
              "       'Smiths Crinkle Cut  Tomato Salsa 150g',\n",
              "       'Kettle 135g Swt Pot Sea Salt',\n",
              "       'Natural ChipCo      Hony Soy Chckn175g',\n",
              "       'Kettle Sweet Chilli And Sour Cream 175g',\n",
              "       'WW Supreme Cheese   Corn Chips 200g',\n",
              "       'WW Original Corn    Chips 200g',\n",
              "       'Cobs Popd Sour Crm  &Chives Chips 110g',\n",
              "       'Pringles Sweet&Spcy BBQ 134g', 'Doritos Cheese      Supreme 330g',\n",
              "       'Red Rock Deli SR    Salsa & Mzzrlla 150g',\n",
              "       'Pringles Mystery    Flavour 134g',\n",
              "       'Thins Chips         Originl saltd 175g',\n",
              "       'Smiths Thinly Cut   Roast Chicken 175g',\n",
              "       'Kettle Mozzarella   Basil & Pesto 175g',\n",
              "       'Smiths Crinkle Cut  Salt & Vinegar 170g',\n",
              "       'Red Rock Deli Sp    Salt & Truffle 150G',\n",
              "       'Smiths Thinly       Swt Chli&S/Cream175G', 'Kettle Chilli 175g',\n",
              "       'Kettle Honey Soy    Chicken 175g', 'Pringles Barbeque   134g',\n",
              "       'Kettle Sensations   BBQ&Maple 150g',\n",
              "       'RRD Steak &         Chimuchurri 150g',\n",
              "       'Doritos Corn Chips  Nacho Cheese 170g',\n",
              "       'Tostitos Splash Of  Lime 175g',\n",
              "       'Kettle Sensations   Camembert & Fig 150g',\n",
              "       'Cobs Popd Sea Salt  Chips 110g',\n",
              "       'Smith Crinkle Cut   Mac N Cheese 150g',\n",
              "       'Woolworths Mild     Salsa 300g',\n",
              "       'Smiths Crinkle Cut  Snag&Sauce 150g',\n",
              "       'Thins Chips Seasonedchicken 175g',\n",
              "       'Woolworths Medium   Salsa 300g',\n",
              "       'Kettle Tortilla ChpsBtroot&Ricotta 150g',\n",
              "       'Infuzions SourCream&Herbs Veg Strws 110g', 'Cheezels Cheese 330g',\n",
              "       'RRD Chilli&         Coconut 150g',\n",
              "       'Smiths Crinkle Cut  Chips Chs&Onion170g',\n",
              "       'Smiths Chip Thinly  CutSalt/Vinegr175g',\n",
              "       'Twisties Cheese     Burger 250g', 'RRD Lime & Pepper   165g',\n",
              "       'RRD Honey Soy       Chicken 165g',\n",
              "       'French Fries Potato Chips 175g',\n",
              "       'Natural Chip Co     Tmato Hrb&Spce 175g',\n",
              "       'Sunbites Whlegrn    Crisps Frch/Onin 90g', 'Cheetos Puffs 165g',\n",
              "       'Smiths Crinkle Cut  Chips Original 170g',\n",
              "       'Tostitos Lightly    Salted 175g',\n",
              "       'Woolworths Cheese   Rings 190g',\n",
              "       'Smiths Crnkle Chip  Orgnl Big Bag 380g',\n",
              "       'Snbts Whlgrn Crisps Cheddr&Mstrd 90g',\n",
              "       'WW Crinkle Cut      Original 175g',\n",
              "       'Cheetos Chs & Bacon Balls 190g'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hnQlfnYAYJAA"
      },
      "source": [
        "split_prods = merged_data[\"PROD_NAME\"].str.replace(r'([0-9]+[gG])','').str.replace(r'[^\\w]',' ').str.split()"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ErviFcyTYI9W",
        "outputId": "f266de4a-ae65-4e23-c67a-8e4459d76eea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "word_counts = {}\r\n",
        "def count_words(line):\r\n",
        "  for word in line:\r\n",
        "    if word not in word_counts:\r\n",
        "      word_counts[word] = 1\r\n",
        "    else:\r\n",
        "      word_counts[word] += 1\r\n",
        "split_prods.apply(lambda line: count_words(line))\r\n",
        "print(pd.Series(word_counts).sort_values(ascending = False))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Chips     49770\n",
            "Kettle    41288\n",
            "Smiths    28860\n",
            "Salt      27976\n",
            "Cheese    27890\n",
            "          ...  \n",
            "Onin       1432\n",
            "Pc         1431\n",
            "Garden     1419\n",
            "NCC        1419\n",
            "Fries      1418\n",
            "Length: 198, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uote108UYI6M",
        "outputId": "88a4eda9-0096-4904-a95e-0f539a749a53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "print(merged_data.describe(), '\\n')\r\n",
        "print(merged_data.info())"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "       LYLTY_CARD_NBR     STORE_NBR  ...       PROD_QTY      TOT_SALES\n",
            "count    2.648360e+05  264836.00000  ...  264836.000000  264836.000000\n",
            "mean     1.355495e+05     135.08011  ...       1.907309       7.304200\n",
            "std      8.057998e+04      76.78418  ...       0.643654       3.083226\n",
            "min      1.000000e+03       1.00000  ...       1.000000       1.500000\n",
            "25%      7.002100e+04      70.00000  ...       2.000000       5.400000\n",
            "50%      1.303575e+05     130.00000  ...       2.000000       7.400000\n",
            "75%      2.030942e+05     203.00000  ...       2.000000       9.200000\n",
            "max      2.373711e+06     272.00000  ...     200.000000     650.000000\n",
            "\n",
            "[8 rows x 6 columns] \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 264836 entries, 0 to 264835\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count   Dtype         \n",
            "---  ------            --------------   -----         \n",
            " 0   LYLTY_CARD_NBR    264836 non-null  int64         \n",
            " 1   LIFESTAGE         264836 non-null  object        \n",
            " 2   PREMIUM_CUSTOMER  264836 non-null  object        \n",
            " 3   DATE              264836 non-null  datetime64[ns]\n",
            " 4   STORE_NBR         264836 non-null  int64         \n",
            " 5   TXN_ID            264836 non-null  int64         \n",
            " 6   PROD_NBR          264836 non-null  int64         \n",
            " 7   PROD_NAME         264836 non-null  object        \n",
            " 8   PROD_QTY          264836 non-null  int64         \n",
            " 9   TOT_SALES         264836 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(1), int64(5), object(3)\n",
            "memory usage: 22.2+ MB\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W1fVB_asaG9_",
        "outputId": "c6316720-cf72-461b-ce58-e5e1bcff8de7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data[\"PROD_QTY\"].value_counts(bins=4).sort_index()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.8, 50.75]       264834\n",
              "(50.75, 100.5]          0\n",
              "(100.5, 150.25]         0\n",
              "(150.25, 200.0]         2\n",
              "Name: PROD_QTY, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7fW_v8CVajEz"
      },
      "source": [
        "**From above binning we see that PROD_QTY values above 50.75**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JJ88GWBbaG6o",
        "outputId": "c5f471b8-e77e-4662-e195-4b00e68233c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "merged_data.sort_values(by=\"PROD_QTY\", ascending=False).head()"
      ],
      "execution_count": 37,
      "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>LYLTY_CARD_NBR</th>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th>DATE</th>\n",
              "      <th>STORE_NBR</th>\n",
              "      <th>TXN_ID</th>\n",
              "      <th>PROD_NBR</th>\n",
              "      <th>PROD_NAME</th>\n",
              "      <th>PROD_QTY</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>71456</th>\n",
              "      <td>226000</td>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>2018-08-19</td>\n",
              "      <td>226</td>\n",
              "      <td>226201</td>\n",
              "      <td>4</td>\n",
              "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
              "      <td>200</td>\n",
              "      <td>650.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71457</th>\n",
              "      <td>226000</td>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>2019-05-20</td>\n",
              "      <td>226</td>\n",
              "      <td>226210</td>\n",
              "      <td>4</td>\n",
              "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
              "      <td>200</td>\n",
              "      <td>650.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34105</th>\n",
              "      <td>97211</td>\n",
              "      <td>OLDER FAMILIES</td>\n",
              "      <td>Budget</td>\n",
              "      <td>2018-08-15</td>\n",
              "      <td>97</td>\n",
              "      <td>97600</td>\n",
              "      <td>11</td>\n",
              "      <td>RRD Pc Sea Salt     165g</td>\n",
              "      <td>5</td>\n",
              "      <td>15.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102451</th>\n",
              "      <td>235164</td>\n",
              "      <td>OLDER SINGLES/COUPLES</td>\n",
              "      <td>Mainstream</td>\n",
              "      <td>2018-08-17</td>\n",
              "      <td>235</td>\n",
              "      <td>238117</td>\n",
              "      <td>4</td>\n",
              "      <td>Dorito Corn Chp     Supreme 380g</td>\n",
              "      <td>5</td>\n",
              "      <td>16.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>259068</th>\n",
              "      <td>183032</td>\n",
              "      <td>YOUNG SINGLES/COUPLES</td>\n",
              "      <td>Premium</td>\n",
              "      <td>2019-05-14</td>\n",
              "      <td>183</td>\n",
              "      <td>184969</td>\n",
              "      <td>21</td>\n",
              "      <td>WW Sour Cream &amp;OnionStacked Chips 160g</td>\n",
              "      <td>5</td>\n",
              "      <td>9.50</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        LYLTY_CARD_NBR              LIFESTAGE  ... PROD_QTY TOT_SALES\n",
              "71456           226000         OLDER FAMILIES  ...      200    650.00\n",
              "71457           226000         OLDER FAMILIES  ...      200    650.00\n",
              "34105            97211         OLDER FAMILIES  ...        5     15.00\n",
              "102451          235164  OLDER SINGLES/COUPLES  ...        5     16.25\n",
              "259068          183032  YOUNG SINGLES/COUPLES  ...        5      9.50\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YoFUApbbBXN"
      },
      "source": [
        "**Two outliers of value 200 in PROD_QTY will be removed. Both entries are by the same customer and will be examined by this customer's transactions**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fGtQo5M8aG3h"
      },
      "source": [
        "merged_data = merged_data[merged_data[\"PROD_QTY\"] < 6]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-LAYbj_aG0u",
        "outputId": "4f1dfd24-a178-40ca-a6d6-57fbc8c4c5a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(merged_data[merged_data[\"LYLTY_CARD_NBR\"]==226000])"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wbUuxocaGye",
        "outputId": "064110b3-3eee-46db-8a3f-86459141e9be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data[\"DATE\"].describe()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                  264834\n",
              "unique                    364\n",
              "top       2018-12-24 00:00:00\n",
              "freq                      939\n",
              "first     2018-07-01 00:00:00\n",
              "last      2019-06-30 00:00:00\n",
              "Name: DATE, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8gjPhoabu2n"
      },
      "source": [
        "**There are 365 days in a year but in the DATE column there are only 364 unique values so one is missing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lMdfxT-vaGuc",
        "outputId": "06e6213c-96d1-48da-f3a0-7023c17b381d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pd.date_range(start=merged_data[\"DATE\"].min(), end=merged_data[\"DATE\"].max()).difference(merged_data[\"DATE\"])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2018-12-25'], dtype='datetime64[ns]', freq=None)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPK_c7OKcGeU"
      },
      "source": [
        "**Using the difference method we see that 2018-12-25 was a missing date**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTLJWWexcFIO"
      },
      "source": [
        "check_null_date = pd.merge(pd.Series(pd.date_range(start=merged_data[\"DATE\"].min(), end = merged_data[\"DATE\"].max()), name=\"DATE\"), merged_data, on = \"DATE\", how = \"left\")"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYEbsyzlcE8P",
        "outputId": "2c41bba0-d871-4e6b-935e-41c660ff3a82",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        }
      },
      "source": [
        "trans_by_date = check_null_date[\"DATE\"].value_counts()\r\n",
        "dec = trans_by_date[(trans_by_date.index >= pd.datetime(2018,12,1)) & (trans_by_date.index < pd.datetime(2019,1,1))].sort_index()\r\n",
        "dec.index = dec.index.strftime('%d')\r\n",
        "ax = dec.plot(figsize=(15,3))\r\n",
        "ax.set_xticks(np.arange(len(dec)))\r\n",
        "ax.set_xticklabels(dec.index)\r\n",
        "plt.title(\"2018 December Sales\")\r\n",
        "plt.savefig(\"2018 December Sales.png\", bbox_inches=\"tight\")\r\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VpdTBjdEcE5R",
        "outputId": "f582845c-e72d-400f-8520-0c1090b990be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "check_null_date[\"DATE\"].value_counts().sort_values().head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2018-12-25      1\n",
              "2018-11-25    648\n",
              "2018-10-18    658\n",
              "2019-06-13    659\n",
              "2019-06-24    662\n",
              "Name: DATE, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6gG4gUfIdfc5"
      },
      "source": [
        "**The day with no transaction is a Christmas day that is when the store is closed. So there is no anomaly in this.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_8-yDpjduRO"
      },
      "source": [
        "**Explore Packet sizes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xr4FQcvOcE1t",
        "outputId": "d067ddee-1bd5-405f-bb73-5910fb00582f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        }
      },
      "source": [
        "merged_data[\"PROD_NAME\"] = merged_data[\"PROD_NAME\"].str.replace(r'[0-9]+(G)','g')\r\n",
        "pack_sizes = merged_data[\"PROD_NAME\"].str.extract(r'([0-9]+[gG])')[0].str.replace(\"g\",\"\").astype(\"float\")\r\n",
        "print(pack_sizes.describe())\r\n",
        "pack_sizes.plot.hist()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    258770.000000\n",
            "mean        182.324276\n",
            "std          64.955035\n",
            "min          70.000000\n",
            "25%         150.000000\n",
            "50%         170.000000\n",
            "75%         175.000000\n",
            "max         380.000000\n",
            "Name: 0, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe9b157b00>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXPiNg-NcEy8",
        "outputId": "3b198f50-b4e2-4ceb-a5cc-40730723c14a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data[\"PROD_NAME\"].str.split().str[0].value_counts().sort_index()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Burger         1564\n",
              "CCs            4551\n",
              "Cheetos        2927\n",
              "Cheezels       4603\n",
              "Cobs           9693\n",
              "Dorito         3183\n",
              "Doritos       24962\n",
              "French         1418\n",
              "Grain          6272\n",
              "GrnWves        1468\n",
              "Infuzions     11057\n",
              "Infzns         3144\n",
              "Kettle        41288\n",
              "NCC            1419\n",
              "Natural        6050\n",
              "Old            9324\n",
              "Pringles      25102\n",
              "RRD           11894\n",
              "Red            5885\n",
              "Smith          2963\n",
              "Smiths        28860\n",
              "Snbts          1576\n",
              "Sunbites       1432\n",
              "Thins         14075\n",
              "Tostitos       9471\n",
              "Twisties       9454\n",
              "Tyrrells       6442\n",
              "WW            10320\n",
              "Woolworths     4437\n",
              "Name: PROD_NAME, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EKIZemByhJuU"
      },
      "source": [
        "Some product names are written in more than one way. Example : Dorito and Doritos, Grains and GrnWves, Infusions and Ifzns, Natural and NCC, Red and RRD, Smith and Smiths and Snbts and Sunbites."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wqh8mLlcEwX",
        "outputId": "1fdab89a-d9c2-48e9-9d11-904a9302fad0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "merged_data[\"PROD_NAME\"].str.split()[merged_data[\"PROD_NAME\"].str.split().str[0] == \"Red\"].value_counts()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
          ]
        },
        {
          "output_type": "stream",
          "text": [
            "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
            "Traceback (most recent call last):\n",
            "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 1709, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
            "TypeError: unhashable type: 'list'\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Red, Rock, Deli, Sp, Salt, &, Truffle, g]        1498\n",
              "[Red, Rock, Deli, Thai, Chilli&Lime, 150g]        1495\n",
              "[Red, Rock, Deli, SR, Salsa, &, Mzzrlla, 150g]    1458\n",
              "[Red, Rock, Deli, Chikn&Garlic, Aioli, 150g]      1434\n",
              "Name: PROD_NAME, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFNuBXBncEt2"
      },
      "source": [
        "merged_data[\"Cleaned_Brand_Names\"] = merged_data[\"PROD_NAME\"].str.split().str[0]"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IguRhNLIcEqc"
      },
      "source": [
        "def clean_brand_names(line):\r\n",
        "    brand = line[\"Cleaned_Brand_Names\"]\r\n",
        "    if brand == \"Dorito\":\r\n",
        "        return \"Doritos\"\r\n",
        "    elif brand == \"GrnWves\" or brand == \"Grain\":\r\n",
        "        return \"Grain Waves\"\r\n",
        "    elif brand == \"Infzns\":\r\n",
        "        return \"Infuzions\"\r\n",
        "    elif brand == \"Natural\" or brand == \"NCC\":\r\n",
        "        return \"Natural Chip Co\"\r\n",
        "    elif brand == \"Red\":\r\n",
        "        return \"RRD\"\r\n",
        "    elif brand == \"Smith\":\r\n",
        "        return \"Smiths\"\r\n",
        "    elif brand == \"Snbts\":\r\n",
        "        return \"Sunbites\"\r\n",
        "    elif brand == \"WW\":\r\n",
        "        return \"Woolworths\"\r\n",
        "    else:\r\n",
        "        return brand"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Rs5p2V3cEnw"
      },
      "source": [
        "merged_data[\"Cleaned_Brand_Names\"] = merged_data.apply(lambda line: clean_brand_names(line), axis=1)"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZlJgZlHcEky",
        "outputId": "29a63110-1a1c-470d-ea3d-14db840caadb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "merged_data[\"Cleaned_Brand_Names\"].value_counts(ascending=True).plot.barh(figsize=(10,5))"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe8d7b9c18>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fBdrc5ZIcEhD",
        "outputId": "fc3b05d6-30c6-41ff-d7e2-ab1abd74603a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data.isnull().sum()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LYLTY_CARD_NBR         0\n",
              "LIFESTAGE              0\n",
              "PREMIUM_CUSTOMER       0\n",
              "DATE                   0\n",
              "STORE_NBR              0\n",
              "TXN_ID                 0\n",
              "PROD_NBR               0\n",
              "PROD_NAME              0\n",
              "PROD_QTY               0\n",
              "TOT_SALES              0\n",
              "Cleaned_Brand_Names    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D8-XdWXAla12"
      },
      "source": [
        "1. Who spends the most on chips (total sales), describing customers by lifestage and how premium their general purchasing behaviour is\r\n",
        "2.How many customers are in each segment\r\n",
        "3. How many chips are bought per customer by segment\r\n",
        "4. What's the average chip price by customer segment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPLt0LIAlbTe",
        "outputId": "6bea9959-b278-450a-d43b-e9bf925247fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        }
      },
      "source": [
        "grouped_sales = pd.DataFrame(merged_data.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"TOT_SALES\"].agg([\"sum\", \"mean\"]))\r\n",
        "grouped_sales.sort_values(ascending=False, by=\"sum\")"
      ],
      "execution_count": 54,
      "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></th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>168363.25</td>\n",
              "      <td>7.269570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>157621.60</td>\n",
              "      <td>7.558339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>155677.05</td>\n",
              "      <td>7.252262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>139345.85</td>\n",
              "      <td>7.287201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>136769.80</td>\n",
              "      <td>7.430315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mainstream</th>\n",
              "      <td>133393.80</td>\n",
              "      <td>7.282116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>132263.15</td>\n",
              "      <td>7.449766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>113147.80</td>\n",
              "      <td>7.443445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>103445.55</td>\n",
              "      <td>7.262395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>97646.05</td>\n",
              "      <td>7.456174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>92788.75</td>\n",
              "      <td>7.189025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>90803.85</td>\n",
              "      <td>7.647284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>84025.50</td>\n",
              "      <td>7.266756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>80658.40</td>\n",
              "      <td>7.208079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>61141.60</td>\n",
              "      <td>6.615624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>58432.65</td>\n",
              "      <td>7.112056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>41642.10</td>\n",
              "      <td>6.629852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>35514.80</td>\n",
              "      <td>7.074661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>21928.45</td>\n",
              "      <td>7.297321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mainstream</th>\n",
              "      <td>17013.90</td>\n",
              "      <td>7.317806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>11491.10</td>\n",
              "      <td>7.231655</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               sum      mean\n",
              "LIFESTAGE              PREMIUM_CUSTOMER                     \n",
              "OLDER FAMILIES         Budget            168363.25  7.269570\n",
              "YOUNG SINGLES/COUPLES  Mainstream        157621.60  7.558339\n",
              "RETIREES               Mainstream        155677.05  7.252262\n",
              "YOUNG FAMILIES         Budget            139345.85  7.287201\n",
              "OLDER SINGLES/COUPLES  Budget            136769.80  7.430315\n",
              "                       Mainstream        133393.80  7.282116\n",
              "                       Premium           132263.15  7.449766\n",
              "RETIREES               Budget            113147.80  7.443445\n",
              "OLDER FAMILIES         Mainstream        103445.55  7.262395\n",
              "RETIREES               Premium            97646.05  7.456174\n",
              "YOUNG FAMILIES         Mainstream         92788.75  7.189025\n",
              "MIDAGE SINGLES/COUPLES Mainstream         90803.85  7.647284\n",
              "YOUNG FAMILIES         Premium            84025.50  7.266756\n",
              "OLDER FAMILIES         Premium            80658.40  7.208079\n",
              "YOUNG SINGLES/COUPLES  Budget             61141.60  6.615624\n",
              "MIDAGE SINGLES/COUPLES Premium            58432.65  7.112056\n",
              "YOUNG SINGLES/COUPLES  Premium            41642.10  6.629852\n",
              "MIDAGE SINGLES/COUPLES Budget             35514.80  7.074661\n",
              "NEW FAMILIES           Budget             21928.45  7.297321\n",
              "                       Mainstream         17013.90  7.317806\n",
              "                       Premium            11491.10  7.231655"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UWtFfvqFlcPE",
        "outputId": "5cbf5073-8570-416e-a40f-14ce12b7e299",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "grouped_sales[\"sum\"].sum()"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1933114.9999999765"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TI8rAJwlcLE",
        "outputId": "3ee958b9-42ba-4ae6-88aa-ffc9b330bdfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "grouped_sales[\"sum\"].sort_values().plot.barh(figsize=(12,7))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe933a5438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3YErRCvlcIi",
        "outputId": "5aa3e363-4afb-4638-bd67-41d3a7bb427c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        }
      },
      "source": [
        "# Values of each group\r\n",
        "bars1 = grouped_sales[grouped_sales.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Budget\"][\"sum\"]\r\n",
        "bars2 = grouped_sales[grouped_sales.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Mainstream\"][\"sum\"]\r\n",
        "bars3 = grouped_sales[grouped_sales.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Premium\"][\"sum\"]\r\n",
        " \r\n",
        "bars1_text = (bars1 / sum(grouped_sales[\"sum\"])).apply(\"{:.1%}\".format)\r\n",
        "bars2_text = (bars2 / sum(grouped_sales[\"sum\"])).apply(\"{:.1%}\".format)\r\n",
        "bars3_text = (bars3 / sum(grouped_sales[\"sum\"])).apply(\"{:.1%}\".format)\r\n",
        " \r\n",
        "# Names of group and bar width\r\n",
        "names = grouped_sales.index.get_level_values(\"LIFESTAGE\").unique()\r\n",
        "\r\n",
        "# The position of the bars on the x-axis\r\n",
        "r = np.arange(len(names))\r\n",
        "\r\n",
        "plt.figure(figsize=(13,5)) \r\n",
        "\r\n",
        "# Create brown bars\r\n",
        "budget_bar = plt.barh(r, bars1, edgecolor='grey', height=1, label=\"Budget\")\r\n",
        "# Create green bars (middle), on top of the firs ones\r\n",
        "mains_bar = plt.barh(r, bars2, left=bars1, edgecolor='grey', height=1, label=\"Mainstream\")\r\n",
        "# Create green bars (top)\r\n",
        "tmp_bar = np.add(bars1, bars2)\r\n",
        "prem_bar = plt.barh(r, bars3, left=bars2, edgecolor='grey', height=1, label=\"Premium\")\r\n",
        "\r\n",
        "for i in range(7):\r\n",
        "    budget_width = budget_bar[i].get_width()\r\n",
        "    budget_main_width = budget_width + mains_bar[i].get_width()\r\n",
        "    plt.text(budget_width/2, i, bars1_text[i], va='center', ha='center', size=8)\r\n",
        "    plt.text(budget_width + mains_bar[i].get_width()/2, i, bars2_text[i], va='center', ha='center', size=8)\r\n",
        "    plt.text(budget_main_width + prem_bar[i].get_width()/2, i, bars3_text[i], va='center', ha='center', size=8)\r\n",
        "    \r\n",
        "# Custom X axis\r\n",
        "plt.yticks(r, names)\r\n",
        "plt.ylabel(\"LIFESTAGE\")\r\n",
        "plt.xlabel(\"TOTAL SALES\")\r\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\r\n",
        "\r\n",
        "plt.title(\"Total Sales per Lifestage\")\r\n",
        "\r\n",
        "plt.savefig(\"lifestage_sales.png\", bbox_inches=\"tight\")\r\n",
        "\r\n",
        "# Show graphic\r\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9II6JPxXT8cksJq8s5oRDtk8o3vZJHjd0TwLgD12SuOjNXK7qksRbc4o4IMXoPyaXTxcXbbediNRe33//PWvXrqVDh+0GyHLXXXdx9dVXA3DjjTdy2WWX0bt3b5588kl++ctfcs011/Daa6/VdMgiUgvUaLIgvFP9GjCq9E5+VZtUo+xl4Dgza7WjTsI1CaaZ2XYjBtw9C+gKXAWsBV4zs8t30FUx8A/gtkr2dUC4r/lmNmBH7ULlpyF84e7FBFMmegPzgUfN7K6w/RkEyY2fq6rz+ksz+y7c1/3uXlWyIHIaws0A7v48cATBVImTgK/NLLn8hhasWTHZzCbn5OTsyrHs01LbdqfZ/3uShPRG5C78Jqpuy7f/JfGAQ6hzcJADSmnVhcbn30ZJXhap7U4ge/Y4DvjVdWTP+iwWoYtILfTCtELOaZfArGvr8n9tE3jx+8Ko+n9+nU+HRvH0aBGMbDqjTQJv9EllY55zwREJvDSjkOFnp2y3nYjUXhs2bOC6667j3//+93Z1b7/9NuvXr+fiiy8G4Oijj+b111+nTZs2dOrUieeee46hQ4cyduzYmg5bRGqBWDwNoSR8AcwmuFCP1JVtd7LXAw0j6jKAdZGN3b2IYHrALRHFs4FMs2B8trvfE87Br1dRQO5e7O7j3H0wcB3wm0riHwX0BA6JKJsFdAn7Wh/u62mgbiX97JAHvnH3+4DfRcRzJsG6BKX7LH/uSv3c8/qFux/t7l3dffiuHAOAu69w9+fc/TygiGA0RPk2T7t7N3fvlpqauqu72id50bY/xi05FUvYlmvJXTyV/OVzqH/C76K38RJyF00mtW13SvKyAMq+iog4kJESDDdulGpszttW9+HCIr5aVsygnklR25S4894PRZx7eCIb84K8culXEandioqK6NevHw899BBNm0YvCfb999/zxBNP8MQTT2y33dNPP81VV13Fxo0bAdi0aVONxCsitUusH534IPBAONwdM+sMXA48GdaPAy4J6+KBfkBFt0lHECxM2BjA3RcQDLv/e7gdZlYH2G6iZbhqf9uIos7A0h0F7O6FBEPsI5eUfRC4w8yOiCjbpStfM2tWbkHBzsBSCyaTHQVMC8sfBy4rfXJDuO0F4cKHDwG32bYnQ7QkmKrxcNh0HMHChaXn4zIqPq+7LHw6RGL4vilwALB8d+5jX5e7eAqrXr6VVS/fSkn2JhIbt2TzV8EwwA0fP0XR5tWsfuU21o99vGyb7FmfkdYhmEmT0vJoVo68kZRWVa1PKSL7i4uPTOT12YWcNCKbl2YU8psOCdwzPhjod/37eSzeVMLJL+Rw9bu5Zdu8+H0hF4eLGp7eOoFjn8nijNYJFfYvIrXLG2+8wbfffsvAgQM56aSTmDhxYtmChTfffDOrV6/mV7/6FeedV/bgLcaPH0/37t1JTk6mX79+9OjRo8LpCyKy74vp//bu/o6ZNQe+MjMnWNivn7uvDJv8DRhmZtMJLvTHAi9W0E+BmQ0F/hVR/AeCKQMLzGw9kAsMrCCMusBj4Zz6ImABwZSEyvwbGBSx/xlm9idgpJnVI7hL/yMwOGKbp8zsn+H7ZcBFhGsWRLR5DvgvwZMLmgF5BFMj+hOMDPgufLIE7r7azH4Xtj2QYLTGeGBsWHcL8G54wV4IDHT30n09DbQHpofnfTKVTK2IcKqZ/RTxuU/49SUzK/3Lcp27n0YwZeJfZlZ63+pmd19VjX1IKLXtcaS2PS6qrP4JvwWg+ZVPVbhN3U6nlr1P7/J/pHf5vz0XoIjsdRrUMT7olxZVdkfPYNTSvOsqHgx3aea2kQbXHpPEtcckVdhORGqfiy66iIsuuiiq7Pjjjwfggw8+qHCbnj170rNnTwDOPfdczj333D0bpIjUWhb57HapvcxsELDA3V+NdSx7SrNmzTzp0mdiHYbsB65ImcxgfyTWYYjIbjTEbmJ0y9GxDkMq0XtJ733id+8Qu4nBgwdX3VAkRsxsirt3i3UcsvfTOMK9hLv/PdYxiIiIiIiIyP4h1msWiIiIiIiIiEgto2SBiIiIiIiIiERRskBEREREREREoihZICIiIiIiIiJRlCwQERERERERkShKFoiIiIiIiIhIFCULRERERERERCSKkgUiIiIiIiIiEkXJAhERERERERGJomSBiIiIiIiIiERRskBEREREREREoihZICIiIiIiIiJRzN1jHYMIAM2aNferr74q1mHIfiDeiyi2hFiHsVcrpph44mMdhkgZfU/WfvvKv1Ft+T+kflod/jzglliHIbWQmU1x926xjkP2frH/TSdSxnk+V7/XRPYGV6RMZnTL0bEOQ2Sf0XtJbwb7I7EOQ6qrFtxrG5J9U6xDEJF9nKYhiIiIiIiIiEgUJQtEREREREREJIqSBSIiIiIiIiISRckCEREREREREYmiZIGIiIiIiIiIRFGyQERERERERESiKFkgIiIiIiIiIlGULBARERERERGRKEoWiIiIiIiIiEgUJQtEREREREREJIqSBSIiIiIiIiISRckCEZEq5C6awqqXb2XVy7fy0+OXkDN/Ylndpgkvs3LUX1g56i/kLpkGQM6Cb1g58i9sGj8KgML1y9g88fWYxC4i+75HJ+bT47nsqLI3ZhVy7DNZdH82i//OLQTgzk/z6P5sFmPmB5+fmVLA+KVFNR6vyL7m0UcfpUePHlFlf/rTnzjxxBPp3r07X375JQB33nkn3bt3Z8yYMQA888wzjB8/vsbjFakuJQuqwQITzOzMiLI+ZjbWzK4ys7nh6xsz6xHRZomZNYr4fJKZjQnfX25mJWZ2VET9TDNrGb6va2bDzGyhmU01sylmduUO4is2s2kRr9I+/mxmeWZWv1wMbmZ/iCjrHJYNCD+PMLPe4ftxZtat3P7KH8facvvvYGZxZjY0PKYZZvatmbXa+bMvEnsprbvS9OL7aXrx/cTXa0ydlp3L6tI6ncJBlzxMkz5D2PzlKwBkz/qMpn0foGD9jwBsmfIu6V3OjknsIrJvyy9ypq0u2a780a8LGHd5GuMuS+ORrwsAmL2uhAlXpPHi94XkFzmTlhfT89CEmg5ZZJ+Sn5/PtGnTtit/6KGH+Pzzz3n99de59957AZg9ezYTJkzgxRdfJD8/n0mTJtGzZ8+aDlmk2pQsqAZ3d6A/8IiZ1TGzusC9wPvA1UAPd28ftnnZzJpWs+ufgDt2UPcssBFo6+5dgF5Axg7a5rp754jXkrD8IuBb4IJy7WcCF0Z8vgiYXs2YK/Jauf3PBn4LNAOOcvcjgV8Dm37GPkRirnDTKuLTGhCXlFJWltgg/HGPTwQzACw+AS8pxjAK1iwioX5T4pJTYxGyiOzj/v1dIZdlJm5X3iYjjuwCJ6vAqZcc/m4CCoohOcF4ekohV3VNquFoRfY9//73v7nsssu2K09MDH4us7KyyMzMBMDMKCgoIDk5maeffpqrrrqqRmMV2VlKFlSTu88E3gVuAf4KjAR6Aze7+7qwzVTgBeCP1ex2DNDRzA6PLDSzNsCxwCB3Lwn7XuvuD1Q33rCPusAggmRApKVAHTNrYmZGkIh4v7p9V9NBwMqI+H9y9427eR8iNSpn3lektj2+wrrNX75MeudeANQ75nzW/+9RUg//BVunvkedQzqy/oMnyJ6joYYisvsUFjvjlhRxSqvtRwf8un0CRz+VTeensrn+2CApcH77BK74by79uyYyc00xizeW0H9MLjNWF9d06CL7hMLCQsaNG8cpp5xSYf2vf/1rzjjjDE477TQAzj//fK644gr69+/PzJkzWbx4Mf3792fGjBk1GbZItSlZsHOGABcDZwIPAh2BKeXaTA7Lq6Mk7Of2cuUdgemlF9rVkBIxBeDtsOx3wKvAF8DhZtak3DajgT7ACcBUIL+a+6rIb8tNQ0gBXgfOCT8/bGZHV7RhOI1jsplNzsnJ+RkhiOx5uQu/IaVt9+3Kc+Z/RUnuVtI6nARAUpM2ND7/VhIaNCWx8aFkff8RGaddTe6iqTUcsYjsy0Z9X8jFR24/qgDg7s/zmf3Husz5Y13u/jz4L77fUUm83ieVL5cVc+0xSYxdWMRjZ9bh8W8KajJskX3GqFGjuPjii3dY//bbbzNp0iRuvz34U79fv368/vrrfPnll1x77bWMHTuWxx57jMcff7ymQhbZKUoW7AR3zwZeA0a5e3Uurr0aZS8Dx1U2n9/M7ggvulfsoEnkNIRfh2UXAa+GCYc3CRIDkV4Pyy4CXqnqQKpQfhpCrrv/BBwO3EaQFPnEzE4tv6G7P+3u3dy9W2qqhmlL7VWctRGLSyA+pV5UecGaxWyd+j8yTr9mu22ypn9AemYvSvKyACjJz6qRWEVk/zBvXQnDJhfQ68VsZq0t5rFJ2y76kxMgNRHSEqGgeNufHlvznUUbS8hsGs+mvKB8U35Ff66ISFXmzZvHsGHD6NWrF7NmzeKxxx4rq8vPDy4V6tatS1paWln51q1bWbRoEZmZmWzaFMzQLf0qUttoVZudVxK+AGYDXYFPI+q7ArPC9+uBhsC68HNGxHsA3L3IzB4mmN5QajaQaWZx7l7i7vcA95hZta40zOxIoC3wUTDLgCRgMVCWtnT3VWZWCJwO/IlghMFuFSZU3gfeN7PVwPnAJ7t7PyI1IWfB11GjCjZ8NJyM0/uz8bPnKM7exOrX7yQuOY0Df3MnAHnLZpJ0UDssIZG0jiez6qWBUQsjioj8XA+cXqfsfY/nsunTMYF7xudzR89krumWxC/CJyRErk0wdFJB2bSEDo3i6PF8Nnf8MrlmAxfZRzzwwLYZwj169KBPnz7cc8893HHHHfz2t79l06ZNFBcXc99995W1Gzp0KNdffz0AHTp0oEePHtxxx46WMBOJLQvW7pPqMrO7gCx3f8jMzgXuBHq5+3oz6wy8A3R395Vm9hCQ4+5/NbN44A3gP+4+0swuB7q5+3VmlkSQIEgPt11iZq8DC4A73b3YzOoA6909rYKYsty9bsTne4Gt7n5fRNli4CSgFTDA3c82sxOAA939P+WOawQwxt1Hm9m4sP3kiL5Oiuij7DjKxdQFWOXuK8wsDhgBfO/uD+3o3DZr1syTLn2m0vMvIrXDFSmTGd1ydKzDENln9F7Sm8H+SKzDkL3IELuJwYMHxzoMqYXMbIq7d6u6pUjlNLLgZ3D3d8ysOfCVmTmwFejn7ivDJn8DhpnZdIJFiMcCL1bQT4GZDQX+FVH8B+AfwAIzWw/kAgOrGdrvgLPKlb0dlk+K2O9X1ezvf+EoBICJwBPl6n8b+chI4FqgHvCMmZXerviGiJENIiIiIiIiUntpZIHUGhpZILL30MgCkd1LIwtkZ2lkgeyIRhbI7qIFDkVEREREREQkipIFIiIiIiIiIhJFyQIRERERERERiaJkgYiIiIiIiIhEUbJARERERERERKIoWSAiIiIiIiIiUZQsEBEREREREZEoShaIiIiIiIiISBQlC0REREREREQkipIFIiIiIiIiIhJFyQIRERERERERiaJkgYiIiIiIiIhEMXePdQwiADRr1tyvvvqqWIchItVQTDHxxMc6DJF9hn6mRAK1+Wehflod/jzglliHUSUzm+Lu3WIdh+z9EmIdgMg2zvO5+r0mIjXvipTJDPZHYh2GVNMQu4nRLUfHOgwR2QN6L+lda38fD8m+KdYhiNQoTUMQERERERERkShKFoiIiIiIiIhIFCULRERERERERCSKkgUiIiIiIiIiEkXJAhERERERERGJomSBiIiIiIiIiERRskBEREREREREoihZICIiIiIiIiJRlCwQERERERERkShKFoiIiIiIiIhIFCULRERERERERCSKkgUiUqsVbV7Nssf6serlW1n92p1RdQWrF7HqxZtZ9dJA8pbNBCBnwTesHPkXNo0fBUDh+mVsnvh6jcctIlJbleSXsOSRJSy6bxFL/7WUksKSsro176xh7p/nsvrN1WVlq99czcK7F7Jl2hYANozbQPa87BqPW0RkdzGzlma22szGmdmH5ep+b2aLzezFiLL+Zva1mfUPP59hZpfWdNw1TcmCvZCZFZvZNDObaWbvmlmDsLylmeWGdaWvS81sUvj+RzNbG1HX0syWmFmjXek3rFtiZkOypNEAACAASURBVDMiyoeG5cdF7HeOmd0Vo9Ml+4CUlp1pevH9NPnt36LKN014kUbn3sKBF95dlhDInvUZTfs+QMH6HwHYMuVd0rucXeMxi4jUVltnbCW1TSqtb2tNSqsUsmZkldVlnJjBwVcfHNU+f0U+rW9vzeavNlNSWELuolzSDk+r6bBFRHa3j9z9JHc/o1z5O8Dp5cpOd/fjgNK2FwMv7ekAYy0h1gHILsl1984AZvYC8EfgnrBuYWldhJFh28uBbu5+XWmFmf2cfkud7O7rypW9AFzo7tPNLB44fCeOTyRK3o/fs+qlgaS2O4F6x5xfVl6Sl0VCvUYAeGEeJYX5WHwCXlKMYRSsWURC/abEJafGKnQRkVon6cAkchflAlCSU0J83fiyuoT6CbCi3AYGXuxYorFx3EYantSwBqMVEdljTjazL4C33P3R0kJ3X2dmdcu1LTazhPDrucB77l5ck8HGgkYW7P0mAs1rYb8HAisB3L3Y3WfvlqhkvxOflkGzK5+myUX3kbdkGgVrFpfVxaXWp2DtEopzNlO4dikl+dnUO+Z81v/vUVIP/wVbp75HnUM6sv6DJ8ieMz6GRyEiUnskN0kmZ0EOP9z+A7lLckk9rPKEar0u9fjp2Z/IODmDvOV5FK4tZPmI5eQty6uhiEVEdruVQDvgZOA0MzuqivbPAq8ATwMXAOvNbLiZnbJnw4wtJQv2YuEd+1MJhsqUalNuusAva6DfzyLKbwzLHgXmmdnbZna1mdXZ2ThEACwhkbikOlhcPCmHHUvhuqVldQ1PvJyNn/6bDR88QeKBrYhPqUdSkzY0Pv9WEho0JbHxoWR9/xEZp11N7qKpMTwKEZHaY+OXG0nvnE7be9uSnpnOpombKm3f4IQGtPhjC3J+yCHjlAy2zthKs37NWP/J+hqKWERk93L3fHfPdvciYAzQqYr2H7p7H6Ah8BbQ1937A/32fLSxo2TB3inFzKYBq4AmwEcRdQvdvXPE64sa6PfkiPJHAdz9bqAb8CHBnJ6xFe3QzK4ys8lmNjknJ2cnQpX9RUn+tu+L/J9mk9DgoLLPiRnNafLbv5Hxqz+SUK8xFr9tZlXW9A9Iz+xFSV5W2M+2ObkiIvs1h/i0YOpBfN14SnJKqtgAinOLKVhbQEqLFIpzgpG3pV9FRPY2ZpYe8fEXwMJqbBMHnOXu7xAkDYj4uk9SsmDvVLq2wKGAEawtUOv6dfeF7j6MYJRCppkdUEGbp929m7t3S03VvHLZXv5Ps1g54k+sGjWA+PQDSG52OBs+Gg7A1ukfsuqV21j3v0eo36Nv2TZ5y2aSdFA7LCGRtI4ns+qlgSQ2ahGrQxARqVUaHNeALd9uYdF9i9j09SbqdavHmnfWALDh8w2senUVmyZuYsXIbYsXrP9oPRmnZQBQp1kdFt27iAbHN4hJ/CIiu8EvzWyKmX0FLHf3SWb2GICZnQ28CJxqZm9GbNMPeDl8/5GZfUNwY3SfpQUO92LunmNmNwD/MbMna1O/ZvZ/BAt/ONAWKAYqH+coUoGUNseQ0uaYqLKM0/sDkJ55BumZ5RewhTqHdKLOIcFostS23Ult233PByoispeIT4un5YCWUWUHnnsgEDwNIePEjO22Ka0HaNK7CU1oskdjFBHZk9z9PeC9cmXXh1/HEExNKL/NyIj3TwK77fqrttLIgr2cu38HfA9cFBaVX1vghhroN3LNgtIfoksI1iyYBowimNej8YoiIiIiIiJ7AY0s2Au5e91yn8+J+JhSyXYjgBHlylr+nH4jty9X/rsdxSEiIiIiIiK1m0YWiIiIiIiIiEgUJQtEREREREREJIqSBSIiIiIiIiISRckCEREREREREYmiZIGIiIiIiIiIRNnlZIGZ6UkKIiIiIiIiIvugSi/4zWyCu/cI349y90siqr8BuuzJ4EREREREROTnmTJlyoEJCQnPAp3Q6PKaUgLMLCoq+kPXrl3XxDqYXVHV6IC0iPcdy9XZbo5FREREREREdrOEhIRnmzZtekTjxo03xsXFeazj2R+UlJTY2rVrO6xatepZ4NxYx7MrqsoqVfaNpG8yERERERGR2q9T48aNtyhRUHPi4uK8cePGmwlGc+yVqhpZ0MDMfk2QVGhgZheE5QbU36ORiYiIiIiIyO4Qp0RBzQvP+V477aOqwD8nGDJxdvj+nPB1NjB+z4YmIiIiIiIi+4L4+Piu7du373D44Yd36NChwxEfffRRWtVbbTNmzJj0k08++bBd3f+tt97adFe33V9VOrLA3a+oqUBERERERERkz7vvwYcyC3Kzd9vT7ZJS0opuGzhgemVtkpOTS+bOnTsb4M0336x3++23H3z66afP210xVGXo0KEH3X///atqan/7gqqehnAw0NLdJ4SfbwLqhtUvu/uCPRyf7FeMK1ImxzoIEdkPxXsRQ+ymWIexWxRTTDzxsQ5jjyqmmN5Lesc6DBHZA4opjtnv46p+f8Z7EUOGDCn7XD+tDn8ecEtNhLbbFeRmJzyf22239XcFk3cq8bB58+b4+vXrF0EwYuDhhx9u8tlnny0AuPTSS1t069Yt+4Ybblg/evToejfffPMhKSkpJccee2xW6fYrVqxI6N27d6s1a9Ykde3aNeuLL76oN2XKlDkHHXRQ0ZNPPpkxbNiwJoWFhdalS5fskSNHLr3hhhua5+fnx7Vv375Du3btct95553Fu+3g92FV/aP+A3gp4vPVwNNAKjAE6LuH4pL9krM7f2mJiOyPrkiZzOiWo2MdhojIXqf3kt4M9kcqbxQx639I9r6RZK4ppRfr+fn5tm7dusT33ntvfmXtc3Jy7Lrrrmv50UcfzevYsWP+2Wef3bq07tZbb2124oknbr3vvvtWjR49ut7rr7/eCGDq1Kl1Ro8enTF58uS5ycnJ3q9fvxbDhw8/4Mknn1w+YsSIA0tHNkj1VJUsONzdx0R8znH3hwHM7Is9F5aIiIiIiIjsKyKnIXz88cdpV1xxRav58+fP2lH7adOm1Tn44IPzjzzyyHyAvn37rn/22WcbA3zzzTd1//Of/ywA6N2795Z69eoVA4wdOzZ95syZqZmZmUcA5OXlxR144IFFe/rY9lVVJQvqlPt8asT7Rrs5FhEREREREdnHnXbaadkbN25MWLlyZUJiYqKXlJSU1eXn59uu9uvu1qdPn/VPPPHE8t0S6H6uqqchbDWzdqUf3H0DgJm1B7buycBERERERERk3/Pdd9/VKSkpoUmTJkVt2rTJX7BgQUpubq6tW7cufsKECfUAOnfunLd8+fKkWbNmJQO8+uqrGaXbH3PMMVmjRo3KAHjrrbfqbdmyJR6gV69eW8aMGdNw+fLlCQCrV6+Onz9/fhJAQkKC/5xExP6oqpEFg4ExZnYPMDUs6wrcDvxpTwYmIiIiIiIi+4bSNQsA3J1hw4YtSUhI4LDDDis855xzNrZv377jwQcfnN+xY8ccgNTUVH/ssceWnn322YelpKSUdO/ePSsrKyse4P7771/Ru3fv1m3btj2ga9euWY0aNSps0KBB8UEHHVQ0aNCg5aeeemq7kpISEhMTfejQoT+2a9euoG/fvmuPOOKIDp06dcrRAofVU9WjE8ea2QXAQOCGsHgWcIG7z9zTwYmIiIiIiMjulZSSVrSzTzCoqr+q2hQXF0/ZUd3w4cN/An4qX967d+8tvXv33m5dg4yMjOLx48fPT0xM5OOPP06bNm1aWkpKigNceeWVG6+88sqN5bcZNmzYckDTE3ZCld8gYVLg0sgyMzvEzG5293/sschERERERERkt7tt4IDpsY7h51iwYEHShRde2KZ09MBTTz21JNYx7YuqnU0ys8ZAH+AioBnw9p4KSkRERERERKQiRx55ZP6cOXP0GMQ9rNJkgZmlAxcAFwPtgLeAVu5+cA3EJiIiIiIiIiIxUNXIgjXAN8AgYIK7u5n9es+HJSIiIiIiIiKxUtWjE28DkoEngdvMrM2eD0lEREREREREYqnSZIG7/9PdjwPOC4v+AzQzs1vMrN0ej05EREREREREalylyQIzuxfA3Re5+73ufiTQDagHvFcD8YmIiIiIiMhezsy6nnfeea1KPxcWFtKwYcPMk08++bDKths/fnzq5Zdffsiu7PPuu+8+cOvWrVWNppcdqGrNgl7A7ZEF4aMU7whfO83MDgaeADoQJCvGADe7e4GZnQQMcPezy20zDjgIyAeSgI+BQe6+KawvBmZEbPKqu98fsV0eUABc6e7TKojpbOBvYTyJwL/c/SkzuwvIcveHzGwEcDrQ2t3zzawRMNndW4Z9tAUeBY4ANgFbgMHuPt7MLge6uft15fa7BNgKFIdF4939BjM7DvgXwRSQZOA1d78r3CYRmOTuXcysKfBP4Jhwn6uBP7v7fDPrCDwGNA+PayTw93DdibLjKhdLN3dfF3E+E4A5wGXunmNmWe5et9wx3AVcCayNKD4pPN/PAEcBFsbXy92zyp9/kdood9EUNn/9BgBFG5aTcca1pLY7HoCs7z9k01evUaf5ETQ6ZwAAW797j6wZn1D3yFNJP/oschdPpTh7I3U7nRqzYxARqY02frmRTRM24SXOIf0PIbFhIgBZM7NY/dZq4pLiaHZpM5KbJbPh0w1snLCRhj0aknFKBltnbqVocxENf9Ewxkchsnd75MF7M7fmFlb7yXhVSU9JLLpp4O2VPo4xJSWlZN68eSlZWVlWt25df/vtt+s1adKksKq+e/bsmdOzZ8+cXYnrqaeeanLllVduSE9PLylfV1RURELCbjsFNcrMLgUuA+KBvu6+PCzvA9wMOHCvu//XzP4GnAH8zd3HmNmVwDx3H1/Vfqo6O/Fm1pDgYm877r6h2kcUBG8ET1QY5u7nmVk88DRwD8FBVaavu082syTgPuC/wIlhXa67d65iuyuAfxBc8EfGlBjGcKy7/2RmyUDLHfRVDPweGFaujzrA/wgSHe+EZZ0IRmFU9Y9wsruvK1f2AnChu08Pz9HhEXU9gC/Dc/k28IK7/y7cZybQxMyWAe8A17j7h2aWCrwJXEuQqKlK2fk0s5eA/sAjlbR/NDLxEG53G7A6HI2CmR0OVPnLQKS2SGndlZTWXQFYOfIm6rTc9ism5bDuJB/Sic0TXi4ry1syjYMufZg1b99D+tFnkT37cw4484Yaj1tEpDYr3FhI9txsWt3Saru6Ne+sodUtrSjOLWblyytpcW0LsmZl0eavbfjxsR/JOCWDzRM30/z/NY9B5CL7lq25hQmDvbI/73fOkNybqnXVfdppp21+4403GlxxxRUbX3nllYzf/OY3G7766qu6AJ999lnqjTfe2CI/Pz+uTp06JSNGjFicmZmZP2bMmPSHH364yWeffbbgpptuarZs2bKkpUuXJq9YsSKpf//+qwcNGrRmy5Ytceeee27rlStXJpWUlNjAgQNXrF69OnHNmjWJJ554YruGDRsWTZo0aX5qaurRffv2XTt+/Ph6Q4cO/XHhwoVJw4YNa1JYWGhdunTJHjly5NKEhAT69u3bYvr06Wl5eXlx55xzzsZHH310BUDz5s2PPP/88zd88skn9RMSEnz48OFLb7311uZLly5Nvv7661cPHDhwbeVn4Oczs+bAie5e0R2pGwlu3DowluCauQPBNeQoM/sI6O7uz1RnX1UNyWgPTNnBa3J1dlDOKUCeuz8P4O7FBAf0+/CCtkruXgAMBFqEF8fVNZHgLnt56QRJk/Vh//nuPm8HffwTuNHMyv8w9AUmliYKwn5muvuInYgv0oHAyrCfYnePfIZoL+B94GSg0N2HR+xzurt/QfCoyy/d/cOwPAe4Drh1F2L5Aqh0aNAOHAQsj4htnrvn70I/IjFVuGkV8WkNiEtKKSuLT62PxcVHN7Q4vKQYszhyfphESutu27cREdnPZc3IghJY/MBiVoxagZd4VH1cchyJDRIpWFMQFoAXOxhs+W4L6ZnpWFyF97BEZC9wySWXbHjttdca5uTk2Jw5c1KPP/747NK6zMzMvG+//XbunDlzZg8ePHj5wIEDD66ojwULFtT5/PPP53/77bdzHnrooWb5+fn21ltv1WvatGnhvHnzZv/www+zLrjggi2DBg1ac+CBBxZ+/vnn8ydNmjQfIDc3N6579+7Z8+bNm924ceOi0aNHZ0yePHnu3LlzZ8fFxfnw4cMPAHjkkUeWz5w5c87cuXNnffnll+mTJk0q+0OwRYsWBXPnzp3dvXv3rN///vct33333YWTJk2a+8ADDzTb0+cv9CuCm/qfmNlj4c3lUguBNKAuwUh3CBIHSQSj9K8iuFFeLVUlC2a7e2t3b1XBq3W1D2ebjgSJhjLuvgX4kZ24IA2TDNMJkhkAKWY2LeL12wo260WwQGP5vjYQ3IVfamavmFlfM9vRefkRmABcUsFxTa1u/OV8FhH3jWHZo8A8M3vbzK4ORy6UOhkYB3Si3LksF0/587wQqGtm9aobWJgUOZPoKR4VuTHiGD4Ly54DbjGziWb293CahsheJ2feV6S2Pb7KdnUzz2DdOw9SN7MXOfO/Ii4lnfUfPE7u0kpH5ImI7FeKthThxU6rW1oRlxzHlqlbous3F5G/Ip/8FcH9hYYnNmTZsGVknJTBlslbiK8bz/IRy8marVmNInuj7t275/7000/JzzzzTMZpp522ObJuw4YN8WeddVabtm3bdhw4cOAh8+fPr1NRH2ecccamlJQUP+igg4oyMjIKf/rpp4QuXbrkfvHFF/Wuueaa5mPHjq17wAEHFFe0bXx8PJdffvlGgLFjx6bPnDkzNTMz84j27dt3mDBhQr1FixYlA7zwwgsZHTp0OKJDhw4dfvjhhzrTp08vi+XCCy/cBHDkkUfmdOnSJbthw4YlzZo1K0pKSipZt25dTdwpagIkhSMLctj2MAIIRp5/B0wjmJIOwTXw88BwgmvIVmY23MyOrGpHe/NiD5Fp5Vx37xzxei2i7iUzW0ywxkKFQ/Dd/Q/AqcA3wACCC90duY9gysQOz114kT/TzN6qxnGcHBH3o2E8dxNMYfiQYJTA2LDf5sCGcKTAz+FVlKeY2TSC0SM/Av+uor9HI47h5PAYpgGtCaZ+ZADfmtkR5Tc0s6vMbLKZTc7J+bmHJbL75S78hpS23atsl9KqC43Pv42SvCxS251A9uxxHPCr68ie9VmV24qI7C/iUuJIOzwNgLQj0sqSAgBNL2zKsmHLWPu/taS2DQacpndKp8V1LSjOKaZet3psmriJ5pc3Z9PETTGJX0R+vl69em0aPHjwIZdeemnUlPZbbrml+Yknnrj1hx9+mPXuu+8uKCgoqPB6Kzk5uexaJj4+nqKiIjvqqKPyp06dOvvII4/MvfPOO5sPGDDgoIq2TUpKKildp8DdrU+fPuvnzp07e+7cubOXLFky85FHHlkxd+7cpMcff7zJ559/Pn/+/PmzTznllM15eXllsdSpU8cB4uLiSEpKKoslLi6OwsLCmhj6tBn4PHz/KcGaeaX+SjDt4IjwPe7+ortfCPwCeJLgJvr1BCPPK1VVsuBf5QvMrGE4X35XzAa6luuvHtACWFDdTsKhFkcSLL5Xlb4EF60vsC27sh13nxFerJ8O/KaSdj8QZGoujCieBXSJaPNr4HKCi+Rd4u4L3X0YQRIj08wOIPiH/SBin113sHlF57k1waKGWwimXJRfGSidYBFCiE6+XB9O/diVY8hy97fc/VrgReCsCto87e7d3L1bamq1ZqKI1JjirI1YXALxKdUbkONeQu6iyaS27U5JXnDXq/SriIhA6mGp5C3LAyDvxzySGidF1bW6tRWNz2lMnWbbbih6ibN1+lbqHV2P4uzgZmHpVxHZ+1xzzTXrBgwYsOLYY4/NjSzfsmVL/MEHH1wA8NRTTzXamT6XLFmSmJ6eXnLttdduuOmmm1ZNmzYtFSAtLa148+bNFV7z9urVa8uYMWMaLl++PAFg9erV8fPnz0/auHFjfEpKSklGRkbxsmXLEsaNG1d/1450j/mKYBF5gM7A4oi6fILRBtkEUw8AMLN0goX6pwMNwuIGVKGqZEELM2sf7iA5HGK+EFhtZqdV40DK+wRIDVdvLL3ofxgYUd275eGChPcBy9z9++ps4+4O3AkcV3o8Ef3VDZ/CUKozsLSKLu8hGIFQ6mXgF2Z2bkTZLl/5mtn/RSRk2hIsrLiJbesVQJBFSjazqyK2O8rMfgm8BPQo/TcysxRgKPBg2HQ8cG74TYOZXQBMD6d37BZm9otwcUzCRSk7UPV5FalVchZ8HTWqYMNHw8Pyb1j37sPkLZ3O2rfvLavPnvUZaR2CdVdTWh7NypE3ktKqCyIiEkg5NAVLMhbdt4jcxbmktktlzTtrgGCBw8X3L2b16NU0Pq9x2TabJm6i/vHB3+p1O9Zl4ZCF1O1Ut8L+RaT2a9OmTeGgQYPWlC+/5ZZbVt11110HH3HEER2Kiop2qs8pU6akdO7c+Yj27dt3uOeee5r99a9/XQlw2WWXrevVq1e77t27tyu/TdeuXfMGDRq0/NRTT23Xrl27Dqecckq7ZcuWJR5//PG5nTp1ymnTpk2nCy+8sHXXrl1r1Z2fcAR3bvjkv2OACWZW+qTCYcCXBAmFyLUJbmDbjfPZBFPrX6pqXxZcR++g0mwW0Cl83N5VwEXAaUA7glX4j92J4yrt8xCC4Q/tCZIV7xE8RSA/vGh/n3CxwVAfguRA6aMTkwkenXhHJY9OHOvut4YncIC7Tw7b/QXo4O7/LyKedOA1oA2QS5CF+VP4BIW7iH504hh3Hx1u9xbQJeLRie0JnhjQnuARhluBB9394/DRiY+z7c49wHEE/0iRj0783t0vNbNXCUYq5ABFBFMoPiZ4VOPREbE3I1h0sSvB4yGXEDw68YdwDspj4XmLB0YBd4eJE8zsaoKnIziwBujv7ovCuu0ekRiWlwArIooeAeqx/aMTzwd6EiRUjODf+X/ALV7JN1yzZs086dJqLcwpIiI7cEXKZEa3HB3rMERE9jq9l/RmZ54QMMRuYvDgwXswol1jZlPcvVtk2fTp05dkZmaWPYEtFo9O3F9Nnz69UWZmZstYx7ErqvoGKYi4uPsV8Gp493lOBU8EqBZ3Xwacs4O6cUBKBVUnVdFnhQtJuPtJ5T4/XEGbrVQwPD6suyvi/eXl6i4o93luJf2MAEZUUNVyB+1/V77MzHoAk8q1W0H0dIjIuhlUct7c/SngqR3UVZiud/cdjUS5q4KyJcDIHe1fRERERERiQxf2Uh1VXfDnm1kngjvlJxM99F4TzGuQu08gGIkgIiIiIiIiskdVlSz4MzAaaEyw4v1iADM7i+CRDCIiIiIiIiKyj6k0WeDuXxPMwS9f/h7BWgMiIiIiIiIiso+p9GkIZvbPiPd/Klc3Yg/FJCIiIiIiIiIxVNWjE3tGvL+sXN1RiIiIiIiIiMg+p6pkge3gvYiIiIiIiEi1xMfHd23fvn2Htm3bdjzzzDNbb926tapr0Z324IMPNn788ccP2N397q+qWuAwzswaEiQVSt+XJg0qfFyhiIiIiIiI1F73/eO+zIKcgqquBastKTWp6Labb6v0cYzJycklc+fOnQ1w7rnntnr44Yf/f3v3Hh5Vee59/HsnISEhgIqKIihiFUQQRFSkoGgFD9t6KlQo1opayta6FSvWbkoBu2m7W63datWiVYqgqKiVstmIJ14FRA3KIYJyjAoCAnJMQpJJ7vePWQlrhiQkkGRI8vtc11yz1nNa98rKBOae51lzzLhx4zaX1hcVFdGkSZNDiuPee+/dckgDSIwD/YK0BBaxL0HwcajOayUiERERERERqTWFeYUp09tPr7HxBuYMrFbioU+fPnuWLl2aPnPmzOZjx45t07Jly+K1a9c2Xb16dfbtt9/edv78+c0LCwvtpz/96TejRo3aOnPmzObjx49v06JFi8jnn3+ecdVVV33btWvX/Mcee6x1QUGBvfrqq2vOOOOMgrvvvrtNZmZm8f3337/53HPP7fjAAw98dcEFF+Rt3LgxpWfPnqdv2LBh2cMPP9xqxowZR+Tl5SV98cUXTW+//fZNhYWFSS+88EKr1NTUkjlz5qxq3bp1cY39cOqxA30bQvs6ikNEREREREQauKKiIl5//fUWAwYM2AWwfPnyjE8++eTTTp06FT7wwANHt2zZsjg7O3tFfn6+nXPOOZ2+//3v7wL47LPP0rOzsz899thjIyeddFLXtLS0rcuWLVvx29/+9tgHH3zw2KeffvqrqsawcuXK9CVLlizPz89P6tixY5cxY8ZsWLFixfJbbrml3d/+9rdWv/nNb76prfOvTw70bQg/D22fUfvhiIiIiIiISENTUFCQ1KlTp85du3bt3LZt28I777xzK8CZZ56Z26lTp0KAN998s8WLL77YqlOnTp3POuus07dv356yfPnypgBdu3bNPemkk4rS09P9xBNPLLj88st3AnTr1i3/yy+/TK1OLL1799595JFHlrRp0yaSmZlZPGjQoB3BMfJycnLSavbM668DTRe5GXg02H4W6FG74YiIiIiIiEhDE75nQVhGRkZJ6ba724MPPvjlD37wg13hNjNnzmyelpZWtgw+KSmJpk2beul2cXHxfjfjT0lJ8eLi6GqCvLy8mPrU1NQKx4pEIrqxf6A6d6DUD01ERERERERqRf/+/Xc+/vjjxxQUFBjA0qVL03bt2nVQ35rQrl27gg8//LAZwNSpU4+syTgbiwPNLDjCzK4lmlRoYWbXhSvd/ZVai0xEREREREQajZEjR27NyclJ69q16+nubkcddVTRrFmz1hzMWPfdd9/m66+/vsOkSZOO6d+//46ajrUxMPeKv9TAzJ6ppK+7+801H5I0Vm3anOA/+9nwRIchIoeZYopJ1rf1Vpl+XiIiB6e6fz+TPUKx7fvstWWzptx1zy9rI7RqMbNF7t4zY1wySgAAIABJREFUXLZkyZKcbt26bS3dT8RXJzZWS5YsObpbt27tEx3HwTjQtyEMq6tARMB5Jr/ngZuJSKMyLD2Lmvx6J5GGbmDOQMb6nxMdhkjjEPrcdXzu3YmLo5r0xl6qotJkgZlV+hvvrn+JRERERERERBqaA009aV5JXcXrF0RERERERESk3jrQMoTxFdWZ2V01H46IiIiIiIjUsJKSkhJLSkrSB751qKSkxICSAzY8TB3U11AE6s+iHBERERERkcYre8uWLS2DN69SB0pKSmzLli0tgexEx3KwDuUOmPpFExEREREROcxFIpFbN23a9NSmTZu6cGgfGEvVlQDZkUjk1kQHcrAOJVmgKSwiIiIiIiKHubPPPvsb4KpExyH1y4G+DWE35ScFDEivlYhEREREREREJKEOdIPDyr4NQUREREREREQaIK1XEREREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIF1WRmbc3sNTNbZWZrzOx/zCw1qOtnZjPL6TPXzD43s6Vm9pmZPWpmR4Tqi81scehxX1y/JWb2kZl1ryCm0nal/QcG5d3NzM3ssrj2bmZTQvspZralNHYzu8nMHg22x5nZPeUcc0/w3N7M8uPivzGou9nMlgXnnW1mV1f35y0i5Ssp2ss3L41j03P38c3Lv8UjRWV1W177bzY9dx8bn/0FXz9zBwC7P5nFxsm/YPcnswDIX/cxe7LfSkjsIiL1Qc6OElo/sJt+k3IZ8GzufvXuTvcn9vDUx4UAPJFVSK+ncnkiK7o/Z02EyUsK6zRmkcbIzEaa2by4sr+Z2Xwzm2dmZwZlI8xsoZmNCPYHlL5vkfIpWVANZmbAK8A/3f1U4DQgE5hQhe5D3f1M4EygAHgtVJfv7t1Djz/E9esGPAb86QDjl/afHpQNAeYFz2G5QBczK/1Gi/7AhiqcQ0XWxMU/2czaAqOBPsF59wKWHsIxRCRk79qPSW3TkeN+9AfSjj+N/HWLyuqOufqXHPejP9DyvB+Qfsq50fY5izn+xgfJz/kEgNzl/49mnfslInQRkXqjf4cU5t7UjDk/brZf3b9WRjimmZXtv7E2wsJbmzFnTQSA55YVMbRrkzqLVaQxMrM0oLwPVP/g7t8FhgFjg7L+7t4LGBDs/wiYWvtR1l9KFlTPxcBed38GwN2LgZHAzWaWUZUB3L0QuBc40cy6VePY7wMnVLVxkNgYBNwE9DezpnFNZgH/FmwPAZ6vRixVcSywG9gD4O573H1dDR9DpNFKOfI4vGgvACUFuSSl7//lNXkr3yfjtPOjO5aElxRjlkTeqg9I79ATS0quy5BFROqdd3Ii9H0ml4feL9iv7rllRQw+Y18yINkgUuIkJ8GMz4u44tQUkpNsv34iUqNuAf4RXxh631EEFAfbxWaWEjxfBcwK3s9JBZQsqJ4zgEXhAnffBXwJfKeqgwS/lEuATkFRetw0/uvL6XYZ8M9Khp0a6t8K6A2sc/c1wFz2JQZKTQMGB0mEM4EPqhp/OU6Ji79vcH6bgXVm9oyZff8QxheROClHtqFgw2d8/dRtFGxaRdoJp8fUe3GEwi1fkHZc9E9TZrcBbJ3xRzK7XUbeygUkpTdn2+uPkv/FkkSELyJy2Ds+01j580ze+UkGb64rZunmfe8p5qyJcOFJKSSH/id9a49Uhrycz/AeqbyyIkKrdGPEzHzeXhdJQPQiDZ+ZNQH6ufvblTT7PfBwsP0U0Q9IJwLXAdvM7Akzu7h2I62/lCxInHCqOX4Zwguhuqlmto7olP6/VjJeeBnCNqKzBaYFddOIW4rg7kuB9kH5rEM8l/hlCO8FCZHLgIHASuAhMxsX39HMhptZlpll5eXlHWIYIo1HbvbbpH/nXNrc+hjpHc4h99N3Yur3frmMpid2LdtPP7kHx1zzK0r27iHjtN7kLp9Lq0t/vl8/ERGJSksxmqUaKUnGlaemkP1NSVndUx8XMuys2CUGA05J4aVBGWzf61x3egpTlxXxxJXpTFlaFD+0iNSMHwPPVVRpZncBy919HoC7z3H3QcCRRJeWD3X3EcANdRFsfaRkQfUsB84OF5hZC+BEYHVVBzGzZKArsKIKzYcCHYhOr3mkGuP/APiNmeUE/S4zs/h5yjOAB6j5JQgAeNSH7v57YHAQU3ybie7e0917ZmRUaSWHiAC4k9Q0+pJOzmhBSUFssi1vVWgJQlmXEvLXZpFx6nmU7N0DUPYsIiKxdhd42fb8r4o55ch9n/Os3FbCNdPyePD9Qv6ysJDPtkZnHZS4M2tVhKs6NmH73mj/0mcRqXEdgX83s9nAGWZ2R2mFmQ0gOtP6v8IdzCwJuMLdZxBNGhB6ljhKFlTPW0BG6G7/ycCDwCR3r9LH4sF0md8DXwWf7h+QuzswBuhlZp0O1B74HrDU3du5e3t3Pwl4Gbg2rt3TwHh3X1aVOKrDzNqYWY9QUXfgi5o+jkhj1azzheR99h6bnruP3E/nknFab3YuiE5KcncKNnxGWtvOMX1yP32HZp0vBCC9/VlsnDyS9JN77De2iIjAe19GOHviHnr/PZcTmhvntU3hjln5ACwekcnsG5rxi/NTuatXKp2Ojt4DZsrSIn4U3NSwf4cUzn1yDwM6pCTsHEQaMnf/pbtf6u6XAZ8CL5nZ6KD6EeBk4B0z+1uo2w3sm43whpl9CMyps6DrGf31qgZ3dzO7FnjMzMYQTbbMAv4z1Ox7ZrY+tD8oeJ5qZgVAGvAmEP4awXQzWxzan+3u98UdO9/MHgRGEb2RR2WGAK/Glb0M/DswOTTmevat4anMr4NpPKX92sbVnxIX/9NEv+3hATNrA+wFtgAjqnAsEamCpKaZtL7+tzFlLXtHb3diZrQZtv9LO7PL98q2m/f4N5r3iL+ViYiIlLri1CZccWrsUoNHrkiP2b+pe2rM/o3d9u3fdk4qt50TWy8itcPd+wSbE4L9jhW0C78XeozoN85JBZQsqCZ3/woo92Z97j4XSC+nqt8Bxiz3luTu3i9u/8EqthtWTpsZRJcd4O6Z5dTPJXojRNx9EjAp2B4HjCunfWbwnEP55wzRb48QERERERGRekbLEEREREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQsEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGKkJDoAkX2MYelZiQ6iSoopJpnkBnvMZI9QbPrzIIeHYooZmDMw0WGI1BvFFDPe7k50GCKNTstmTRMdgkiN0rsBOYw4z+T3THQQVTIsPYvp7afX6TEH5gxkrP+5To413u5m7NixdXIsERERERE5/GgZgoiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQsEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQskAYpsnsbGyfdyRcPXIuXFMfU7VzwAuv/eiPb3322rGzHu8+ycfLd5K3+EIDdi2ez96vsOo25PBuf28ja361l49SNMeVvrInQ66lcLvpHLp9tjZ7fE1mF9HoqlyeyCgGYsybC5CWFNRrPyJEj6du3L3feeWdM+bfffssPf/hDLr74YiZMmADAmDFjOO+885g5cyYATz75JO+++26NxiMiIiIiIrVDyYJaYmZuZg+G9u8xs3HB9jgz22Bmi0OPI8zsEzPrHrRJMbM9ZnZDaIxFZtYj7jj9zGxnaJw3Q3X/NLOFce3HBbF9J1R2V1DWM9jPMbOjg+095ZzbODO7J9ieZGbrQsdfEJS3NrOZZrbEzJab2axD+HFWW3J6c44dPIG0Nh33q8vsNoCjr7wnpqxo21ccN/SP5H76Dh4ponDjSpq261JX4ZYrPyefkr0ldPjPDnjE2bBhQ1nd/e8W8NaNGTx3XTpj5xYA8MbaCAtvbcacNREAnltWxNCuTWosno8//pg9e/bw3nvvUVhYyEcffVRWN378eO6//37efvttRo8eDcDy5cuZN28eU6ZMoaCggA8++IALLrigxuIREREREZHao2RB7SkArit9012Oh9y9e+ixA5gP9A7quwErS/fNrBlwCrCknLHeC41zSdD+COBsoKWZdYhrvwwYHNofBHxa/VMsMyp0/NL47wfecPdu7t4ZuO8Qxq82S0kluWlmuXXJzY4E27/ciyNYShN2L5lNZrdLaznCA8tbk0dml+g5NOvcjK+++iqmvlmqcXzzJNZ8WwJAskGkxElOghmfF3HFqSkkJ5Vzogdp4cKF9O/fH4BLLrmE999/v6wuOzub3/3ud1x00UVl5WZGYWEhaWlpTJw4keHDh9dYLCIiIiIiUruULKg9EWAiMLIafRawL1nQG3gC6B7snwsscvfi8jqW4zrgX8A0YhMDAP8ErgYws1OAncDWasRZFccD60t33H1pDY9fo9JPO59ts/5C8+6XU7TlCyI7N7Pt9Ucp3JKTsJiK84pJahp9iSZnJLN3796Y+s17SvhsazErtkaTBbf2SGXIy/kM75HKKysitEo3RszM5+11kRqJZ8eOHbRo0QKAli1bsmPHjrK6BQsW8Ktf/Ypp06YxatQoAK655hqGDRvGiBEjyM7OZt26dYwYMYJly5bVSDwiIiIiIlJ7lCyoXX8FhppZy3LqRoam7r8TlIVnFvQG3gUKzKx5sL+gguP0DY01OigbAjwfPIbEtd8FfGVmXYgmEl44mJML+VPo+FODsr8Cfzezd8xstJm1Ka+jmQ03sywzy8rLyzvEMA5e5hkXccw197F3/Qqa97iC/LUfc9QlI9i9aGbCYkpOT6ZkbzQRUJxfTNOmTcvq/nhJGoNfzucP8wr5brtkAAacksJLgzLYvte57vQUpi4r4okr05mytKhG4mnZsiW7du0CYNeuXRxxxBFldaeddhqnn346rVu3Jikp+mflhhtu4MUXX2T+/PncdtttzJ49m0ceeYRHH320RuIREREREZHao2RBLXL3XcBk4D/KqQ4vQ7goaP8FkGpmxwGdgM+Bj4DziCYL5ldwqPAyhAlm1ho4FZjn7iuBoiAxEFY64+Aa4NVDO9OYZQhDg3N5HegAPBmcyydmdkx8R3ef6O493b1nRkbGIYZxaEoK8ojs3ETqsR0oKdgTlOUmLJ6M72SwZ3k0jtzlubRt27as7vx2Kbzzk2aM7pvK6Ucnl5WXuDNrVYSrOjZh+14HKHs+VOeffz5vvfUWAG+++Sa9evUqqzvttNPYuHEjubm5RCL7ZjLs3r2btWvX0q1bt7KZCOEZCSIiIiIicnhSsqD2/QW4BWhWxfYLiN5DYKO7O7AQ+C7RZQjvV9Yx5IfAkcA6M8sB2rP/7IKZwI+BL4OkRo1z92/d/Tl3/zHRpEed3d3OiyNsnjaaom/W8c2LY9j75TJ2LohOoNi9ZA7b3/47ucvnsm3O42V9di/6Fy16fB+AJkefyKap99Ksc7+6Cnk/6e3TSWqSxNrfrQWLfrI/4d3ozQwnvFvARf/I5VdvFfCbC1PL+kxZWsSPgpsa9u+QwrlP7mFAh5QaiadHjx40bdqUvn37kpyczIknnlj2zQfjx49nyJAhXHzxxfz6178u6/Pwww9zxx13ANC5c2f69OnD0KFDayQeERERERGpPRZ9Pyo1zcz2uHtmsP1Hop/iP+3u44JvRdjj7g+U0+8u4A5gkrv/1syOB+YBee7etZz2/YB73P3KUNkC4Bfu/n6wfzLwprufEj62mQ0GVrr7x2Y2NxgnK0gw9HT3reHzCI0fHmMSMNPdp8e1uRhY6O55wTKKD4Eb3f0jKtCmTRtPvfHJiqoPK8PSs5jefvqBG9aggTkDGet/rpNjjbe7GTt2bJ0cS0RERERqjpktcveeiY5D6j/NLKgbDwLx34oQvmfBYjNrH5TPJzp9/30Ad98IJFPx/QpiBOOcRHRGAsEY64CdZnZeuK27T3P3jw8wZIaZrQ897i6nzZ/iziWV6DcxZJnZ0uBcnqosUSAiIiIiIiKHj5qZnyz7CX8a7+6bgYzQ/jhgXAX9PiLui/3cvX0lx5kLzA3t5wAnlNOuR7D5QQXj9CvveO5eaULJ3W+qoOpPwUNERERERETqGc0sEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQsEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhLD3D3RMYgA0KbNCf6znw1PdBhVUkwxySQ32GMme4RiS6mTY8nhLxG/79JwpWemc+8v7k10GCIiDZaZLXL3nomOQ+o/vRuQw4jzTL7+rtW2YelZjPU/H7ih8ogSGG93M7399ESHIQ3EwJyBiQ5BREREqkDLEEREREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQsEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiaFkgUgFIru3sXHSnXzxwLV4SXFMXe5n89g4eSQbJ99N3qqFAOx499no/uoPAdi9eDZ7v8qu87ilYXno/QL6PJ0bUzZ4eh79JuVy/t9z6f7EHgCeyCqk11O5PJFVCMCcNREmLyms83hFDkV2dja9e/emb9++DBs2DHePqevTpw/f/e53Wbp0KQBjxozhvPPOY+bMmQA8+eSTvPvuuwmJXUREpKGptWSBmbmZTQntp5jZFjObGezfZGaPBtvjzGyDmS02s1Vm9oqZdY4br3sw5mVx5aea2UwzW2Nmi8zsHTO7IHSMLcG4pY+YcYN2o83sUzNbGrQ5Lyifa2Y9g+0cM3s51GegmU0K7V9mZh+a2WfBGC+Y2YlB3SQzGxh3zPZmlh8X241B3c1mtiyIJ9vMrg7162VmTwbb55rZu2b2uZl9YmZPmVlGUHdN0H9FMNY1oTHKzisUS3aw3c/MdgbxrDCzsaHymeX87OYGxy89h+lBecegrnScifF9D3fJ6c05dvAE0tp03K9u90f/pPWQ39N6yO/Z9dE/ASja9hXHDf0juZ++g0eKKNy4kqbtutR12NKAFEScxZtL9iufNjCDuTc1497eqVx5WgoAb6yNsPDWZsxZEwHguWVFDO3apE7jFTlUHTt2ZMGCBbz33nsAZGVlldWNGTOG559/nhdffJExY8YAsHz5cubNm8eUKVMoKCjggw8+4IILLkhI7CIiIg1NSi2OnQt0MbN0d88H+gMbKmn/kLs/AGBm1wNvm1lXd98S1A8B5gXPs4N2TYH/Be5x9xlBWRegJ1D60cIL7v7zig5qZucDVwI93L3AzI4GUitofraZdXb35XFjdAEeAa5y9xVB2VVAe+DLSs55jbt3jxurLTA6iGenmWUCx4SaXA7MNrPWwEvAYHd/P+g7EGhuZqcCDwD93X2dmZ0MvGFma919aSXxlHrP3a80s2bAYjP71wHaD3X3rLiyh4le09eC2LpW4biHFUtJJTml/F+FlCOPx4sKAEhKTS8r9+IIltKE3Utmk9nt0jqJUxquv39SxE+6NeE37xSUW//qZxHu6hX9HU02iJQ4yUkw4/Mirjg1heQkq8twRQ5Zkyb7ElxpaWm0a9eubH/79u1l+zt27ADAzCgsLCQtLY2JEycyfPjwug1YRESkAavtZQizgH8LtocAz1elk7u/AMwBfgRgZgYMAm4C+gdJAoChwPuliYKgb7a7T6pGjMcDW929IOi/1d2/rqDtg0TfyMf7JfC70kRBMM4Mdz+YuZDHAruBPcE4e9x9Xaj+e8CbwO3AP0oTBUHb6e6+GbgniGddUL4O+D0wqjqBuHsusAj4zkGcx/HA+tBYyw5ijMNWxqnns/GZ/2DjM3fQ/OzvA5B+2vlsm/UXmne/nKItXxDZuZltrz9K4ZacxAYr9VJRsTM3J8LFJ5ef0y0qdpZ9U0yP45MBuLVHKkNezmd4j1ReWRGhVboxYmY+b6+L1GXYIodsxowZdOnShc2bN9OqVauy8pKSfbNsSpcnXHPNNQwbNowRI0aQnZ3NunXrGDFiBMuWNah/ckRERBKitpMF04DBwZv7M4EPqtH3Y6BTsN0bWOfua4C57EtAnBG0q8z1cVP90+Pq5wDtzGylmT1mZhdWMtaLQA8zi3/zXJU4ynNKXGx9gSXAZmCdmT1jZt8vbRzMeihy951AF6Jv5MtzRjl1WUF5lZlZK6AX8OkBmk4NncOfgrKHiM4O+T8zG2lmR1Tn2Ie7HQuep82tj9Pm1ifYOX8aAJlnXMQx19zH3vUraN7jCvLXfsxRl4xg96L9Vm+IHNCzS4v4USXLCObmFNPvpH2JhAGnpPDSoAy273WuOz2FqcuKeOLKdKYsLaqLcEVqzFVXXUV2djZt27YtuxcBRGcRlEpKiv735YYbbuDFF19k/vz53HbbbcyePZtHHnmERx99tM7jFhERaWhqNVkQTHlvT3RWwaxqdg/Pnx1CNPFA8Dyk3A5mrwZr/F8JFb/g7t1Dj/y4GPcAZwPDgS3AC2Z2UwUxFQN/An5VYdBmrYI3zSvN7J5Kzg+CZQihx3vuXgxcBgwEVgIPmdm4oP0AosmNQ+UHKOtrZp8Ex/qDux8oWTA0dA6jANz9GeB0oksl+gELzSwtvqOZDTezLDPLysvLO5hzSQhLboI1ScOapOEl+96MlRTkEdm5idRjO1BSsCcoy61oGJEKfb61hMezCrlsSi6fbinmkQ9ib1b46mdFXHt67KyDEndmrYpwVccmbN8bfUmXPovUBwUF+5bctGjRgvT0ffn9o446ivXr1/P111/TokWLsvLdu3ezdu1aunXrVrY8ofRZREREDl5dfBvCDKLr56u0BCHkLGCFmSUDPwB+Y2Y5RO8NcJmZNSf6iXeP0g7ufi3RpQpHVedA7l7s7nPdfSzw8+B4FXkWuABoFyori8PdtwX3IZgIZFYnjlA87u4fuvvvgcGheC4nuF9DcMyzKxhieTl1Z7NvhsA24MhQ3VHA1tD+e+5+lruf7e5PHMw5ALj71+7+tLtfDUSIzoaIbzPR3Xu6e8+MjIyDPVSt8OIIm6eNpuibdXzz4hj2frmMnQteAKD5WVewacooNk0ZRWa3fffc3L3oX7ToEZ0M0uToE9k09V6ade6XiPClnvvv/k15/YZmzL6hGWcck8ygM1KY8G70jZS78/76YvqcmBzTZ0poNkL/Dimc++QeBnSozVvTiNSs2bNnc+GFF3LhhReyefNmzjzzTCZMmADA+PHjuf766xk0aBD3339/WZ+HH36YO+64A4DOnTvTp08fhg4dmpD4RUREGpK6+F/k08AOd19mZv2q0sHMfkD0U/RfEF2jv9TdLw3V/wO4FngO+JWZXRW6b0G13nGaWUegxN1XBUXdgS8qau/uRWb2EHAf8HZQ/EfgVTNbGLpvwUG98zWzNsBx7l66rKE78EVw34YzgcVB+aPAh2b2v+7+QdD3OmA+0eTMS2b2trvnmFl74D+JzlaA6FKOG8zsTY8u/PwJ8M7BxFvJeVwGvBX8vI4DWlH5DS4PO5acQuvBE2LKmp4YvU9jZtdLyOx6yX59Wva+vmz7yAtuhAturN0gpVGYd3MzAEZfEJ2cY2Z88rP9c5E3dtt3Q87bzknltnMquleryOHp6quv5uqrr44pGz06equgM888k/nz5+/Xp7QeYMKECWXJBRERETk0tZ4scPf1RO+MfyAjzewGoBmQDVzs7lvMbAjwalzbl4F/d/fJZnYl8Gcz+wvRtf67gf8Ktb3ezPqE9m9z9wWh/UzgkWBNfQRYTXRJQmX+Dvw6dI7LzOxOYLKZtSD6Kf2XwNhQn78FMQJ8RXQpxSlmtjjU5mngNeCBIGmwl+jSiBFEZwZ8Ery5x903m9ngoO2xQAnRb4CYHdT9EviXmTUBioB73b30WBOJ3g9iiZk50fsZVLi0IuR7ZrY+tD8oeJ5qZqXLO7a6+yVEkz3/Y2Z7g/JR7r6pCscQERERERGRBLPSOwrL4c3Mfg2sdvdpB2xcT7Vp08ZTb3wy0WE0eMPSsxjrf050GFKPjLe7md5+eqLDkAZiYM5Axo4de+CGIiJyUMxskbv3THQcUv9pMWs94e7/deBWIiIiIiIiIoeuLm5wKCIiIiIiIiL1iJIFIiIiIiIiIhJDyQIRERERERERiaFkgYiIiIiIiIjEULJARERERERERGIoWSAiIiIiIiIiMZQsEBEREREREZEYShaIiIiIiIiISAwlC0REREREREQkhpIFIiIiIiIiIhJDyQIRERERERERiZGS6ABESiUlJzMsPSvRYTR4KRQz3u5OdBhSjxRTzMCcgYkOQxqI9Mz0RIcgIiIiVWDunugYRADo2bOnZ2UpWSAiIiIicrDMbJG790x0HFL/aRmCiIiIiIiIiMRQskBEREREREREYihZICIiIiIiIiIxlCwQERERERERkRhKFoiIiIiIiIhIDCULRERERERERCSGkgUiIiIiIiIiEkPJAhERERERERGJoWSBiIiIiIiIiMRQskBEREREREREYihZICIiIiIiIiIxzN0THYMIAGa2G/g80XFIQhwNbE10EJIQuvaNl65946Vr33jp2teNk9z9mEQHIfVfSqIDEAn53N17JjoIqXtmlqVr3zjp2jdeuvaNl65946VrL1K/aBmCiIiIiIiIiMRQskBEREREREREYihZIIeTiYkOQBJG177x0rXW1dSdAAAHjklEQVRvvHTtGy9d+8ZL116kHtENDkVEREREREQkhmYWiIiIiIiIiEgMJQsk4czsMjP73MxWm9l9iY5HDp6Z5ZjZMjNbbGZZQdlRZvaGma0Kno8Mys3MHg6u+1Iz6xEa5ydB+1Vm9pNQ+dnB+KuDvlb3ZykAZva0mX1jZtmhslq/1hUdQ+pOBdd+nJltCF77i83silDdr4Lr+LmZXRoqL/dvv5mdbGYfBOUvmFlqUJ4W7K8O6tvXzRlLKTNrZ2bvmNlyM/vUzO4MyvXab+AqufZ67Ys0YEoWSEKZWTLwV+ByoDMwxMw6JzYqOUQXuXv30Fcj3Qe85e6nAm8F+xC95qcGj+HA4xD9DyEwFjgPOBcYG/pP4ePAT0P9Lqv905EKTGL/n39dXOuKjiF1ZxLlv/YeCl773d19FkDw93wwcEbQ5zEzSz7A3/7/Dsb6DrAduCUovwXYHpQ/FLSTuhUBfuHunYFewO3BddNrv+Gr6NqDXvsiDZaSBZJo5wKr3X2tuxcC04CrExyT1KyrgX8E2/8ArgmVT/aohcARZnY8cCnwhrt/6+7bgTeAy4K6Fu6+0KM3W5kcGkvqmLu/C3wbV1wX17qiY0gdqeDaV+RqYJq7F7j7OmA10b/75f7tDz5FvhiYHvSP/z0qvfbTge+VfuosdcPdN7r7x8H2bmAFcAJ67Td4lVz7iui1L9IAKFkgiXYC8FVofz2V/+MjhzcH5pjZIjMbHpS1dveNwfYmoHWwXdG1r6x8fTnlcvioi2td0TEk8X4eTDV/OvQpcXWvfStgh7tH4spjxgrqdwbtJQGCqeBnAR+g136jEnftQa99kQZLyQIRqUl93L0H0emFt5vZBeHK4JMifQVLI1AX11q/T4eVx4FTgO7ARuDBxIYjtcnMMoGXgbvcfVe4Tq/9hq2ca6/XvkgDpmSBJNoGoF1ov21QJvWQu28Inr8BXiU63XBzMLWU4PmboHlF176y8rbllMvhoy6udUXHkARy983uXuzuJcCTRF/7UP1rv43oVPWUuPKYsYL6lkF7qUNm1oTom8Wp7v5KUKzXfiNQ3rXXa1+kYVOyQBLtI+DU4A64qURvhjMjwTHJQTCzZmbWvHQbGABkE72epXe6/gnwWrA9A7gxuFt2L2BnMMX0dWCAmR0ZTGccALwe1O0ys17BWsUbQ2PJ4aEurnVFx5AEKn0TF7iW6GsfotdrcHA385OJ3rDuQyr42x98YvwOMDDoH/97VHrtBwJvB+2ljgSvx78DK9z9z6EqvfYbuIquvV77Ig2cu+uhR0IfwBXASmANMDrR8ehx0NexA7AkeHxaei2Jrit8C1gFvAkcFZQb0TsirwGWAT1DY91M9GZIq4FhofKeRP8jsgZ4FLBEn3djfQDPE51yWkR0bektdXGtKzqGHgm/9s8G13Yp0f/YHx9qPzq4jp8Dl4fKy/3bH/wt+TD4nXgJSAvKmwb7q4P6Don+WTS2B9CH6PT/pcDi4HGFXvsN/1HJtddrXw89GvCj9A+wiIiIiIiIiAigZQgiIiIiIiIiEkfJAhERERERERGJoWSBiIiIiIiIiMRQskBEREREREREYihZICIiIiIiIiIxlCwQERGpJ8yslZktDh6bzGxDaP9EM3vNzFaZ2Roz+x8zSzWzS0Nt9pjZ58H25GDMa8zMzaxT6DjtzSy74kjAzDqa2dxgrBVmNjGu/i4z22tmLUNl/cxsZjljzQ3FtdjMplflGCIiIlJ7UhIdgIiIiFSNu28DugOY2Thgj7s/YGYGfAA87u5Xm1kyMBGY4O6jgNeDPnOBe9w9KzTsEGBe8Dy2GuE8DDzk7q8FY3eNqx8CfARcBzxThfGGxsVVlWOIiIhILdHMAhERkfrvYmCvuz8D4O7FwEjgZjPLqKiTmWUCfYBbgMHVPObxwPrSHXdfFhr3FCAT+DXRpMHBqvAYIiIiUruULBAREan/zgAWhQvcfRfwJfCdSvpdDcx295XANjM7uxrHfAh428z+z8xGmtkRobrBwDTgPaCjmbWuwnhTQ8sQ/lSFY4iIiEgtUrJARESk8RpC9E09wXOVZwEEsxhOB14C+gELzSwtPK67lwAvA4OqMORQd+8ePEZV4RgiIiJSi3TPAhERkfpvOTAwXGBmLYATgdXldTCzo4guX+hqZg4kA25mo6p6UHf/GngaeDq4IWIXMysETgXeiN5KgVRgHfBodU+qomMQN4tCREREap5mFoiIiNR/bwEZZnYjQHCDwweBSe6eV0GfgcCz7n6Su7d393ZE39T3rcoBzewyM2sSbB8HtAI2EJ1VMC4Ys727twHamNlJ1T2pSo4hIiIitUzJAhERkXrO3R24FhhkZquAlcBe4D8r6TYEeDWu7GX2LUXoaGbrQ4/4pQQDgGwzW0L02xZGufsmovcriB/3VfbdQPF7ceOeH5SH71nw5gGOISIiIrXMov+/EBERERERERGJ0swCEREREREREYmhZIGIiIiIiIiIxFCyQERERERERERiKFkgIiIiIiIiIjGULBARERERERGRGEoWiIiIiIiIiEgMJQtEREREREREJIaSBSIiIiIiIiIS4/8DPQc6Ny8WN/0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRwieNAplcGl",
        "outputId": "bfcfccea-7a3d-430b-d8fd-56f04a3a5cb7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "stage_agg_prem = merged_data.groupby(\"LIFESTAGE\")[\"PREMIUM_CUSTOMER\"].agg(pd.Series.mode).sort_values()\r\n",
        "print(\"Top contributor per LIFESTAGE by PREMIUM category\")\r\n",
        "print(stage_agg_prem)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Top contributor per LIFESTAGE by PREMIUM category\n",
            "LIFESTAGE\n",
            "NEW FAMILIES                  Budget\n",
            "OLDER FAMILIES                Budget\n",
            "OLDER SINGLES/COUPLES         Budget\n",
            "YOUNG FAMILIES                Budget\n",
            "MIDAGE SINGLES/COUPLES    Mainstream\n",
            "RETIREES                  Mainstream\n",
            "YOUNG SINGLES/COUPLES     Mainstream\n",
            "Name: PREMIUM_CUSTOMER, dtype: object\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5REAtP0ppH1n"
      },
      "source": [
        "The top 3 total sales contributor segment are (in order):\r\n",
        "\r\n",
        "1. Older families (Budget) \\$156,864\r\n",
        "2. Young Singles/Couples (Mainstream) \\$147,582\r\n",
        "3. Retirees (Mainstream) \\$145,169"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ogqb5EHFlcCZ",
        "outputId": "d8147057-3609-431f-c239-485316f58a8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        }
      },
      "source": [
        "unique_cust = merged_data.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"LYLTY_CARD_NBR\"].nunique().sort_values(ascending=False)\r\n",
        "pd.DataFrame(unique_cust)"
      ],
      "execution_count": 64,
      "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></th>\n",
              "      <th>LYLTY_CARD_NBR</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>8088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>6479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>4930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>4929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>4750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>4675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>4454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>4017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>3872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>3779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>3340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>2831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>2728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>2574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>2433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>2431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>2273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>1504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>1112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mainstream</th>\n",
              "      <td>849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>588</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         LYLTY_CARD_NBR\n",
              "LIFESTAGE              PREMIUM_CUSTOMER                \n",
              "YOUNG SINGLES/COUPLES  Mainstream                  8088\n",
              "RETIREES               Mainstream                  6479\n",
              "OLDER SINGLES/COUPLES  Mainstream                  4930\n",
              "                       Budget                      4929\n",
              "                       Premium                     4750\n",
              "OLDER FAMILIES         Budget                      4675\n",
              "RETIREES               Budget                      4454\n",
              "YOUNG FAMILIES         Budget                      4017\n",
              "RETIREES               Premium                     3872\n",
              "YOUNG SINGLES/COUPLES  Budget                      3779\n",
              "MIDAGE SINGLES/COUPLES Mainstream                  3340\n",
              "OLDER FAMILIES         Mainstream                  2831\n",
              "YOUNG FAMILIES         Mainstream                  2728\n",
              "YOUNG SINGLES/COUPLES  Premium                     2574\n",
              "YOUNG FAMILIES         Premium                     2433\n",
              "MIDAGE SINGLES/COUPLES Premium                     2431\n",
              "OLDER FAMILIES         Premium                     2273\n",
              "MIDAGE SINGLES/COUPLES Budget                      1504\n",
              "NEW FAMILIES           Budget                      1112\n",
              "                       Mainstream                   849\n",
              "                       Premium                      588"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N_iPVE1Clb_I",
        "outputId": "e3cc03a7-a737-40b8-9c9d-046983a5a38c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "unique_cust.sort_values().plot.barh(figsize=(12,7))"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7efe8d90ea58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YxWVpJrJlbx0",
        "outputId": "1452c9d0-7219-4f18-aaf2-83037dadbf7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "# Values of each group\r\n",
        "ncust_bars1 = unique_cust[unique_cust.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Budget\"]\r\n",
        "ncust_bars2 = unique_cust[unique_cust.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Mainstream\"]\r\n",
        "ncust_bars3 = unique_cust[unique_cust.index.get_level_values(\"PREMIUM_CUSTOMER\") == \"Premium\"]\r\n",
        " \r\n",
        "ncust_bars1_text = (ncust_bars1 / sum(unique_cust)).apply(\"{:.1%}\".format)\r\n",
        "ncust_bars2_text = (ncust_bars2 / sum(unique_cust)).apply(\"{:.1%}\".format)\r\n",
        "ncust_bars3_text = (ncust_bars3 / sum(unique_cust)).apply(\"{:.1%}\".format)\r\n",
        " \r\n",
        "# # Names of group and bar width\r\n",
        "#names = unique_cust.index.get_level_values(\"LIFESTAGE\").unique()\r\n",
        "\r\n",
        "# # The position of the bars on the x-axis\r\n",
        "#r = np.arange(len(names))\r\n",
        "\r\n",
        "plt.figure(figsize=(13,5)) \r\n",
        "\r\n",
        "# # Create brown bars\r\n",
        "budget_bar = plt.barh(r, ncust_bars1, edgecolor='grey', height=1, label=\"Budget\")\r\n",
        "# # Create green bars (middle), on top of the firs ones\r\n",
        "mains_bar = plt.barh(r, ncust_bars2, left=ncust_bars1, edgecolor='grey', height=1, label=\"Mainstream\")\r\n",
        "# # Create green bars (top)\r\n",
        "prem_bar = plt.barh(r, ncust_bars3, left=ncust_bars2, edgecolor='grey', height=1, label=\"Premium\")\r\n",
        "\r\n",
        "for i in range(7):\r\n",
        "    budget_width = budget_bar[i].get_width()\r\n",
        "    budget_main_width = budget_width + mains_bar[i].get_width()\r\n",
        "    plt.text(budget_width/2, i, ncust_bars1_text[i], va='center', ha='center', size=8)\r\n",
        "    plt.text(budget_width + mains_bar[i].get_width()/2, i, ncust_bars2_text[i], va='center', ha='center', size=8)\r\n",
        "    plt.text(budget_main_width + prem_bar[i].get_width()/2, i, ncust_bars3_text[i], va='center', ha='center', size=8)\r\n",
        "    \r\n",
        "# Custom X axis\r\n",
        "plt.yticks(r, names)\r\n",
        "plt.ylabel(\"LIFESTAGE\")\r\n",
        "plt.xlabel(\"UNIQUE CUSTOMERS\")\r\n",
        "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\r\n",
        "\r\n",
        "plt.title(\"Unique Customers per Lifestage\")\r\n",
        "\r\n",
        "plt.savefig(\"lifestage_customers.png\", bbox_inches=\"tight\")\r\n",
        "\r\n",
        "# # Show graphic\r\n",
        "plt.show()"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 936x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hBaMpMEVp406"
      },
      "source": [
        "The high sales amount by segment \"Young Singles/Couples - Mainstream\" and \"Retirees - Mainstream\" are due to their large number of unique customers, but not for the \"Older - Budget\" segment. Next we'll explore if the \"Older - Budget\" segment has:\r\n",
        "\r\n",
        "High Frequency of Purchase and,\r\n",
        "Average Sales per Customer compared to the other segment."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qlmCtX8Wp4Q5",
        "outputId": "82db5aa2-db0c-47d5-ea68-2d464850aa32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        }
      },
      "source": [
        "freq_per_cust = merged_data.groupby([\"LYLTY_CARD_NBR\", \"LIFESTAGE\", \"PREMIUM_CUSTOMER\"]).count()[\"DATE\"]\r\n",
        "freq_per_cust.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"]).agg([\"mean\", \"count\"]).sort_values(ascending=False, by=\"mean\")"
      ],
      "execution_count": 68,
      "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></th>\n",
              "      <th>mean</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">OLDER FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>5.031438</td>\n",
              "      <td>2831</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>4.954011</td>\n",
              "      <td>4675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>4.923009</td>\n",
              "      <td>2273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">YOUNG FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>4.760269</td>\n",
              "      <td>4017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>4.752569</td>\n",
              "      <td>2433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mainstream</th>\n",
              "      <td>4.731305</td>\n",
              "      <td>2728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">OLDER SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>3.737684</td>\n",
              "      <td>4750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>3.734429</td>\n",
              "      <td>4929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mainstream</th>\n",
              "      <td>3.715619</td>\n",
              "      <td>4930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>3.555090</td>\n",
              "      <td>3340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">RETIREES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>3.412887</td>\n",
              "      <td>4454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>3.382231</td>\n",
              "      <td>3872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>3.379679</td>\n",
              "      <td>2431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>3.337766</td>\n",
              "      <td>1504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>3.313166</td>\n",
              "      <td>6479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">NEW FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>2.738516</td>\n",
              "      <td>849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>2.702381</td>\n",
              "      <td>588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>2.702338</td>\n",
              "      <td>1112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>2.578388</td>\n",
              "      <td>8088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>2.445621</td>\n",
              "      <td>3779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Premium</th>\n",
              "      <td>2.440171</td>\n",
              "      <td>2574</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             mean  count\n",
              "LIFESTAGE              PREMIUM_CUSTOMER                 \n",
              "OLDER FAMILIES         Mainstream        5.031438   2831\n",
              "                       Budget            4.954011   4675\n",
              "                       Premium           4.923009   2273\n",
              "YOUNG FAMILIES         Budget            4.760269   4017\n",
              "                       Premium           4.752569   2433\n",
              "                       Mainstream        4.731305   2728\n",
              "OLDER SINGLES/COUPLES  Premium           3.737684   4750\n",
              "                       Budget            3.734429   4929\n",
              "                       Mainstream        3.715619   4930\n",
              "MIDAGE SINGLES/COUPLES Mainstream        3.555090   3340\n",
              "RETIREES               Budget            3.412887   4454\n",
              "                       Premium           3.382231   3872\n",
              "MIDAGE SINGLES/COUPLES Premium           3.379679   2431\n",
              "                       Budget            3.337766   1504\n",
              "RETIREES               Mainstream        3.313166   6479\n",
              "NEW FAMILIES           Mainstream        2.738516    849\n",
              "                       Premium           2.702381    588\n",
              "                       Budget            2.702338   1112\n",
              "YOUNG SINGLES/COUPLES  Mainstream        2.578388   8088\n",
              "                       Budget            2.445621   3779\n",
              "                       Premium           2.440171   2574"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gIiir5iYqH-w"
      },
      "source": [
        "The above table describes the \"Average frequency of Purchase per segment\" and \"Unique customer per segment\". The top three most frequent purchase is contributed by the \"Older Families\" lifestage segment. We can see now that the \"Older - Budget\" segment contributes to high sales partly because of the combination of:\r\n",
        "\r\n",
        "High Frequency of Purchase and,\r\n",
        "Fairly high unique number of customer in the segment"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYgJFjUnp4ON",
        "outputId": "ac1f3d5b-aa82-4cf1-c49b-350fe2d1323b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 703
        }
      },
      "source": [
        "grouped_sales.sort_values(ascending=False, by=\"mean\")"
      ],
      "execution_count": 69,
      "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></th>\n",
              "      <th>sum</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LIFESTAGE</th>\n",
              "      <th>PREMIUM_CUSTOMER</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>90803.85</td>\n",
              "      <td>7.647284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>157621.60</td>\n",
              "      <td>7.558339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>97646.05</td>\n",
              "      <td>7.456174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>132263.15</td>\n",
              "      <td>7.449766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>113147.80</td>\n",
              "      <td>7.443445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER SINGLES/COUPLES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>136769.80</td>\n",
              "      <td>7.430315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">NEW FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>17013.90</td>\n",
              "      <td>7.317806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>21928.45</td>\n",
              "      <td>7.297321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>139345.85</td>\n",
              "      <td>7.287201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER SINGLES/COUPLES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>133393.80</td>\n",
              "      <td>7.282116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Budget</th>\n",
              "      <td>168363.25</td>\n",
              "      <td>7.269570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>84025.50</td>\n",
              "      <td>7.266756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>103445.55</td>\n",
              "      <td>7.262395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RETIREES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>155677.05</td>\n",
              "      <td>7.252262</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NEW FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>11491.10</td>\n",
              "      <td>7.231655</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OLDER FAMILIES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>80658.40</td>\n",
              "      <td>7.208079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>YOUNG FAMILIES</th>\n",
              "      <th>Mainstream</th>\n",
              "      <td>92788.75</td>\n",
              "      <td>7.189025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">MIDAGE SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>58432.65</td>\n",
              "      <td>7.112056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>35514.80</td>\n",
              "      <td>7.074661</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">YOUNG SINGLES/COUPLES</th>\n",
              "      <th>Premium</th>\n",
              "      <td>41642.10</td>\n",
              "      <td>6.629852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget</th>\n",
              "      <td>61141.60</td>\n",
              "      <td>6.615624</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                               sum      mean\n",
              "LIFESTAGE              PREMIUM_CUSTOMER                     \n",
              "MIDAGE SINGLES/COUPLES Mainstream         90803.85  7.647284\n",
              "YOUNG SINGLES/COUPLES  Mainstream        157621.60  7.558339\n",
              "RETIREES               Premium            97646.05  7.456174\n",
              "OLDER SINGLES/COUPLES  Premium           132263.15  7.449766\n",
              "RETIREES               Budget            113147.80  7.443445\n",
              "OLDER SINGLES/COUPLES  Budget            136769.80  7.430315\n",
              "NEW FAMILIES           Mainstream         17013.90  7.317806\n",
              "                       Budget             21928.45  7.297321\n",
              "YOUNG FAMILIES         Budget            139345.85  7.287201\n",
              "OLDER SINGLES/COUPLES  Mainstream        133393.80  7.282116\n",
              "OLDER FAMILIES         Budget            168363.25  7.269570\n",
              "YOUNG FAMILIES         Premium            84025.50  7.266756\n",
              "OLDER FAMILIES         Mainstream        103445.55  7.262395\n",
              "RETIREES               Mainstream        155677.05  7.252262\n",
              "NEW FAMILIES           Premium            11491.10  7.231655\n",
              "OLDER FAMILIES         Premium            80658.40  7.208079\n",
              "YOUNG FAMILIES         Mainstream         92788.75  7.189025\n",
              "MIDAGE SINGLES/COUPLES Premium            58432.65  7.112056\n",
              "                       Budget             35514.80  7.074661\n",
              "YOUNG SINGLES/COUPLES  Premium            41642.10  6.629852\n",
              "                       Budget             61141.60  6.615624"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DfyTdKcrqN5j"
      },
      "source": [
        "Highest average spending per purchase are contributed by the Midage and Young \"Singles/Couples\". The difference between their Mainstream and Non-Mainstream group might seem insignificant (7.6 vs 6.6), but we'll find out by examining if the difference is statistically significant."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7IkWwYGp4JD",
        "outputId": "6701a5cc-45be-455e-c501-0d507cbcd9b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from scipy.stats import ttest_ind\r\n",
        "mainstream = merged_data[\"PREMIUM_CUSTOMER\"] == \"Mainstream\"\r\n",
        "young_midage = (merged_data[\"LIFESTAGE\"] == \"MIDAGE SINGLES/COUPLES\") | (merged_data[\"LIFESTAGE\"] == \"YOUNG SINGLES/COUPLES\")\r\n",
        "\r\n",
        "budget_premium = (merged_data[\"PREMIUM_CUSTOMER\"] == \"Budget\") | (merged_data[\"PREMIUM_CUSTOMER\"] == \"Premium\")\r\n",
        "\r\n",
        "a = merged_data[young_midage & mainstream][\"TOT_SALES\"]\r\n",
        "b = merged_data[young_midage & budget_premium][\"TOT_SALES\"]\r\n",
        "stat, pval = ttest_ind(a.values, b.values, equal_var=False)\r\n",
        "\r\n",
        "print(pval)\r\n",
        "pval < 0.0000001"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.854204010750742e-281\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9aYe8NdOqX9V"
      },
      "source": [
        "P-Value is close to 0. There is a statistically significant difference to the Total Sales between the \"Mainstream Young Midage\" segment to the \"Budget and Premium Young Midage\" segment.\r\n",
        "\r\n",
        "Next, let's look examine what brand of chips the top 3 segments contributing to Total Sales are buying."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqxTprmqp4GM",
        "outputId": "16a39205-e54d-4849-eb89-fbe6976c2804",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "merged_data.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"Cleaned_Brand_Names\"].agg(pd.Series.mode).sort_values()"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LIFESTAGE               PREMIUM_CUSTOMER\n",
              "MIDAGE SINGLES/COUPLES  Budget              Kettle\n",
              "YOUNG FAMILIES          Premium             Kettle\n",
              "                        Mainstream          Kettle\n",
              "                        Budget              Kettle\n",
              "RETIREES                Premium             Kettle\n",
              "                        Mainstream          Kettle\n",
              "                        Budget              Kettle\n",
              "OLDER SINGLES/COUPLES   Premium             Kettle\n",
              "YOUNG SINGLES/COUPLES   Mainstream          Kettle\n",
              "OLDER SINGLES/COUPLES   Mainstream          Kettle\n",
              "OLDER FAMILIES          Mainstream          Kettle\n",
              "                        Budget              Kettle\n",
              "NEW FAMILIES            Premium             Kettle\n",
              "                        Mainstream          Kettle\n",
              "                        Budget              Kettle\n",
              "MIDAGE SINGLES/COUPLES  Premium             Kettle\n",
              "                        Mainstream          Kettle\n",
              "OLDER SINGLES/COUPLES   Budget              Kettle\n",
              "YOUNG SINGLES/COUPLES   Premium             Kettle\n",
              "OLDER FAMILIES          Premium             Smiths\n",
              "YOUNG SINGLES/COUPLES   Budget              Smiths\n",
              "Name: Cleaned_Brand_Names, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Qk7gMTGp4D0",
        "outputId": "23a7bf50-dbcc-4401-a288-6a1393f6dc98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for stage in merged_data[\"LIFESTAGE\"].unique():\r\n",
        "    for prem in merged_data[\"PREMIUM_CUSTOMER\"].unique():\r\n",
        "        print('==========',stage, '-', prem,'==========')\r\n",
        "        summary = merged_data[(merged_data[\"LIFESTAGE\"] == stage) & (merged_data[\"PREMIUM_CUSTOMER\"] == prem)][\"Cleaned_Brand_Names\"].value_counts().head(3)\r\n",
        "        print(summary)\r\n",
        "        plt.figure()\r\n",
        "        summary.plot.barh(figsize=(5,1))\r\n",
        "        plt.show()"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Premium ==========\n",
            "Kettle     838\n",
            "Smiths     826\n",
            "Doritos    570\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Budget ==========\n",
            "Smiths     1245\n",
            "Kettle     1211\n",
            "Doritos     899\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAABVCAYAAAAfUrzeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALK0lEQVR4nO3de7BVZRnH8e+vgyBqchF0IM2DhpqjhnYqGU1NExUZq4kJHZygdCiN1MppQBpv0yRG45SWFzLNvGWhGeMlMNQZxybkkAh4QfGuaIpOeKEQ8OmP9W5cnuCw99pnX87u95k5c9Z633ev/eyXvR/etdY+76uIwMzMKveRRgdgZtZbOYGamRXkBGpmVpATqJlZQU6gZmYF9Wl0AD1lyJAh0d7e3ugwzKzFLF68eHVEDN1cXcsk0Pb2djo7Oxsdhpm1GEnPb6nOp/BmZgU5gZqZFeQEamZWUMtcA1328hrap93Z6DDMGuK5mcc3OoT/S1sdgUraKGmJpEclPSLpB5IqGrlKGi5pTtoeJWls0YDNzJpFOSPQf0fEKABJOwM3ATsC55XzBJL6RMQqYHwqGgV0AHdVHq6ZWfOoaCQZEa8BU4Cpymwr6VpJyyQ9LOkLAJImS5or6V5ggaR2Scsl9QUuBCakUe0ESYMl3S5pqaS/SzogHePw1GZJOvZHe/i1m5lVpeJroBHxjKQ2YGfg5Kwo9pe0DzBf0l6p6UHAARHxpqT29Nj3JJ0LdETEVABJlwEPR8SXJR0J/I5slHo28J2IeFDSDsB/qnqlZmY9rNq78IcCNwBExBPA80Apgd4TEW+WeYzr0zHuBXaStCPwIHCJpDOAgRGxoesDJU2R1Cmpc+PaNVW+FDOzylScQCXtAWwEXttK03cLRZRExEzgVKA/8GAa4XZtMzsiOiKio227AdU8nZlZxSq9mz4UuBL4ZWRT2T8ATEx1ewEfB1Zs5TBvA/nrmfljHAGsjoi3JO0ZEcsi4mJgEfA/CdTMrJHKuQbaX9ISYBtgA9np9iWp7nLgCknLUt3kiFgnqbvj3QdMS8e8CDgfuEbSUmAtMCm1OyvdlHofeBS4u5IXZmZWa2qVNZE6OjrCk4mYWU+TtDgiOjZX5z/lNDMryAnUzKwgJ1Azs4KcQM3MCnICNTMryAnUzKwgJ1Azs4KcQM3MCvKM9GbWUuo5O39VI1BJ7+S2x0p6UtLu3bQ/J7c9UNLpuf12ScuricfMrJ565BRe0lHApcBxEbHFNZSBc3LbA4HTt9TQzKzZVZ1AJR0G/BoYFxFPp7KTJT2UZpO/SlKbpJmkiUkk3QjMBPZM+7O6HLNN0ixJi9JM9d+qNk4zs55W7TXQfsDtwBFpQmUkfRKYABwSEeslXQ5MjIhpkqbm1ldqB/brsl9yCrAmIj4jqR/ZfKDzI+LZKuM1M+sx1SbQ9cDfyBLemansKODTwKI0rV1/tj75cldjgAMklRaiGwCMBD6UQCVNIVujibYdhxYI38ysuGoT6PvA18gWjjsnIn4CCLguIqZXcVwB342Ied01iojZwGyAfsNGtsa8fGbWa1R9DTQi1gLHAxMlnQIsAManJZBJq26W7syvl7RN2u46M33ePOC0UltJe0navtpYzcx6Uo/chU+Lxx0L/Aj4RPo9P80yfw8wLDWdDSyVdGNEvEF2bXN515tIwNXAY8A/0lebrqKFvrNqZq3BM9KbmXXDM9KbmdWAE6iZWUFOoGZmBTmBmpkV5ARqZlaQE6iZWUFOoGZmBTmBmpkV1DJ/3eMZ6c1sa3p6tvqyR6CSZkh6NM3PuUTS58p83HBJc9L2KEljc3XnSzq78rDNzBqvrBGopNHAOOCgiFgnaQjQt5zHRsQqoDQt3SigA7irQKxmZk2l3BHoMGB1RKwDiIjVEbFK0nOSLkoj0k5JB0maJ+lpSd+GD9Y6ktQXuBCYkNpPSMfeV9L9kp6RdEZ6zPaS7pT0SHrshM0FZWbWSOUm0PnAbmnRuMslHZ6reyHNKv8A8Fuy0ebBwAX5A0TEe8C5wC0RMSoibklV+wDHAJ8FzktT2B0LrIqIT0XEfsBfir08M7PaKSuBRsQ7ZLPMTwFeB26RNDlVz02/lwELI+LtiHgdWCdpYBmHvzMi1kXEarKZ63dJxzpa0sWSPh8Razb3QElT0si3c+PazTYxM6uZsm8iRcTGiLg/Is4DpgJfTVXr0u/3c9ul/XKuseYfsxHoExFPAgeRJdIfSzp3CzHNjoiOiOho225AuS/FzKxHlJVAJe0taWSuaBTQ3fLFW9LdLPT55xsOrI2IG4BZZMnUzKyplPs90B2Ay9Ip+QZgJdnp/LgKn+8+YJqkJcBF3bTbH5gl6X2yhetOq/B5zMxqzjPSm5l1wzPSm5nVgBOomVlBTqBmZgU5gZqZFdQyN5EkvQ2saHQcZRoCrG50EGXoLXGCY62V3hJrLePcPSKGbq6iZaazA1Zs6U5Zs5HU2Rti7S1xgmOtld4Sa6Pi9Cm8mVlBTqBmZgW1UgKd3egAKtBbYu0tcYJjrZXeEmtD4myZm0hmZvXWSiNQM7O6cgI1MyuoJRKopGMlrZC0UtK0Bseym6T7JD2WFuE7M5UPlnSPpKfS70GpXJIuTbEvlVT3qfsktUl6WNIdaX+EpIUpplvScixI6pf2V6b69jrGOFDSHElPSHpc0uhm7VNJ30v/9ssl3Sxp22bpU0nXSHpN0vJcWcX9KGlSav+UpEl1jHVWeg8slfSn/KTtkqanWFdIOiZXXrv8EBG9+gdoA54G9iBb6O4RYN8GxjOMbPE9yOY+fRLYF/gpMC2VTwMuTttjgbsBkS2FsrABMX8fuAm4I+3/ATgxbV8JnJa2TweuTNsnki3PUq8YrwNOTdt9gYHN2KfAx4Bngf65vpzcLH0KHEY2v+7yXFlF/QgMBp5Jvwel7UF1inUM2aTrABfnYt03ffb7ASNSTmirdX6oy5uqxm+I0cC83P50YHqj48rF82fgaLK/khqWyoaRffEf4CrgpFz7Te3qFN+uwALgSOCO9GFZnXuTbupfYB4wOm33Se1UhxgHpKSkLuVN16cpgb6Ykkuf1KfHNFOfAu1dklJF/QicBFyVK/9Qu1rG2qXuK8CNaftDn/tSv9Y6P7TCKXzpDVvyUipruHQ6diCwENglIl5JVa+Srf0EjY//58APyZZgAdgJ+FdEbNhMPJtiTfVrUvtaG0G2Fte16VLD1ZK2pwn7NCJeBn4GvAC8QtZHi2m+Ps2rtB8b/Z4t+SbZCBkaFGsrJNCmJGkH4FbgrIh4K18X2X+FDf/+mKRxwGsRsbjRsWxFH7JTuSsi4kDgXbJTzU2aqE8HAV8iS/rDge3JVpntFZqlH7dG0gyy1TFubGQcrZBAXwZ2y+3vmsoaRtnSzLeSnV7clor/KWlYqh9GtgIpNDb+Q4ATJD0H/J7sNP4XwEBJpXkS8vFsijXVDwDeqEOcLwEvRcTCtD+HLKE2Y59+EXg2Il6PiPXAbWT93Gx9mldpPzb0M6dsReBxwMSU8OkmpprG2goJdBEwMt3l7Et2IX7uVh5TM5IE/AZ4PCIuyVXNBUp3KyeRXRstlX893fE8GFiTO52qqYiYHhG7RkQ7Wb/dGxETydauGr+FWEuvYXxqX/PRSkS8Crwoae9UdBTwGE3Yp2Sn7gdL2i69F0qxNlWfdlFpP84DxkgalEbcY1JZzUk6luyS0wkRsbbLazgxfathBDASeIha54daXqyu1w/Z3cInye62zWhwLIeSnQItBZakn7Fk17UWAE8BfwUGp/YCfpViXwZ0NCjuI/jgLvwe6c23Evgj0C+Vb5v2V6b6PeoY3yigM/Xr7WR3f5uyT4ELgCeA5cD1ZHeGm6JPgZvJrs2uJxvZn1KkH8muP65MP9+oY6wrya5plj5bV+baz0ixrgCOy5XXLD/4TznNzApqhVN4M7OGcAI1MyvICdTMrCAnUDOzgpxAzcwKcgI1MyvICdTMrKD/Aq7CXMdmuGOmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Mainstream ==========\n",
            "Kettle      3844\n",
            "Doritos     2541\n",
            "Pringles    2315\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Premium ==========\n",
            "Kettle     1206\n",
            "Smiths      986\n",
            "Doritos     837\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Budget ==========\n",
            "Kettle     713\n",
            "Smiths     633\n",
            "Doritos    533\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Mainstream ==========\n",
            "Kettle     2136\n",
            "Smiths     1337\n",
            "Doritos    1291\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Premium ==========\n",
            "Kettle      247\n",
            "Doritos     167\n",
            "Pringles    165\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Budget ==========\n",
            "Kettle     510\n",
            "Doritos    343\n",
            "Smiths     341\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Mainstream ==========\n",
            "Kettle     414\n",
            "Doritos    274\n",
            "Smiths     254\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAABVCAYAAAAfUrzeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAALHklEQVR4nO3df7BUZR3H8fcnBCRRCUEHxbpimDlKhFfT0dR0TEUnbGLCGZx0xsZSKZ1yCrRRbCoxJystf6D5W5PSMkYzIcHJsREB+a2CqFihieiIGIUI3/44z+p65+6y9+zuWVg/r5mdPb/2fJ/nucuXc85z7/MoIjAzs577SKsLYGa2vXICNTPLyQnUzCwnJ1Azs5ycQM3Mctqh1QVolEGDBkVHR0eri2FmbWb+/PlrI2Jwd/vaJoF2dHQwb968VhfDzNqMpJcq7fMtvJlZTk6gZmY5OYGameXUNs9Al6xeR8fEB1tdDDNWTTm51UWwgtR8BSrpYknLJC2WtFDS52r83J6S7k3LIyWNLts3WdKFPS+2mVnr1XQFKulw4BRgVERslDQI6FPLZyPiZWBsWh0JdAJ/zlFWM7NtSq1XoEOAtRGxESAi1kbEy5JWSbo8XZHOkzRK0sOSnpf0TQBJHZKWSuoD/BAYl44fl859gKRHJb0g6dvpMztJelDSovTZcd0VysyslWpNoDOAvSWtkHStpKPL9v0jIkYCjwG3kl1tHgZcVn6CiHgHuASYFhEjI2Ja2rU/cAJwKHCppN7AicDLEfGZiDgQ+Eu+6pmZNU9NCTQi3gYOBs4GXgOmSToz7Z6e3pcAcyJifUS8BmyUNKCG0z8YERsjYi2wBtgjnet4SVdI+nxErOvug5LOTle+8zZv6PYQM7OmqbkTKSI2R8SjEXEpMAH4Stq1Mb1vKVsurdfyjLX8M5uBHSJiBTCKLJH+SNIlFco0NSI6I6Kz10d3rbUqZmYNUVMClfQpScPLNo0EKv55UxXrgZ1riLcnsCEi7gSuJEumZmbblFp/D7Q/cE26JX8XWEl2O39KD+PNBiZKWghcXuW4g4ArJW0BNgHn9DCOmVnTqV3mROrs7AwPJmJmjSZpfkR0drfPf8ppZpaTE6iZWU5OoGZmOTmBmpnl5ARqZpaTE6iZWU5OoGZmOTmBmpnl5BHpzbYBHsV++7TVK1BJm9P4ncvS+JzfldSjK9dqo9KbmW2varkC/W8a7xNJuwN3A7sAl9YSQNIOHpXezNpRj64kI2IN2SAiE5TZUdItkpZIWiDpCwCSzpQ0XdIs4JFqo9JLGijp/jTX0hOSRqRzHJ2OWZjOvdVRnMzMitTjZ6AR8YKkXsDuwOnZpjhI0v7ADEn7pUNHASMi4g1JHemz76SxPTsjYgKApGuABRFxqqRjgdvJrlIvBM6LiMcl9Qf+V1dNzcwarN5e+COBOwEi4lmyMUJLCXRmRLxR4znuSOeYBewmaRfgceCqNE/SgIh4t+sHPSK9mbVSjxOopGFkI8ev2cqh/8lVoiQipgBfB/oBj6cr3K7HeER6M2uZnvamDwauB34V2UCijwHj0779gI8Dy7dymq6j0pef4xiy2T/fkrRvRCyJiCuAuWSTz5mZbTNqeQbaL40g35tsNPo7gKvSvmuB6yQtSfvOTPPGVztf11HpJwM3S1oMbADOSMddkDqltgDLgId6UjEzs2bziPRmZlV4RHozsyZwAjUzy8kJ1MwsJydQM7OcnEDNzHJyAjUzy8kJ1MwsJydQM7OcPCK9mX1oNHrk/7quQCW9XbY8WtIKSZ+ocvxFZcsDJJ1btt4haWk95TEzK1JDbuElHQdcDZwUES9VOfSisuUBwLmVDjQz29bVnUAlHQXcCJwSEc+nbadLejKNJn+DpF6SppAGJpF0FzAF2DetX9nlnL0kXSlpbhqp/hv1ltPMrNHqfQbaF7gfOCYNqIykTwPjgCMiYpOka4HxETFR0oSy+ZU6gAO7rJecBayLiEMk9SUbD3RGRLxYZ3nNzBqm3gS6Cfg7WcI7P207DjgYmJuGtevH1gdf7uqLwAhJpYnodgWGAx9IoJLOJpujiV67DM5RfDOz/OpNoFuAr5JNHHdRRPwEEHBbREyq47wCvhURD1c7KCKmAlMB+g4Z3h7j8pnZdqPuZ6ARsQE4GRgv6SzgEWBsmgKZNOtmqWd+k6TeabnryPTlHgbOKR0raT9JO9VbVjOzRmpIL3yaPO5E4AfAJ9P7jDTK/ExgSDp0KrBY0l0R8TrZs82lXTuRgJuAp4Gn0q823UAb/c6qmbUHj0hvZlaFR6Q3M2sCJ1Azs5ycQM3McnICNTPLqW06kSStB5a3sAiDgLWO/6GNvy2UwfGbE/8TEdHtX+q0068GLa/UU1YESfMc/8Mbf1sog+MXH9+38GZmOTmBmpnl1E4JdKrjO36LtboMjl+wtulEMjMrWjtdgZqZFcoJ1Mwsp7ZIoJJOlLRc0kpJEwuKuUrSkjQlyby0baCkmZKeS+8fa2C8myWtKZ94r1I8Za5O7bFY0qgmxZ8saXVqg4WSRpftm5TiL5d0QgPi7y1ptqSnJS2TdH7aXkgbVIlfSBtI2jFNk7Moxb8sbd9H0pwUZ5qkPml737S+Mu3vaFL8WyW9WFb/0gwTDf8OpvP2krRA0gNpvZD6VxQR2/UL6AU8DwwD+gCLgAMKiLsKGNRl20+BiWl5InBFA+MdBYwClm4tHjAaeIhsYOrDgDlNij8ZuLCbYw9IP4e+wD7p59OrzvhDgFFpeWdgRYpTSBtUiV9IG6R69E/LvYE5qV6/A05L268HzknL5wLXp+XTgGl11r9S/FuBsd0c3/DvYDrvd4C7gQfSeiH1r/RqhyvQQ4GVEfFCRLwD3AOMaVFZxgC3peXbgFMbdeKI+BvwRo3xxgC3R+YJYICkIdShQvxKxgD3RMTGyOaxWkn2c6on/isR8VRaXg88A+xFQW1QJX4lDW2DVI/SNOK90yuAY4F70/au9S+1y73AcVI2x06D41fS8O+gpKFkg7fflNZFQfWvpB0S6F7AP8vW/0X1L3ajBNmg0fOVzc0EsEdEvJKW/w3s0eQyVIpXZJtMSLdoN5c9smhq/HQ79lmyq6DC26BLfCioDdLt60KyOcZmkl3VvhkR73YT4734af86YLdGxo+IUv1/nOr/c2WTQH4gfjdly+sXwPfIphKCrD6F1b877ZBAW+XIiBgFnAScp2x65/dEdu9Q2O+IFR0vuQ7YFxgJvAL8rNkBJfUH7gMuiIi3yvcV0QbdxC+sDSJic2Sz2A4lu5rdv1mxaokv6UBgUirHIcBA4PvNiC3pFGBNRMxvxvnzaocEuhrYu2x9aNrWVBGxOr2vAf5I9oV+tXSbkt57OhtpT1WKV0ibRMSr6R/VFuBG3r9FbUp8ZXNk3QfcFRF/SJsLa4Pu4hfdBinmm8Bs4HCyW+PSmBblMd6Ln/bvCrze4PgnpkcbEREbgVtoXv2PAL4kaRXZY7pjgV/SgvqXa4cEOhcYnnrj+pA9MJ7ezICSdpK0c2mZbBrmpSnuGemwM4A/NbMcVeJNB76WekIPA9aV3eY2TJdnWl8ma4NS/NNST+g+ZFNSP1lnLAG/AZ6JiKvKdhXSBpXiF9UGkgZLGpCW+wHHkz2HnQ2Upv/uWv9Su4wFZqUr9EbGf7bsPy+RPX8sr3/D2j8iJkXE0IjoIPs3PisixlNQ/asVbLt/kfX4rSB7JnRxAfGGkfWwLgKWlWKSPWN5BHgO+CswsIExf0t2i7iJ7FnPWZXikfV8/jq1xxKgs0nx70jnX0z2hR1SdvzFKf5y4KQGxD+S7PZ8MbAwvUYX1QZV4hfSBsAIYEGKsxS4pOy7+CRZJ9Xvgb5p+45pfWXaP6xJ8Wel+i8F7uT9nvqGfwfLynIM7/fCF1L/Si//KaeZWU7tcAtvZtYSTqBmZjk5gZqZ5eQEamaWkxOomVlOTqBmZjk5gZqZ5fR/t0jZ5Mif3pUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Premium ==========\n",
            "Smiths     1515\n",
            "Kettle     1512\n",
            "Doritos    1065\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Budget ==========\n",
            "Kettle     3320\n",
            "Smiths     3093\n",
            "Doritos    2351\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Mainstream ==========\n",
            "Kettle     2019\n",
            "Smiths     1835\n",
            "Doritos    1449\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Premium ==========\n",
            "Kettle     2947\n",
            "Smiths     2042\n",
            "Doritos    1958\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Budget ==========\n",
            "Kettle     3065\n",
            "Smiths     2098\n",
            "Doritos    1954\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Mainstream ==========\n",
            "Kettle     2835\n",
            "Smiths     2180\n",
            "Doritos    2008\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Premium ==========\n",
            "Kettle     2216\n",
            "Smiths     1458\n",
            "Doritos    1409\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Budget ==========\n",
            "Kettle     2592\n",
            "Doritos    1742\n",
            "Smiths     1679\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Mainstream ==========\n",
            "Kettle     3386\n",
            "Smiths     2476\n",
            "Doritos    2320\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Premium ==========\n",
            "Kettle     1745\n",
            "Smiths     1442\n",
            "Doritos    1129\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Budget ==========\n",
            "Kettle     2743\n",
            "Smiths     2459\n",
            "Doritos    1996\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAABVCAYAAAAfUrzeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJ5UlEQVR4nO3dfYxcVRnH8e/PhdbKW1sKpCCyUItIAOu6IsYKKJFCIQEjERKIRTEoUIVE/ijUQDVGio38AcpLUbS8KE1EsQliW3mJpAboFspuC7SUWtSClFpTitW2tI9/3LNmXHamM3d2e+cOv08ymTvn3rnz3JzZJ/feM/scRQRmZta49xQdgJlZWTmBmpnl5ARqZpaTE6iZWU5OoGZmOe1VdABDZdy4cdHZ2Vl0GGbWZpYtW7YxIg4abF3bJNDOzk56enqKDsPM2oykV6qt8yW8mVlOTqBmZjk5gZqZ5dQ290D71m+mc8ZDRYdhVph1s88qOoR3nd2egUraKWm5pJWSnpP0LUkNnblKOlTSr9LyJElT8wZsZtYq6jkD/XdETAKQdDDwC2B/4Pp6PkDSXhHxKnBeapoEdAO/azxcM7PW0dCZZERsAC4FpivzXkk/k9Qn6VlJnwGQdLGkBZIeBR6R1ClphaQRwHeB89NZ7fmSxkp6UFKvpCclnZD2cUraZnna935DfOxmZk1p+B5oRKyV1AEcDFyUNcXxko4BFkk6Om3aBZwQEZskdab3bpd0HdAdEdMBJN0CPBsR50r6LHA32Vnq1cAVEbFE0r7Af5o6UjOzIdbsKPxk4F6AiHgReAXoT6CLI2JTnfu4J+3jUeBASfsDS4CbJH0TGB0Rbw98o6RLJfVI6tm5dXOTh2Jm1piGE6iko4CdwIbdbPqvXBElETEb+CowCliSznAHbjM3IrojorvjfQc083FmZg1rdDT9IOB24EeRlbJ/ArgwrTsa+ACwaje72QJU3s+s3MepwMaIeFPShIjoi4gbgaXAOxKomVmR6rkHOkrScmBv4G2yy+2b0rpbgdsk9aV1F0fENkm19vcYMCPt8wZgFnCXpF5gKzAtbXdVGpTaBawEHm7kwMzMhpvaZU6k7u7ucDERMxtqkpZFRPdg6/yvnGZmOTmBmpnl5ARqZpaTE6iZWU5OoGZmOTmBmpnl5ARqZpaTE6iZWU6uSG9mhSl7Ff26z0AlzUxV6XtTjc5P1Pm+qtXoJc2SdHXjYZuZFa+uM1BJnwTOBrrS/7qPA0bU815XozezdlXvGeh4sipJ2wAiYmNEvCppnaQb0hlpj6QuSQslvSzp6wC1qtGnfR8r6XFJa1PtTyTtI+mhNAfTioptzcxaRr0JdBFwuKTVkm6VdErFur+kOZOeAH5OdrZ5EvCdyh1ExHbgOmB+REyKiPlp1THAFOBE4HpJewNnAK9GxEci4jjg9/kOz8xs+NSVQCPiLeBjZPMhvQHMl3RxWr0gPfcBT0XEloh4A9gmaXQdu38oIrZFxEayIs2HpH19TtKNkj4dEYOWm3dFejMrUt2DSBGxMyIej4jrgenAF9Kqbel5V8Vy/+t67rFWvmcnsFdErCabU6kP+F6aR2mwmFyR3swKU1cClfQhSRMrmiaRzX/UqIHV6Kt93qHA1oi4F5hDlkzNzFpKvb8D3Re4JV2Svw2sIbucP7vBzxtYjb6a44E5knYBO4DLGvwcM7Nh54r0ZmY1uCK9mdkwcAI1M8vJCdTMLCcnUDOznJxAzcxycgI1M8vJCdTMLCcnUDOznFyR3szeNYa6An5TZ6CS3qpYnprK3R1RY/trK5ZHS7q84nWnpBXNxGNmticNySW8pNOAm4EzI6JWkZFrK5ZHA5dX29DMrNU1nUAlnQzcCZwdES+ntoskPZ0qz98hqUPSbGBUarsPmA1MSK/nDNhnh6Q5kpamOZi+1mycZmZDrdl7oCOBB4FTI+JFAEkfBs4HPhUROyTdClwYETMkTU/V65HUCRw34HW/S4DNEfFxSSOBJZIWRcSfm4zXzGzINJtAdwB/Ikt4V6a208iq1y+VBDCKrNJ8I04HTpDUPxndAcBE4P8SqKRLycrq0bH/QTnCNzPLr9kEugv4IvCIpGsj4vuAgHkRcU0T+xXwjYhYWGujiJgLzAUYOX5ie9TlM7PSaPoeaERsBc4CLpR0CfAIcJ6kgwEkja0Ymd+RJo2D2tXpFwKX9W8r6WhJ+zQbq5nZUBqSUfiI2EQ2k+a3gQ+m50WSeoHFZNMiQ3a22Cvpvoj4B9m9zRUDB5GAnwDPA8+knzbdQRv9ZtXM2oMr0puZ1eCK9GZmw8AJ1MwsJydQM7OcnEDNzHJqm0EkSVuAVUXH0aRxwMaig2iC4y9e2Y+hFeM/IiIG/U+ddvpp0KpqI2VlIamnzMfg+ItX9mMoW/y+hDczy8kJ1Mwsp3ZKoHOLDmAIlP0YHH/xyn4MpYq/bQaRzMz2tHY6AzUz26OcQM3McmqLBCrpDEmrJK2RNKPoeKqRtE5SX5rGpCe1jZW0WNJL6XlMapekm9Mx9UrqKijmuyRtqJzwL0/Mkqal7V+SNK3g+GdJWp/6YbmkqRXrrknxr5I0paK9kO+YpMMlPSbpeUkrJV2Z2kvRBzXiL00f1BQRpX4AHcDLwFHACOA54Nii46oS6zpg3IC2HwAz0vIM4Ma0PBV4mKy49EnAUwXFfDLQBazIGzMwFlibnsek5TEFxj8LuHqQbY9N35+RwJHpe9VR5HeMrBRkV1reD1id4ixFH9SIvzR9UOvRDmegJwJrImJtRGwH7gfOKTimRpwDzEvL84BzK9rvjsyTwGhJ4wfbwXCKiD8CmwY0NxrzFGBxRGyKiH+S1Yg9Y/ijrxp/NecA90fEtsjm31pD9v0q7DsWEa9FxDNpeQvwAnAYJemDGvFX03J9UEs7JNDDgL9WvP4btTuoSEFWaHpZms8J4JCIeC0t/x04JC238nE1GnMrHsv0dIl7V//lLy0ev7KJFz8KPEUJ+2BA/FDCPhioHRJomUyOiC7gTOAKZVNC/09k1zCl+l1ZGWMGbgMmAJOA14AfFhvO7knaF3gAuCoi3qxcV4Y+GCT+0vXBYNohga4HDq94/f7U1nIiYn163gD8huyy5PX+S/P03D+DaSsfV6Mxt9SxRMTrEbEzInYBd5L1A7Ro/MrmBnsAuC8ifp2aS9MHg8Vftj6oph0S6FJgoqQjJY0ALgAWFBzTO0jaR9J+/ctkUzevIIu1f0R0GvDbtLwA+FIaVT0J2FxxyVa0RmNeCJwuaUy6VDs9tRViwL3kz5P1A2TxXyBppKQjyabSfpoCv2OSBPwUeCEibqpYVYo+qBZ/mfqgpqJHsYbiQTbyuJpslG5m0fFUifEospHD54CV/XECB5LNZPoS8AdgbGoX8ON0TH1Ad0Fx/5LsEmsH2X2nS/LEDHyFbEBgDfDlguO/J8XXS/ZHOL5i+5kp/lXAmUV/x4DJZJfnvcDy9Jhalj6oEX9p+qDWw//KaWaWUztcwpuZFcIJ1MwsJydQM7OcnEDNzHJyAjUzy8kJ1MwsJydQM7Oc/gs8ZKrLI4DPEQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Mainstream ==========\n",
            "Kettle     1789\n",
            "Smiths     1772\n",
            "Doritos    1309\n",
            "Name: Cleaned_Brand_Names, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RD6_-ENIqjc6"
      },
      "source": [
        "Every segment had Kettle as the most purchased brand. Every segment except \"YOUNG SINGLES/COUPLES Mainstream\" had Smiths as their second most purchased brand. \"YOUNG SINGLES/COUPLES Mainstream\" had Doritos as their second most purchased brand."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2sh0E1ONp4Af",
        "outputId": "07ff0906-6000-4bf8-95cb-4ee73e3ce40c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        }
      },
      "source": [
        "from mlxtend.frequent_patterns import apriori\r\n",
        "from mlxtend.frequent_patterns import association_rules\r\n",
        "\r\n",
        "temp = merged_data.reset_index().rename(columns = {\"index\": \"transaction\"})\r\n",
        "temp[\"Segment\"] = temp[\"LIFESTAGE\"] + ' - ' + temp['PREMIUM_CUSTOMER']\r\n",
        "segment_brand_encode = pd.concat([pd.get_dummies(temp[\"Segment\"]), pd.get_dummies(temp[\"Cleaned_Brand_Names\"])], axis=1)\r\n",
        "\r\n",
        "frequent_sets = apriori(segment_brand_encode, min_support=0.01, use_colnames=True)\r\n",
        "rules = association_rules(frequent_sets, metric=\"lift\", min_threshold=1)\r\n",
        "\r\n",
        "set_temp = temp[\"Segment\"].unique()\r\n",
        "rules[rules[\"antecedents\"].apply(lambda x: list(x)).apply(lambda x: x in set_temp)]"
      ],
      "execution_count": 73,
      "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>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(OLDER FAMILIES - Budget)</td>\n",
              "      <td>(Smiths)</td>\n",
              "      <td>0.087451</td>\n",
              "      <td>0.120162</td>\n",
              "      <td>0.011679</td>\n",
              "      <td>0.133549</td>\n",
              "      <td>1.111409</td>\n",
              "      <td>0.001171</td>\n",
              "      <td>1.015451</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(OLDER SINGLES/COUPLES - Budget)</td>\n",
              "      <td>(Kettle)</td>\n",
              "      <td>0.069504</td>\n",
              "      <td>0.155901</td>\n",
              "      <td>0.011573</td>\n",
              "      <td>0.166513</td>\n",
              "      <td>1.068064</td>\n",
              "      <td>0.000738</td>\n",
              "      <td>1.012731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(OLDER SINGLES/COUPLES - Premium)</td>\n",
              "      <td>(Kettle)</td>\n",
              "      <td>0.067038</td>\n",
              "      <td>0.155901</td>\n",
              "      <td>0.011128</td>\n",
              "      <td>0.165991</td>\n",
              "      <td>1.064716</td>\n",
              "      <td>0.000676</td>\n",
              "      <td>1.012097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(RETIREES - Mainstream)</td>\n",
              "      <td>(Kettle)</td>\n",
              "      <td>0.081055</td>\n",
              "      <td>0.155901</td>\n",
              "      <td>0.012785</td>\n",
              "      <td>0.157738</td>\n",
              "      <td>1.011779</td>\n",
              "      <td>0.000149</td>\n",
              "      <td>1.002180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(YOUNG SINGLES/COUPLES - Mainstream)</td>\n",
              "      <td>(Kettle)</td>\n",
              "      <td>0.078744</td>\n",
              "      <td>0.155901</td>\n",
              "      <td>0.014515</td>\n",
              "      <td>0.184329</td>\n",
              "      <td>1.182344</td>\n",
              "      <td>0.002239</td>\n",
              "      <td>1.034852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                            antecedents consequents  ...  leverage  conviction\n",
              "0             (OLDER FAMILIES - Budget)    (Smiths)  ...  0.001171    1.015451\n",
              "2      (OLDER SINGLES/COUPLES - Budget)    (Kettle)  ...  0.000738    1.012731\n",
              "4     (OLDER SINGLES/COUPLES - Premium)    (Kettle)  ...  0.000676    1.012097\n",
              "6               (RETIREES - Mainstream)    (Kettle)  ...  0.000149    1.002180\n",
              "8  (YOUNG SINGLES/COUPLES - Mainstream)    (Kettle)  ...  0.002239    1.034852\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4WN3nLy8q0bG"
      },
      "source": [
        "By looking at our a-priori analysis, we can conclude that Kettle is the brand of choice for most segment.\r\n",
        "\r\n",
        "Next, we'll find out the pack size preferences of different segments"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tgQ59sJJp39i",
        "outputId": "3106da0b-0e29-4cb7-e3a7-8b50d455b5af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "merged_pack = pd.concat([merged_data, pack_sizes.rename(\"Pack_Size\")], axis=1)\r\n",
        "\r\n",
        "for stage in merged_data[\"LIFESTAGE\"].unique():\r\n",
        "    for prem in merged_data[\"PREMIUM_CUSTOMER\"].unique():\r\n",
        "        print('==========',stage, '-', prem,'==========')\r\n",
        "        summary = merged_pack[(merged_pack[\"LIFESTAGE\"] == stage) & (merged_pack[\"PREMIUM_CUSTOMER\"] == prem)][\"Pack_Size\"].value_counts().head(3).sort_index()\r\n",
        "        print(summary)\r\n",
        "        plt.figure()\r\n",
        "        summary.plot.barh(figsize=(5,1))\r\n",
        "        plt.show()"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Premium ==========\n",
            "134.0     537\n",
            "150.0     961\n",
            "175.0    1587\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Budget ==========\n",
            "134.0     832\n",
            "150.0    1439\n",
            "175.0    2262\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAIwElEQVR4nO3db4xUVxnH8e9PaonW1kIhBBHdpWITmpiVbhtetKQ1Ci2+oNVG8Q1EUSQtRl+YlIZESdQENFVjYjGtbUoNAYytlWiNIGn0jaXu2i1/2m75023KSiGFCk00Vejji3smXJaZu7uzw96Z2d8nmeydc//sefZOnsyZM/scRQRmZlbde8rugJlZM3OSNDMr4CRpZlbASdLMrICTpJlZgcvK7sBoTJs2LTo6Osruhpm1md7e3jcjYnq1fS2VJDs6Oujp6Sm7G2bWZiS9Vmufh9tmZgWcJM3MCjhJmpkVaKnPJPcNnqZj7R/K7oaZNbGBDZ9t6PWGfScp6VFJJyTtz7Vtl9SXHgOS+lJ7h6T/5Pb9osY1p0raJelg+jmlcSGZmTXOSIbbjwG35xsi4osR0RURXcATwJO53Ycr+yJidY1rrgV2R8RcYHd6bmbWdIZNkhHxV+BUtX2SBHwB2DrK37sU2Jy2NwN3jvJ8M7NxMdaJm1uA4xFxMNfWKel5SX+RdEuN82ZExLG0/QYwY4z9MDO7JMY6cfMlLnwXeQz4SESclHQD8JSk6yPiTK0LRERIqlnUUtIqYBXApKuqfiHezOySqfudpKTLgM8B2yttEfFORJxM273AYeDjVU4/Lmlmus5M4ESt3xMRD0VEd0R0T3r/B+vtrplZXcYy3P408HJEHK00SJouaVLangPMBY5UOXcHsCJtrwB+N4Z+mJldMiP5CtBW4G/AdZKOSlqZdi3j4gmbhcDe9JWg3wCrI+JUus4vJXWn4zYAn5F0kCzZbhh7KGZmjadWWuOmu7s7XODCzBpNUm9EdFfb539LNDMr4CRpZlbASdLMrICTpJlZASdJM7MCTpJmZgWcJM3MCjhJmpkVcGVyswZqdFVsK1+9lcnXSxrMVSBfktt3v6RDkvolLa5xzU5Je9Jx2yVd3phwzMwaq67K5MlPchXInwaQNI/sf7qvT+c8WCl4McTGdP7HgLeAlVWOMTMr3Zgqk1exFNiWSqa9ChwCbsofkKqZf4qsAAa4MrmZNbGxTNyskbQ3DccrC3nNAl7PHXM0teVdA/wrIs4WHGNm1hTqTZKbgGuBLrJq5A80rEdDSFolqUdSz7l/n75Uv8bMrKq6kmREHI+IcxHxLvAw54fUg8Ds3KEfTm15J4GrU2XzWsfkf5crk5tZaepKkpWlF5K7gMrM9w5gmaTJkjrJKpM/lz83sgKWzwB3pyZXJjezpjXs9yRTZfJbgWmSjgLfBW6V1AUEMAB8HSAiDkj6NfAicBa4NyLOpes8DXw1Iv4J3Adsk/R94HngkQbHZWbWEK5MbmYTniuTm5nVyUnSzKyAk6SZWQEnSTOzAk6SZmYFnCTNzAo4SZqZFXCSNDMr4MrkNmquvm0TyYjeSdaoTv69VCqtT9JOSR8acs6Nks5KuvviK4KkGyTtS9XJf5bqTJqZNZWRDrcf4+Lq5D+KiE9ERBfwe+A7lR2pGvlGYGfBNTcBXyMrgjG3yvXNzEo3oiRZrTp5RJzJPb2CrNhFxTeAJ4AT1a6XqghdFRHPpqpAj+Pq5GbWhMb0maSkHwDLgdPAbaltFln5tNuAG2ucOousInmFq5ObWVMa0+x2RKyLiNnAFmBNav4pcF8qyDtmrkxuZmVq1FeAtgCfT9vdZLUiB8gK6z4oaehQepCsInlFzerkrkxuZmWqO0lKmpt7uhR4GSAiOiOiIyI6yFZEvCcinsqfGxHHgDOSFqRZ7eW4OrmZNaERfSZZozr5EknXAe8CrwGrR3CdvjQbDnAP2az5+4A/poeZWVNxZXIzm/BcmdzMrE5OkmZmBZwkzcwKOEmamRVoqYkbSW8D/WX3Y5xMA94suxPjYKLECY61mX00IqZX29FSpdKA/lozUO1GUs9EiHWixAmOtVV5uG1mVsBJ0sysQKslyYfK7sA4miixTpQ4wbG2pJaauDEzG2+t9k7SzGxcOUmamRVoiSQp6XZJ/WnRsLVl96cRJA2khdD6JPWktqmSdkk6mH5OSe1Ki6UdSouvzS+398VqLBw36tgkrUjHH5S0ooxYhlMj1vWSBtO97ZO0JLfv/hRrv6TFufamfo1Lmi3pGUkvSjog6ZupvS3v6wUioqkfwCTgMDAHuBx4AZhXdr8aENcAMG1I2w+BtWl7LbAxbS8hKyUnYAGwp+z+DxPbQmA+sL/e2ICpwJH0c0ranlJ2bCOMdT3w7SrHzkuv38lAZ3pdT2qF1zgwE5iftq8EXknxtOV9zT9a4Z3kTcChiDgSEf8FtpEV+W1HS4HNaXsz5xdHWwo8HplngavTYmpNKaosHMfoY1sM7IqIUxHxFrCLJlxRs0astSwFtkXEOxHxKnCI7PXd9K/xiDgWEf9I228DL5GtS9WW9zWvFZLkLOD13PN2WTQsgJ2SeiWtSm0zIqvaDvAGMCNtt8PfYLSxtXrMa9Iw89HKEJQ2iVVSB/BJYA8T4L62QpJsVzdHxHzgDuBeSQvzOyMbm7Tl97PaObZkE3At0AUcAx4otzuNI+kDZMtFfysuXFa6be9rKyTJQWB27nnNRcNaSUQMpp8ngN+SDbmOV4bR6Wdl3fJ2+BuMNraWjTkijkfEuchWDH2Y7N5Ci8cq6b1kCXJLRDyZmtv+vrZCkvw7MFdSp6TLgWXAjpL7NCaSrpB0ZWUbWATsJ4urMtu3gvOLo+0AlqcZwwXA6dwQp1WMNrY/AYskTUnD1UWprekN+bz4LrJ7C1msyyRNltQJzAWeowVe45IEPAK8FBE/zu1q//ta9szRSB5kM2WvkM0Ariu7Pw2IZw7ZDOYLwIFKTMA1wG7gIPBnYGpqF/DzFP8+oLvsGIaJbyvZMPN/ZJ85rawnNuArZJMbh4Avlx3XKGL9VYplL1mymJk7fl2KtR+4I9fe1K9x4GayofReoC89lrTrfc0//G+JZmYFWmG4bWZWGidJM7MCTpJmZgWcJM3MCjhJmpkVcJI0MyvgJGlmVuD/WOI+lyHo4tIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG SINGLES/COUPLES - Mainstream ==========\n",
            "134.0    2315\n",
            "150.0    3159\n",
            "175.0    4928\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Premium ==========\n",
            "134.0     781\n",
            "150.0    1285\n",
            "175.0    2034\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Budget ==========\n",
            "134.0     449\n",
            "150.0     821\n",
            "175.0    1256\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== MIDAGE SINGLES/COUPLES - Mainstream ==========\n",
            "134.0    1159\n",
            "150.0    1819\n",
            "175.0    2912\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Premium ==========\n",
            "134.0    165\n",
            "150.0    245\n",
            "175.0    371\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJz0lEQVR4nO3dX4wdZRnH8e/PQgvyr5Q2TS3V3WLFgCFrWQgmQABFoF5UtNF6Qy/QioDRCxNKSBCjJKBBDIlAQLBFCS0CYqOYtEKjN9K6laV/gNICJXQtbShSMBq05fFi3tMO25nZZfecM+fA75Oc7Jx3/rzPvj15OjPv2WcUEZiZWbEP1R2AmVknc5I0M6vgJGlmVsFJ0sysgpOkmVmFw+oO4L2YOnVq9PT01B2Gmb3PrF+//rWImFa0rquSZE9PDwMDA3WHYWbvM5JeLlvny20zswpOkmZmFZwkzcwqdNU9yY1De+lZ8oe6wzCzDrb9pi809XgjnklKulfSbkmbcm0rJA2m13ZJg6m9R9J/cuvuLDnmFEmrJW1NP49v3q9kZtY8o7ncXgpcnG+IiK9GRF9E9AEPA4/kVr/QWBcRV5QccwnweETMAR5P783MOs6ISTIi/gK8XrROkoCvAA+8x37nA8vS8jLgi+9xfzOzthjvxM05wK6I2Jpr65X0lKQ/SzqnZL/pEbEzLb8KTB9nHGZmLTHeiZuv8e6zyJ3ARyNij6TTgUclnRoRb5YdICJCUmlRS0mLgcUAE44t/EK8mVnLjPlMUtJhwJeAFY22iHg7Ivak5fXAC8AnCnbfJWlGOs4MYHdZPxFxV0T0R0T/hA8fN9ZwzczGZDyX258DnouIHY0GSdMkTUjLs4E5wIsF+64EFqXlRcDvxhGHmVnLjOYrQA8AfwVOlrRD0uVp1UIOnbA5F9iQvhL0EHBFRLyejvMLSf1pu5uACyVtJUu2N43/VzEzaz510zNu+vv7wwUuzKzZJK2PiP6idf6zRDOzCk6SZmYVnCTNzCo4SZqZVXCSNDOr4CRpZlbBSdLMrIKTpJlZBVcmN2uDZlfLtvYZa2XyGyQN5SqQz8utu1bSNklbJF1UcsxeSWvTdiskTWzOr2Nm1lxjqkye3JqrQP4YgKRTyP6m+9S0z+2NghfD3Jz2/zjwT+Dygm3MzGo3rsrkBeYDy1PJtJeAbcCZ+Q1SNfMLyApggCuTm1kHG8/EzdWSNqTL8caDvGYCr+S22ZHa8k4A3oiIfRXbmJl1hLEmyTuAk4A+smrktzQtomEkLZY0IGlg/7/3tqobM7NCY0qSEbErIvZHxDvA3Ry8pB4CZuU2PTG15e0BJqfK5mXb5PtyZXIzq82YkmTj0QvJpUBj5nslsFDSJEm9ZJXJ1+X3jayA5RpgQWpyZXIz61gjfk8yVSY/D5gqaQfwfeA8SX1AANuBbwJExGZJDwLPAPuAqyJifzrOY8DXI+IfwDXAckk/Ap4C7mny72Vm1hSuTG5mH3iuTG5mNkZOkmZmFZwkzcwqOEmamVVwkjQzq+AkaWZWwUnSzKyCk6SZWQVXJrfauWq3dbJRnUmWVCf/YSqVNihplaSPDNvnDEn7JC049Igg6XRJG1N18ttSnUkzs44y2svtpRxanfwnEXFaRPQBvweub6xI1chvBlZVHPMO4BtkRTDmFBzfzKx2o0qSRdXJI+LN3NujyIpdNHwbeBjYXXS8VEXo2Ih4MlUFug9XJzezDjSue5KSbgQuA/YC56e2mWTl084HzijZdSZZRfIGVyc3s440rtntiLguImYB9wNXp+afAdekgrzj5srkZlanZn0F6H7gy2m5n6xW5Haywrq3Sxp+KT1EVpG8obQ6uSuTm1mdxpwkJc3JvZ0PPAcQEb0R0RMRPWRPRLwyIh7N7xsRO4E3JZ2VZrUvw9XJzawDjeqeZEl18nmSTgbeAV4GrhjFcQbTbDjAlWSz5kcCf0wvM7OO4srkZvaB58rkZmZj5CRpZlbBSdLMrIKTpJlZha6auJH0FrCl7jhypgKv1R1E0kmxgOMZieMpV0csH4uIaUUruqpUGrClbAaqDpIGOiWeTooFHM9IHE+5TooFfLltZlbJSdLMrEK3Jcm76g5gmE6Kp5NiAcczEsdTrpNi6a6JGzOzduu2M0kzs7ZykjQzq9AVSVLSxZK2pIeGLakphu3pwWWDkgZS2xRJqyVtTT+Pb2H/RQ9jK+xfmdvSeG2QNLdN8dwgaSiN0aCkebl116Z4tki6qMmxzJK0RtIzkjZL+k5qr2V8KuKpa3yOkLRO0tMpnh+k9l5Ja1O/KyRNTO2T0vttaX1Pm+JZKuml3Pj0pfaWf54rRURHv4AJwAvAbGAi8DRwSg1xbAemDmv7MbAkLS8Bbm5h/+cCc4FNI/UPzCMrPSfgLGBtm+K5AfhewbanpH+3SUBv+vec0MRYZgBz0/IxwPOpz1rGpyKeusZHwNFp+XBgbfq9HwQWpvY7gW+l5SuBO9PyQmBFk8enLJ6lwIKC7Vv+ea56dcOZ5JnAtoh4MSL+CywnK/LbCeYDy9LyMlr4MLMoeBhbRf/zgfsi8yQwWdnD11odT5n5wPKIeDsiXgK2kf27NiuWnRHx97T8FvAs2TOTahmfinjKtHp8IiL+ld4enl4BXEBWGBsOHZ/GuD0EfDYVx251PGVa/nmu0g1JcibwSu59XQ8NC2CVpPWSFqe26ZFVWQd4FZje5pjK+q9zzK5Ol0T35m4/tC2edGn4abKzk9rHZ1g8UNP4SJogaZDsCaaryc5W34iIfQV9Hognrd8LnNDKeCKiMT43pvG5VdKk4fEUxNpy3ZAkO8XZETEXuAS4StK5+ZWRXRfU9n2quvtP7gBOAvqAncAt7exc0tFkjzL+brz7kce1jE9BPLWNT0Tsj+ypACeSnaV+sl19jyYeSZ8Crk1xnQFMAa6pMcQDuiFJDgGzcu9LHxrWShExlH7uBn5L9kHb1TjtTz8LnzPeQmX91zJmEbErffjfAe7m4CVjy+ORdDhZQro/Ih5JzbWNT1E8dY5PQ0S8AawBPkN22dqo35Dv80A8af1xwJ4Wx3Nxuk0REfE28EtqGJ8i3ZAk/wbMSTNxE8luJK9sZwCSjpJ0TGMZ+DywKcWxKG22iPY/zKys/5XAZWlW8Cxgb+6ys2WG3Se6lGyMGvEsTLOmvcAcYF0T+xVwD/BsRPw0t6qW8SmLp8bxmSZpclo+EriQ7D7pGrInmsKh49MYtwXAE+lMvJXxPJf7D01k90fz49P2z/MB7ZwlGuuLbHbrebL7KNfV0P9sstnHp4HNjRjI7tM8DmwF/gRMaWEMD5Bdov2P7J7M5WX9k80C/jyN10agv03x/Cr1t4Hsgz0jt/11KZ4twCVNjuVsskvpDcBges2ra3wq4qlrfE4Dnkr9bgKuz32u15FNFP0GmJTaj0jvt6X1s9sUzxNpfDYBv+bgDHjLP89VL/9ZoplZhW643DYzq42TpJlZBSdJM7MKTpJmZhWcJM3MKjhJmplVcJI0M6vwf7q0ZzOmxLQpAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Budget ==========\n",
            "134.0    309\n",
            "150.0    448\n",
            "175.0    763\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== NEW FAMILIES - Mainstream ==========\n",
            "134.0    224\n",
            "150.0    384\n",
            "175.0    579\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Premium ==========\n",
            "134.0    1014\n",
            "150.0    1750\n",
            "175.0    2747\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Budget ==========\n",
            "134.0    1996\n",
            "150.0    3708\n",
            "175.0    5662\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
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          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJdUlEQVR4nO3de4wdZRnH8e/PlpY7tLQ2tVR3qxVTElJhQYhACpGL1YiXRktMaBStiBiNf0gbEtSoCWhUNJGbYsAEoSgIDWKgVqL/aHHXFiiXtS2W0FraUKQ10RhbHv+Y58CwnDNdztndcwZ+n+RkZ965nGe602dn5j3neRURmJlZc2/qdgBmZr3MSdLMrIKTpJlZBSdJM7MKTpJmZhUmdzuA12LGjBnR19fX7TDM7HVmaGjouYiY2WxZrZJkX18fg4OD3Q7DzF5nJD3daplvt83MKjhJmplVcJI0M6tQq2eSj27fQ9+K33Q7DDPrYVuv+sCY7u+AV5KSfiZpl6SNpbZVkjbka6ukDdneJ+k/pWXXt9jndElrJG3Kn9PG7pDMzMbOaG63bwbOLzdExCciYmFELATuBO4qLd7SWBYRl7TY5wpgbUTMB9bmvJlZzzlgkoyIPwLPN1smScDHgdte4/teANyS07cAH36N25uZTYhOO27OAHZGxKZSW7+k9ZL+IOmMFtvNiogdOf0sMKvDOMzMxkWnHTcX8sqryB3AWyNit6STgLslHR8Re1vtICJCUsuilpKWA8sBJh3Z9APxZmbjpu0rSUmTgY8CqxptEfHfiNid00PAFuCdTTbfKWl27mc2sKvV+0TEjRExEBEDkw49qt1wzcza0snt9vuAJyNiW6NB0kxJk3J6HjAfeKrJtquBZTm9DLingzjMzMbNaD4CdBvwJ+A4SdskXZyLlvLqDpszgUfyI0G/Ai6JiOdzPz+VNJDrXQWcI2kTRbK9qvNDMTMbe6rTGDcDAwPhAhdmNtYkDUXEQLNl/lqimVkFJ0kzswpOkmZmFZwkzcwqOEmamVVwkjQzq+AkaWZWwUnSzKyCK5ObjbOxrpRtE6vdyuRfl7S9VIF8cWnZSkmbJQ1LOq/FPvslrcv1VkmaMjaHY2Y2ttqqTJ5+UKpAfh+ApAUU3+k+Pre5tlHwYoSrc/t3AP8ELm6yjplZ13VUmbyJC4Dbs2Ta34HNwCnlFbKa+dkUBTDAlcnNrId10nFzmaRH8na8MZDXHOCZ0jrbsq3sGOCFiNhXsY6ZWU9oN0leB7wdWEhRjfx7YxbRCJKWSxqUNLj/33vG623MzJpqK0lGxM6I2B8RLwI/4eVb6u3A3NKqx2Zb2W7g6Kxs3mqd8nu5MrmZdU1bSbIx9EL6CNDo+V4NLJU0VVI/RWXyh8rbRlHA8kFgSTa5MrmZ9awDfk4yK5MvAmZI2gZ8DVgkaSEQwFbgcwAR8ZikO4DHgX3AFyJif+7nPuAzEfEP4HLgdknfAtYDN43xcZmZjQlXJjezNzxXJjcza5OTpJlZBSdJM7MKTpJmZhWcJM3MKjhJmplVcJI0M6vgJGlmVsGVya2Sq2rbG92oriRbVCf/ZpZK2yDpAUlvGbHNyZL2SVry6j2CpJMkPZrVyX+UdSbNzHrKaG+3b+bV1cm/GxEnRMRC4F7gysaCrEZ+NfBAxT6vAz5LUQRjfpP9m5l13aiSZLPq5BGxtzR7GEWxi4YvAncCu5rtL6sIHRkRf86qQD/H1cnNrAd19ExS0reBi4A9wFnZNoeifNpZwMktNp1DUZG8wdXJzawnddS7HRFXRMRc4Fbgsmy+Brg8C/J2zJXJzaybxuojQLcCH8vpAYpakVspCuteK2nkrfR2iorkDS2rk7syuZl1U9tJUtL80uwFwJMAEdEfEX0R0UcxIuKlEXF3eduI2AHslXRq9mpfhKuTm1kPGtUzyRbVyRdLOg54EXgauGQU+9mQveEAl1L0mh8C/DZfZmY9xZXJzewNz5XJzcza5CRpZlbBSdLMrIKTpJlZhVp13Ej6FzDc7TjaNAN4rttBtMmxd0ddY69j3G+LiJnNFtSqVBow3KoHqtdJGnTsE8+xT7y6xt2Kb7fNzCo4SZqZVahbkryx2wF0wLF3h2OfeHWNu6laddyYmU20ul1JmplNKCdJM7MKtUiSks6XNJyDhq3odjzQcnC06ZLWSNqUP6dlu3Kws805eNqJpW2W5fqbJC2boNjnSnpQ0uOSHpP0pbrEL+lgSQ9Jejhj/0a290talzGukjQl26fm/OZc3lfa18psH5Z03njHnu85SdJ6SffWKe583605eN8GSYPZ1vPnTMcioqdfwCRgCzAPmAI8DCzogbjOBE4ENpbavgOsyOkVwNU5vZiiFJyAU4F12T4deCp/TsvpaRMQ+2zgxJw+AvgbsKAO8WcMh+f0QcC6jOkOYGm2Xw98PqcvBa7P6aXAqpxekOfSVKA/z7FJE/Bv/xXgF8C9OV+LuPO9twIzRrT1/DnT8XF3O4BR/GJOA+4vza8EVnY7roylb0SSHAZm5/Rsig+/A9wAXDhyPeBC4IZS+yvWm8DjuAc4p27xA4cCfwXeQ/ENj8kjzxngfuC0nJ6c62nkeVRebxzjPRZYC5xNMcKo6hB36b2aJclanTPtvOpwuz0HeKY038uDhs2Kouo6wLPArJxudQxdP7a8jXs3xRVZLeLPW9YNFKNxrqG4mnohIvY1ieOlGHP5HuCYLsV+DfBVikLVZBx1iLshgAckDUlanm21OGc6UbevJdZGRISknv58laTDKYb+/XJE7C1G0ij0cvwRsR9YKOlo4NfAu7oc0gFJ+iCwKyKGJC3qdjxtOj0itkt6M7BG0pPlhb18znSiDleS24G5pfmWg4b1gJ0qxhRvjC3eGHe81TF07dgkHUSRIG+NiLuyuTbxA0TEC8CDFLepR0tq/NEvx/FSjLn8KGA3Ex/7e4EPqRgg73aKW+4f1iDul0TE9vy5i+KP0ynU7JxpRx2S5F+A+dkLOIXiIfbqLsfUymqg0Vu3jJcHN1sNXJQ9fqcCe/IW5X7gXEnTslfw3GwbVyouGW8CnoiI79cpfkkz8woSSYdQPEt9giJZLmkRe+OYlgC/j+Jh2GpgafYi9wPzgYfGK+6IWBkRx0YxQN7SjOOTvR53g6TDJB3RmKb4XW+kBudMx7r9UHSUD4wXU/TAbgGu6HY8GdNtwA7gfxTPVS6meGa0FtgE/A6YnusK+HHG/ygwUNrPp4HN+frUBMV+OsXzpUeADflaXIf4gROA9Rn7RuDKbJ9HkSw2A78Epmb7wTm/OZfPK+3rijymYeD9E3juLOLl3u1axJ1xPpyvxxr/D+twznT68tcSzcwq1OF228ysa5wkzcwqOEmamVVwkjQzq+AkaWZWwUnSzKyCk6SZWYX/A40wygnMWAJjAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER FAMILIES - Mainstream ==========\n",
            "134.0    1234\n",
            "150.0    2261\n",
            "175.0    3489\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Premium ==========\n",
            "134.0    1744\n",
            "150.0    2854\n",
            "175.0    4382\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Budget ==========\n",
            "134.0    1843\n",
            "150.0    2899\n",
            "175.0    4535\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== OLDER SINGLES/COUPLES - Mainstream ==========\n",
            "134.0    1720\n",
            "150.0    2875\n",
            "175.0    4422\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Premium ==========\n",
            "134.0    1331\n",
            "150.0    2015\n",
            "175.0    3232\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJtElEQVR4nO3da4xcZR3H8e/PQgtybWnT1FLdLVZMSUgtC8EECKDc6ouKNlrftFG0ImD0hQklJIhRE9CgxkQgIIRiCC0CYqMYW7HRN9K6laWUS+kCJXQtbShQMBq05e+L8ww9bGfO7s7tnIHfJ5nMmedc5r/Pzv5zznlm/48iAjMzq+8DZQdgZlZlTpJmZgWcJM3MCjhJmpkVcJI0MytwWNkBTMT06dOjr6+v7DDM7D1m8+bNr0TEjHrreipJ9vX1MTg4WHYYZvYeI+nFRut8uW1mVsBJ0sysgJOkmVmBnron+cTIPvpW/r7sMMyswnbc8Jm2Hm/MM0lJd0raI2lrrm2NpKH02CFpKLX3SfpPbt2tDY45TdJ6SdvT89T2/UhmZu0znsvtu4CL8w0R8cWIWBARC4AHgAdzq5+rrYuIyxsccyXwSETMAx5Jr83MKmfMJBkRfwVerbdOkoAvAPdO8H0XA6vS8irgsxPc38ysK1oduDkb2B0R23Nt/ZIek/QXSWc32G9mROxKyy8DM1uMw8ysI1oduPkS7z6L3AV8OCL2SjoNeEjSKRHxRqMDRERIaljUUtIKYAXApGPrfiHezKxjmj6TlHQY8DlgTa0tIt6KiL1peTPwHPCxOrvvljQrHWcWsKfR+0TEbRExEBEDkz54XLPhmpk1pZXL7U8Dz0TEzlqDpBmSJqXlucA84Pk6+64Flqfl5cBvW4jDzKxjxvMVoHuBvwEnS9op6bK0aimHDticA2xJXwm6H7g8Il5Nx/mlpIG03Q3ABZK2kyXbG1r/UczM2k+9NMfNwMBAuMCFmbWbpM0RMVBvnf8t0cysgJOkmVkBJ0kzswJOkmZmBZwkzcwKOEmamRVwkjQzK+AkaWZWwJXJzVrQ7irYVj3NVia/XtJIrgL5oty6ayQNS9om6aIGx+yXtDFtt0bS5Pb8OGZm7dVUZfLkp7kK5A8DSJpP9j/dp6R9bq4VvBjlxrT/R4HXgMvqbGNmVrqWKpPXsRhYnUqmvQAMA2fkN0jVzM8nK4ABrkxuZhXWysDNVZK2pMvx2kRes4GXctvsTG15JwCvR8T+gm3MzCqh2SR5C3ASsICsGvlNbYtoFEkrJA1KGjzw732dehszs7qaSpIRsTsiDkTE28DtHLykHgHm5DY9MbXl7QWOT5XNG22Tfy9XJjez0jSVJGtTLySXArWR77XAUklTJPWTVSbflN83sgKWG4AlqcmVyc2sssb8nmSqTH4uMF3STuC7wLmSFgAB7AC+DhART0q6D3gK2A9cGREH0nEeBr4aEf8ErgZWS/oB8BhwR5t/LjOztnBlcjN733NlcjOzJjlJmpkVcJI0MyvgJGlmVsBJ0sysgJOkmVkBJ0kzswJOkmZmBVyZ3DrKlbut143rTLJBdfLvp1JpQ5LWSfrQqH1Ol7Rf0pJDjwiSTpP0RKpO/vNUZ9LMrFLGe7l9F4dWJ/9xRJwaEQuA3wHX1VakauQ3AusKjnkL8DWyIhjz6hzfzKx040qS9aqTR8QbuZdHkRW7qPkm8ACwp97xUhWhYyPi0VQV6G5cndzMKqile5KSfggsA/YB56W22WTl084DTm+w62yyiuQ1rk5uZpXU0uh2RFwbEXOAe4CrUvPPgKtTQd6WuTK5mZWpXV8Bugf4fFoeIKsVuYOssO7NkkZfSo+QVSSvaVid3JXJzaxMTSdJSfNyLxcDzwBERH9E9EVEH9mMiFdExEP5fSNiF/CGpDPTqPYyXJ3czCpoXPckG1QnXyTpZOBt4EXg8nEcZyiNhgNcQTZqfiTwh/QwM6sUVyY3s/c9VyY3M2uSk6SZWQEnSTOzAk6SZmYFemrgRtKbwLay4xjDdOCVsoMYg2NsD8fYHlWI8SMRMaPeip4qlQZsazQCVRWSBh1j6xxjezjG1vly28ysgJOkmVmBXkuSt5UdwDg4xvZwjO3hGFvUUwM3Zmbd1mtnkmZmXeUkaWZWoCeSpKSLJW1Lk4atLDmWHWkCsyFJg6ltmqT1kran56mpXWmSs+E0adrCDsZVb7K2CcclaXnafruk5V2I8XpJI6k/hyQtyq27JsW4TdJFufaOfB4kzZG0QdJTkp6U9K3UXpl+LIixMv2Yjn2EpE2SHk9xfi+190vamN5zjaTJqX1Kej2c1veNFX/XRESlH8Ak4DlgLjAZeByYX2I8O4Dpo9p+BKxMyyuBG9PyIrIScALOBDZ2MK5zgIXA1mbjAqYBz6fnqWl5aodjvB74Tp1t56ff9RSgP30GJnXy8wDMAham5WOAZ1MclenHghgr04/pfQUcnZYPBzamProPWJrabwW+kZavAG5Ny0uBNUXxd+rvqN6jF84kzwCGI+L5iPgvsJqsyG+VLAZWpeVVHJzUbDFwd2QeBY5XNgla20WdydqaiOsiYH1EvBoRrwHraeMslg1ibGQxsDoi3oqIF4Bhss9Cxz4PEbErIv6Rlt8Eniabe6ky/VgQYyNd78cUW0TEv9LLw9MjgPPJinHDoX1Z6+P7gU9JUkH8XdMLSXI28FLuddmThgWwTtJmSStS28zIqq0DvAzMTMtlxz7RuMqK96p0uXpn7VK27BjT5d4nyM6AKtmPo2KEivWjpEmShshmTV1Pdhb4ekTsr/Oe78ST1u8DTuhGnGPphSRZNWdFxELgEuBKSefkV0Z2jVC571VVNS6y+ddPAhYAu4Cbyg0HJB1NNiXyt+PdUydXph/rxFi5foyIA5HNRHAi2dnfx0sOqSm9kCRHgDm51w0nDeuGiBhJz3uA35D98nfXLqPTc22+8bJjn2hcXY83InanP6a3gds5eClVSoySDidLPvdExIOpuVL9WC/GqvVjXkS8DmwAPkl2S6JWMyL/nu/Ek9YfB+ztZpyN9EKS/DswL42KTSa7qbu2jEAkHSXpmNoycCGwNcVTG8FczsFJzdYCy9Io6JnAvtxlWzdMNK4/AhdKmpou1y5MbR0z6h7tpWT9WYtxaRr17AfmAZvo4Och3QO7A3g6In6SW1WZfmwUY5X6McUzQ9LxaflI4AKy+6cbyGZRhUP7stbHS4A/p7P2RvF3TzdHiZp9kI0iPkt2T+PaEuOYSzbS9jjwZC0WsnsnjwDbgT8B0+LgCN8vUtxPAAMdjO1essus/5Hdt7msmbiAr5DdHB8GvtyFGH+VYthC9gcxK7f9tSnGbcAlnf48AGeRXUpvAYbSY1GV+rEgxsr0Yzr2qcBjKZ6twHW5v6FNqV9+DUxJ7Uek18Np/dyx4u/Ww/+WaGZWoBcut83MSuMkaWZWwEnSzKyAk6SZWQEnSTOzAk6SZmYFnCTNzAr8H4uWNO2NqnGSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Budget ==========\n",
            "134.0    1517\n",
            "150.0    2381\n",
            "175.0    3768\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAKIUlEQVR4nO3df4wcZR3H8ffHAgX52dKmqaV6LVYMNeQsB8EECKAGKCb1B9H6D42iFQtGTUwoIUGMkhQIgZgIBBRbkNDySySKCRWJmihtrnKUgpQebQk9SxsKFIwGbfn6xzzbzl13p3e7szt78Hklm332mdl5vvvs3rc7M9vvKCIwM7P6PlB1AGZm3cxJ0sysgJOkmVkBJ0kzswJOkmZmBQ6pOoCxmDJlSvT09FQdhpm9x6xbt+61iJhab9m4SpI9PT309/dXHYaZvcdIernRMu9um5kVcJI0MyvgJGlmVmBcHZN8dmg3PUt/V3UYZtbFti67qNTtHfSbpKS7JO2UtCHXt0rSQLptlTSQ+nsk/Se37PYG25wsabWkTel+UnkvycysPKPZ3V4OXJDviIivRERvRPQCDwEP5xa/VFsWEZc12OZS4ImImAM8kR6bmXWdgybJiPgz8Hq9ZZIEfBm4b4zjLgBWpPYK4PNjfL6ZWUe0euLmLGBHRGzK9c2S9LSkP0k6q8HzpkXE9tR+FZjWYhxmZm3R6ombrzL8W+R24MMRsUvSqcAjkuZGxFuNNhARIalhUUtJi4HFABOOqfuDeDOztmn6m6SkQ4AvAqtqfRHxTkTsSu11wEvAx+o8fYek6Wk704GdjcaJiDsioi8i+iZ88NhmwzUza0oru9ufAV6IiG21DklTJU1I7dnAHGBznec+CixK7UXAb1qIw8ysbUbzE6D7gL8BJ0naJunStGghB56wORtYn34S9CBwWUS8nrbzc0l9ab1lwGclbSJLtstafylmZuXTeLrGTV9fX7jAhZmVTdK6iOirt8z/LdHMrICTpJlZASdJM7MCTpJmZgWcJM3MCjhJmpkVcJI0MyvgJGlmVsCVyc1KUnZFbOsOzVYmv1bSUK4C+fzcsqskDUraKOn8BtucJWlNWm+VpMPKeTlmZuVqqjJ5cnOuAvljAJJOJvs/3XPTc26tFbwY4fr0/I8CbwCX1lnHzKxyLVUmr2MBsDKVTNsCDAKn51dI1czPIyuAAa5MbmZdrJUTN1dIWp92x2sX8poBvJJbZ1vqyzseeDMi9hSsY2bWFZpNkrcBJwK9ZNXIbyotohEkLZbUL6l/7793t2sYM7O6mkqSEbEjIvZGxLvAnezfpR4CZuZWPSH15e0CjkuVzRutkx/LlcnNrDJNJcnapReSLwC1M9+PAgslTZQ0i6wy+dr8cyMrYPkkcHHqcmVyM+taB/2dZKpMfg4wRdI24IfAOZJ6gQC2At8CiIjnJN0PPA/sAS6PiL1pO48B34iIfwJXAisl/QR4GvhFya/LzKwUrkxuZu97rkxuZtYkJ0kzswJOkmZmBZwkzcwKOEmamRVwkjQzK+AkaWZWwEnSzKyAK5NbW7hKt71XjOqbZIPq5D9OpdIGJD0u6UMjnnOapD2SLj5wiyDpVEnPpurkP011Js3Muspod7eXc2B18hsj4pSI6AV+C1xTW5CqkV8PPF6wzduAb5IVwZhTZ/tmZpUbVZKsV508It7KPTySrNhFzXeAh4Cd9baXqggdExFPpapAd+Pq5GbWhVo6JinpOuASYDdwbuqbQVY+7VzgtAZPnUFWkbzG1cnNrCu1dHY7Iq6OiJnAvcAVqfsW4MpUkLdlrkxuZlUq6ydA9wJfSu0+slqRW8kK694qaeSu9BBZRfKahtXJXZnczKrUdJKUNCf3cAHwAkBEzIqInojoIbsi4pKIeCT/3IjYDrwl6Yx0VvsSXJ3czLrQqI5JNqhOPl/SScC7wMvAZaPYzkA6Gw6whOys+RHA79PNzKyruDK5mb3vuTK5mVmTnCTNzAo4SZqZFXCSNDMrMK5O3Eh6G9hYdRzJFOC1qoNIuikW6K54HEt93RQLVB/PRyJiar0F46pUGrCx0RmoTpPU71jq66Z4HEt93RQLdF88ed7dNjMr4CRpZlZgvCXJO6oOIMexNNZN8TiW+ropFui+ePYZVyduzMw6bbx9kzQz6ygnSTOzAuMiSUq6QNLGdNGwpR0ac2u6UNmApP7UN1nSakmb0v2k1K90MbPBdHG0eSWMX+/ia2MeX9KitP4mSYtKjOVaSUNpfgYkzc8tuyrFslHS+bn+lt9HSTMlPSnpeUnPSfpuVXNTEEtVc3O4pLWSnknx/Cj1z5K0Jm17laTDUv/E9HgwLe85WJwlxLJc0pbc3PSm/rZ+hlsSEV19AyYALwGzgcOAZ4CTOzDuVmDKiL4bgKWpvRS4PrXnk5V6E3AGsKaE8c8G5gEbmh0fmAxsTveTUntSSbFcC/ygzronp/doIjArvXcTynofgenAvNQ+GngxjdnxuSmIpaq5EXBUah8KrEmv+X5gYeq/Hfh2ai8Bbk/thcCqojhLimU5cHGd9dv6GW7lNh6+SZ4ODEbE5oj4L7CSrMhvFRYAK1J7BfsvXrYAuDsyTwHHKbvYWdOizsXXmhj/fGB1RLweEW8Aq2niqpQNYmlkAbAyIt6JiC3AINl7WMr7GBHbI+Lvqf028A+y6yN1fG4KYmmk3XMTEfGv9PDQdAvgPLIC2HDg3NTm7EHg05JUEGcZsTTS1s9wK8ZDkpwBvJJ73KmLhgXwuKR1khanvmmRVVUHeBWY1uEYxzp+u+O6Iu0a3VXbve1kLGn38JNk31IqnZsRsUBFcyNpgqQBsiuVrib7FvhmROyps+1946blu4Hjy4pnZCwRUZub69Lc3Cxp4shYRoxZ1d//PuMhSVblzIiYB1wIXC7p7PzCyPYFKvv9VNXjk103/USgF9gO3NTJwSUdRXbZ4u/F8Msbd3xu6sRS2dxExN7Iqv+fQPbt7+OdGvtgsUj6BHBViuk0sl3oK6uKb7TGQ5IcAmbmHje8aFiZImIo3e8Efk32gdtR241O97XrincqxrGO37a4ImJH+iN4F7iT/btjbY9F0qFkSeneiHg4dVcyN/ViqXJuaiLiTeBJ4FNku661Og35be8bNy0/FthVdjy5WC5IhygiIt4BfkkFczNmnTwA2syNrAjHZrIDyLWD2nPbPOaRwNG59l/JjoPcyPCTAzek9kUMP+i8tqQ4ehh+smRM45P9S72F7ID3pNSeXFIs03Pt75MdwwKYy/CD/pvJTkyU8j6m13g3cMuI/o7PTUEsVc3NVOC41D4C+AvwOeABhp+4WZLalzP8xM39RXGWFMv03NzdAizr1Ge46b/DTg7WdJDZma8XyY6vXN2B8WanD8kzwHO1McmO1zwBbAL+UHuz0hv7sxTfs0BfCTHcR7ar9j+y4zCXNjM+8HWyA++DwNdKjOWeNNZ64NERieHqFMtG4MIy30fgTLJd6fXAQLrNr2JuCmKpam5OAZ5O424Arsl9ntem1/kAMDH1H54eD6blsw8WZwmx/DHNzQbgV+w/A97Wz3ArN/+3RDOzAuPhmKSZWWWcJM3MCjhJmpkVcJI0MyvgJGlmVsBJ0sysgJOkmVmB/wPSSnNnTPpdlAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== RETIREES - Mainstream ==========\n",
            "134.0    2103\n",
            "150.0    3415\n",
            "175.0    5187\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Premium ==========\n",
            "134.0    1007\n",
            "150.0    1832\n",
            "175.0    2926\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Budget ==========\n",
            "134.0    1674\n",
            "150.0    2981\n",
            "175.0    4800\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "========== YOUNG FAMILIES - Mainstream ==========\n",
            "134.0    1148\n",
            "150.0    2101\n",
            "175.0    3087\n",
            "Name: Pack_Size, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAABVCAYAAADE6Rb8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAJrklEQVR4nO3dbawcdRXH8e/PQgvy2NKmqaV6b7FiICG1XAgmQADlqb6oKNH6hkbRioDRFyaUkCBGTUCDGhOBgBCKIbQIiI1ipGKjb6S1lUspD6UXKKG1tKFAwWjQluOLOUuXdnd67z7cnYXfJ9ns7H9mZ8/8uz2Zmf/e81dEYGZmjX2g1wGYmVWZk6SZWQknSTOzEk6SZmYlnCTNzEoc1OsAxmLq1KkxMDDQ6zDM7D1m3bp1r0TEtEbr+ipJDgwMsHbt2l6HYWbvMZJebLbOl9tmZiWcJM3MSjhJmpmV6Kt7kk9s3cXAkt/3Ogwzq7DN13+mo/s74JmkpDsk7ZC0oa5tuaThfGyWNJztA5L+U7fulib7nCJppaRN+Ty5c4dkZtY5o7ncvhO4oL4hIr4YEXMjYi5wP/BA3ernausi4rIm+1wCPBIRc4BH8rWZWeUcMElGxF+BVxutkyTgC8A9Y/zcBcDSXF4KfHaM7zczGxftDtycAWyPiE11bYOSHpP0F0lnNHnf9IjYlssvA9PbjMPMrCvaHbj5Eu8+i9wGfDgidko6GXhQ0okR8UazHURESGpa1FLSYmAxwIQjG/4g3sysa1o+k5R0EPA5YHmtLSLeioidubwOeA74WIO3b5c0I/czA9jR7HMi4taIGIqIoQkfPKrVcM3MWtLO5fangWciYkutQdI0SRNyeTYwB3i+wXtXAItyeRHw2zbiMDPrmtH8BOge4G/A8ZK2SLo0Vy1k/wGbM4H1+ZOg+4DLIuLV3M8vJQ3ldtcD50raRJFsr2//UMzMOk/9NMfN0NBQuMCFmXWapHURMdRonf8s0cyshJOkmVkJJ0kzsxJOkmZmJZwkzcxKOEmamZVwkjQzK+EkaWZWwpXJzXqo01W0rfNarUx+naStdRXI59etu1rSiKSNks5vss9BSatzu+WSJnbmcMzMOqulyuTpp3UVyB8CkHQCxd90n5jvualW8GIfN+T7Pwq8BlzaYBszs55rqzJ5AwuAZVky7QVgBDi1foOsZn4ORQEMcGVyM6uwdgZurpS0Pi/HaxN5zQReqttmS7bVOwZ4PSJ2l2xjZlYJrSbJm4HjgLkU1chv7FhE+5C0WNJaSWv3/HtXtz7GzKyhlpJkRGyPiD0R8TZwG3svqbcCs+o2PTbb6u0Ejs7K5s22qf8sVyY3s55pKUnWpl5IFwG1ke8VwEJJkyQNUlQmX1P/3igKWK4CLs4mVyY3s8o64O8kszL5WcBUSVuA7wJnSZoLBLAZ+DpARDwp6V7gKWA3cEVE7Mn9PAR8NSL+CVwFLJP0A+Ax4PYOH5eZWUe4MrmZve+5MrmZWYucJM3MSjhJmpmVcJI0MyvhJGlmVsJJ0syshJOkmVkJJ0kzsxKuTG4tcUVte78Y1Zlkk+rk389SacOSHpb0oX3ec4qk3ZIu3n+PIOlkSU9kdfKfZ51JM7NKGe3l9p3sX538xxFxUkTMBX4HXFtbkdXIbwAeLtnnzcDXKIpgzGmwfzOznhtVkmxUnTwi3qh7eRhFsYuabwL3Azsa7S+rCB0ZEY9mVaC7cHVyM6ugtu5JSvohcAmwCzg722ZSlE87GzilyVtnUlQkr3F1cjOrpLZGtyPimoiYBdwNXJnNPwOuyoK8bXNlcjPrpU79BOhu4PO5PERRK3IzRWHdmyTteym9laIieU3T6uSuTG5mvdRykpQ0p+7lAuAZgIgYjIiBiBigmBHx8oh4sP69EbENeEPSaTmqfQmuTm5mFTSqe5JNqpPPl3Q88DbwInDZKPYznKPhAJdTjJofCvwhH2ZmleLK5Gb2vufK5GZmLXKSNDMr4SRpZlbCSdLMrERfDdxIehPY2Os4Rmkq8Eqvgxglx9odjrU7uhHrRyJiWqMVfVUqDdjYbASqaiStdayd51i7w7E258ttM7MSTpJmZiX6LUne2usAxsCxdodj7Q7H2kRfDdyYmY23fjuTNDMbV06SZmYl+iJJSrpA0sacNGxJr+MBkLQ5JzIblrQ226ZIWilpUz5PznblZGcjOXnavC7H1mjitjHHJmlRbr9J0qJxjPU6SVuzb4clza9bd3XGulHS+XXtXf+OSJolaZWkpyQ9Kelb2V65vi2JtXJ9K+kQSWskPZ6xfi/bByWtzs9dLmlitk/K1yO5fuBAx9CWiKj0A5gAPAfMBiYCjwMnVCCuzcDUfdp+BCzJ5SXADbk8n6IUnIDTgNVdju1MYB6wodXYgCnA8/k8OZcnj1Os1wHfabDtCfnvPwkYzO/FhPH6jgAzgHm5fATwbMZUub4tibVyfZv9c3guHwyszv66F1iY7bcA38jly4FbcnkhsLzsGNqNrx/OJE8FRiLi+Yj4L7CMoshvFS0AlubyUvZObrYAuCsKjwJHq5gMrSuiwcRtLcR2PrAyIl6NiNeAlXRhRssmsTazAFgWEW9FxAvACMX3Y1y+IxGxLSL+kctvAk9TzM1Uub4tibWZnvVt9s+/8uXB+QjgHIrC3bB/v9b6+z7gU5JUcgxt6YckORN4qe51VSYNC+BhSeskLc626VFUXQd4GZiey1U4hrHG1uuYr8xL1Dtql68lMY17rHmJ9wmKs55K9+0+sUIF+1bSBEnDFDOsrqQ4C3w9InY3+Nx3Ysr1u4BjuhVrPyTJqjo9IuYBFwJXSDqzfmUU5/+V/H1VlWNLNwPHAXOBbcCNvQ3n3SQdTjFl8rfj3VMrV65vG8Rayb6NiD1RzFpwLMXZ38d7HNI7+iFJbgVm1b1uOmnYeIqIrfm8A/gNxT/s9tpldD7X5h2vwjGMNbaexRwR2/M/zdvAbey9ZOp5rJIOpkg6d0fEA9lcyb5tFGuV+zbjex1YBXyS4vZErb5E/ee+E1OuPwrY2a1Y+yFJ/h2YkyNdEylu1K7oZUCSDpN0RG0ZOA/YkHHVRioXsXdysxXAJTnaeRqwq+7ybLyMNbY/AudJmpyXZOdlW9ftc7/2Ioq+rcW6MEc3B4E5wBrG6TuS971uB56OiJ/Urapc3zaLtYp9K2mapKNz+VDgXIp7qKsoZlyF/fu11t8XA3/OM/hmx9CeTo5SdetBMUr4LMV9imsqEM9silG0x4EnazFR3Bd5BNgE/AmYEntH736R8T8BDHU5vnsoLqX+R3Ff5tJWYgO+QnHzewT48jjG+quMZX1+8WfUbX9NxroRuHA8vyPA6RSX0uuB4XzMr2LflsRaub4FTgIey5g2ANfW/T9bk330a2BSth+Sr0dy/ewDHUM7D/9ZoplZiX643DYz6xknSTOzEk6SZmYlnCTNzEo4SZqZlXCSNDMr4SRpZlbi/4HSNO3+CkNrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 360x72 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X4Y-t4MEp37M",
        "outputId": "ee0a594e-1c4b-4ffc-b1f9-59be08a092b9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "(temp.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"PROD_QTY\"].sum() / temp.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"LYLTY_CARD_NBR\"].nunique()).sort_values(ascending=False)"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LIFESTAGE               PREMIUM_CUSTOMER\n",
              "OLDER FAMILIES          Mainstream          9.804309\n",
              "                        Budget              9.639572\n",
              "                        Premium             9.578091\n",
              "YOUNG FAMILIES          Budget              9.238486\n",
              "                        Premium             9.209207\n",
              "                        Mainstream          9.180352\n",
              "OLDER SINGLES/COUPLES   Premium             7.154947\n",
              "                        Budget              7.145466\n",
              "                        Mainstream          7.098783\n",
              "MIDAGE SINGLES/COUPLES  Mainstream          6.796108\n",
              "RETIREES                Budget              6.458015\n",
              "                        Premium             6.426653\n",
              "MIDAGE SINGLES/COUPLES  Premium             6.386672\n",
              "                        Budget              6.313830\n",
              "RETIREES                Mainstream          6.253743\n",
              "NEW FAMILIES            Mainstream          5.087161\n",
              "                        Premium             5.028912\n",
              "                        Budget              5.009892\n",
              "YOUNG SINGLES/COUPLES   Mainstream          4.776459\n",
              "                        Budget              4.411485\n",
              "                        Premium             4.402098\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VZ1V9inCp34g",
        "outputId": "385680e9-b002-4174-8881-4d85a17ee778",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "(temp.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"PROD_QTY\"].sum() / temp.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"])[\"LYLTY_CARD_NBR\"].nunique()).unstack().plot.bar(figsize=(15,4), rot=0)\r\n",
        "plt.legend(loc=\"center left\", bbox_to_anchor=(1.0, 0.5))\r\n",
        "plt.savefig(\"Average purchase quantity per segment.png\", bbox_inches=\"tight\")"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qoMDDpN0p32H",
        "outputId": "1ccea593-5823-48e5-cbab-516a3e0961d7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Average chips price per transaction by segments\r\n",
        "temp[\"Unit_Price\"] = temp[\"TOT_SALES\"] / temp[\"PROD_QTY\"]\r\n",
        "temp.groupby([\"Segment\"]).mean()[\"Unit_Price\"].sort_values(ascending=False)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Segment\n",
              "YOUNG SINGLES/COUPLES - Mainstream     4.071485\n",
              "MIDAGE SINGLES/COUPLES - Mainstream    4.000101\n",
              "RETIREES - Budget                      3.924883\n",
              "RETIREES - Premium                     3.921323\n",
              "NEW FAMILIES - Budget                  3.919251\n",
              "NEW FAMILIES - Mainstream              3.916581\n",
              "OLDER SINGLES/COUPLES - Premium        3.887220\n",
              "OLDER SINGLES/COUPLES - Budget         3.877022\n",
              "NEW FAMILIES - Premium                 3.871743\n",
              "RETIREES - Mainstream                  3.833343\n",
              "OLDER SINGLES/COUPLES - Mainstream     3.803800\n",
              "YOUNG FAMILIES - Budget                3.753659\n",
              "MIDAGE SINGLES/COUPLES - Premium       3.752915\n",
              "YOUNG FAMILIES - Premium               3.752402\n",
              "OLDER FAMILIES - Budget                3.733344\n",
              "MIDAGE SINGLES/COUPLES - Budget        3.728496\n",
              "OLDER FAMILIES - Mainstream            3.727383\n",
              "YOUNG FAMILIES - Mainstream            3.707097\n",
              "OLDER FAMILIES - Premium               3.704625\n",
              "YOUNG SINGLES/COUPLES - Premium        3.645518\n",
              "YOUNG SINGLES/COUPLES - Budget         3.637681\n",
              "Name: Unit_Price, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "08Hpfykwp3zT",
        "outputId": "4a1cb5d8-10b2-4ef3-ebdd-b7bd5d59cfdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        }
      },
      "source": [
        "temp.groupby([\"LIFESTAGE\", \"PREMIUM_CUSTOMER\"]).mean()[\"Unit_Price\"].unstack().plot.bar(figsize=(15,4), rot=0)\r\n",
        "plt.legend(loc=\"center left\", bbox_to_anchor=(1,0.5))"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efe8dd11630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTQdYTyNp3wZ",
        "outputId": "60a825a2-df61-47ca-c83b-1ed768c67d0a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 673
        }
      },
      "source": [
        "z = temp.groupby([\"Segment\", \"Cleaned_Brand_Names\"]).sum()[\"TOT_SALES\"].sort_values(ascending=False).reset_index()\r\n",
        "z[z[\"Segment\"] == \"YOUNG SINGLES/COUPLES - Mainstream\"]"
      ],
      "execution_count": 79,
      "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>Segment</th>\n",
              "      <th>Cleaned_Brand_Names</th>\n",
              "      <th>TOT_SALES</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Kettle</td>\n",
              "      <td>35423.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Doritos</td>\n",
              "      <td>21705.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Pringles</td>\n",
              "      <td>16006.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Smiths</td>\n",
              "      <td>15265.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Infuzions</td>\n",
              "      <td>8749.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Old</td>\n",
              "      <td>8180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Twisties</td>\n",
              "      <td>7539.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Tostitos</td>\n",
              "      <td>7238.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Thins</td>\n",
              "      <td>7217.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Cobs</td>\n",
              "      <td>6144.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>124</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>RRD</td>\n",
              "      <td>4958.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Tyrrells</td>\n",
              "      <td>4800.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Grain Waves</td>\n",
              "      <td>4201.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Cheezels</td>\n",
              "      <td>3318.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>246</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Natural Chip Co</td>\n",
              "      <td>2130.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>258</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Woolworths</td>\n",
              "      <td>1929.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Cheetos</td>\n",
              "      <td>898.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>327</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>CCs</td>\n",
              "      <td>850.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>383</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>French</td>\n",
              "      <td>429.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>393</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Sunbites</td>\n",
              "      <td>391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>YOUNG SINGLES/COUPLES - Mainstream</td>\n",
              "      <td>Burger</td>\n",
              "      <td>243.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Segment Cleaned_Brand_Names  TOT_SALES\n",
              "0    YOUNG SINGLES/COUPLES - Mainstream              Kettle    35423.6\n",
              "8    YOUNG SINGLES/COUPLES - Mainstream             Doritos    21705.9\n",
              "23   YOUNG SINGLES/COUPLES - Mainstream            Pringles    16006.2\n",
              "24   YOUNG SINGLES/COUPLES - Mainstream              Smiths    15265.7\n",
              "55   YOUNG SINGLES/COUPLES - Mainstream           Infuzions     8749.4\n",
              "59   YOUNG SINGLES/COUPLES - Mainstream                 Old     8180.4\n",
              "65   YOUNG SINGLES/COUPLES - Mainstream            Twisties     7539.8\n",
              "73   YOUNG SINGLES/COUPLES - Mainstream            Tostitos     7238.0\n",
              "74   YOUNG SINGLES/COUPLES - Mainstream               Thins     7217.1\n",
              "92   YOUNG SINGLES/COUPLES - Mainstream                Cobs     6144.6\n",
              "124  YOUNG SINGLES/COUPLES - Mainstream                 RRD     4958.1\n",
              "129  YOUNG SINGLES/COUPLES - Mainstream            Tyrrells     4800.6\n",
              "148  YOUNG SINGLES/COUPLES - Mainstream         Grain Waves     4201.0\n",
              "189  YOUNG SINGLES/COUPLES - Mainstream            Cheezels     3318.3\n",
              "246  YOUNG SINGLES/COUPLES - Mainstream     Natural Chip Co     2130.0\n",
              "258  YOUNG SINGLES/COUPLES - Mainstream          Woolworths     1929.8\n",
              "318  YOUNG SINGLES/COUPLES - Mainstream             Cheetos      898.8\n",
              "327  YOUNG SINGLES/COUPLES - Mainstream                 CCs      850.5\n",
              "383  YOUNG SINGLES/COUPLES - Mainstream              French      429.0\n",
              "393  YOUNG SINGLES/COUPLES - Mainstream            Sunbites      391.0\n",
              "415  YOUNG SINGLES/COUPLES - Mainstream              Burger      243.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HeUsqSQYrMIi"
      },
      "source": [
        "#Trends and Insights :\r\n",
        "Top 3 total sales contributor segment are\r\n",
        "\r\n",
        "*  Older families (Budget) \\$156,864 \r\n",
        "*  Young Singles/Couples (Mainstream) \\$147,582 \r\n",
        "*   Retirees (Mainstream) \\$145,169\r\n",
        "\r\n",
        "1. Young Singles/Couples (Mainstream) has the highest population, followed by Retirees (Mainstream). Which explains their high total sales.\r\n",
        "\r\n",
        "2. Despite Older Families not having the highest population, they have the highest frequency of purchase, which contributes to their high total sales.\r\n",
        "\r\n",
        "3. Older Families followed by Young Families has the highest average quantity of chips bought per purchase.\r\n",
        "\r\n",
        "4. The Mainstream category of the \"Young and Midage Singles/Couples\" have the highest spending of chips per purchase. And the difference to the non-Mainstream \"Young and Midage Singles/Couples\" are statistically significant.\r\n",
        "\r\n",
        "5. Chips brand Kettle is dominating every segment as the most purchased brand.\r\n",
        "\r\n",
        "6. Observing the 2nd most purchased brand, \"Young and Midage Singles/Couples\" is the only segment with a different preference (Doritos) as compared to others' (Smiths).\r\n",
        "\r\n",
        "7. Most frequent chip size purchased is 175gr followed by the 150gr chip size for all segments."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uGlP8kJLrL2l"
      },
      "source": [
        "#Views and Recommendations:\r\n",
        "\r\n",
        "1. Older Families:\r\n",
        "Focus on the Budget segment.\r\n",
        "Strength: Frequent purchase. We can give promotions that encourages more frequency of purchase.\r\n",
        "Strength: High quantity of chips purchased per visit. We can give promotions that encourage them to buy more quantity of chips per purchase.\r\n",
        "\r\n",
        "2. Young Singles/Couples:\r\n",
        "Focus on the Mainstream segment.\r\n",
        "This segment is the only segment that had Doritos as their 2nd most purchased brand (after Kettle). To specifically target this segment it might be a good idea to collaborate with Doritos merchant to do some branding promotion catered to \"Young Singles/Couples - Mainstream\" segment.\r\n",
        "Strength: Population quantity. We can spend more effort on making sure our promotions reach them, and it reaches them frequently.\r\n",
        "\r\n",
        "3. Retirees:\r\n",
        "Focus on the Mainstream segment.\r\n",
        "Strength: Population quantity. Again, since their population quantity is the contributor to the high total sales, we should spend more effort on making sure our promotions reaches as many of them as possible and frequent.\r\n",
        "\r\n",
        "4. General:\r\n",
        "All segments has Kettle as the most frequently purchased brand, and 175gr (regardless of brand) followed by 150gr as the preferred chip size.\r\n",
        "When promoting chips in general to all segments it is good to take advantage of these two points.\r\n"
      ]
    }
  ]
}