06_calcolo_bdi_cdi.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCl792zP3B9M"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/alesaccoia/IULM_DDM2324_Notebooks/blob/main/06_calcolo_bdi_cdi.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SL-90msS3B9Q"
      },
      "source": [
        "# Esercizio calcolo BDI/CDI (sezione 3.2.2 del libro)\n",
        "\n",
        "Si comincia importando il dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPtlNxzg3B9R",
        "outputId": "a1d5a7d7-effc-4181-dd53-56d05496cdb8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "      Customer #id          Segmento         Brand\n",
            "0                0             Other  OceanDelight\n",
            "1                1             Other        SeaYum\n",
            "2                2             Other           NaN\n",
            "3                3             Other  OceanDelight\n",
            "4                4  Health-Conscious           NaN\n",
            "...            ...               ...           ...\n",
            "4995          4995             Other           NaN\n",
            "4996          4996       Adventurous           NaN\n",
            "4997          4997             Other           NaN\n",
            "4998          4998             Other  OceanDelight\n",
            "4999          4999             Other      BlueFish\n",
            "\n",
            "[5000 rows x 3 columns]\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "df = pd.read_excel('https://github.com/alesaccoia/IULM_DDM2324_Notebooks/raw/main/data/calcolo_bdi_cdi.xlsx')\n",
        "print(df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bGwejIy3B9S"
      },
      "source": [
        "Vediamo per prima cosa la dimensione del dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWKLNZQ-3B9S",
        "outputId": "ad119e4d-d932-485b-a4fe-b27fc4174608"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5000"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "totale_campione = len(df)\n",
        "totale_campione"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fr2p6I0w3B9S"
      },
      "source": [
        "Stampiamo a schermo i brand unici all'interno del dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3EQgeLep3B9T",
        "outputId": "db06aa20-08ab-4e47-8f34-ac79c434eee9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['OceanDelight', 'SeaYum', nan, 'TunaKing', 'BlueFish'],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "target_brands = df['Brand'].unique()\n",
        "target_brands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deAzzQTM3B9T"
      },
      "source": [
        "Ripuliamo la lista dal brand \"nan\", valore che indica che il consumatore non acquista nessun brand.\n",
        "\n",
        "Utilizza il caratteristico costrutto chiamato [list comprehension](https://www.w3schools.com/python/python_lists_comprehension.asp)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKNhti6X3B9T",
        "outputId": "4973c41c-d3e3-4369-b942-def497e8d9e9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['OceanDelight', 'SeaYum', 'TunaKing', 'BlueFish']"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "target_brands = [b for b in target_brands if b is not np.nan]\n",
        "target_brands"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cxZ_dRnZ3B9U"
      },
      "source": [
        "Calcoliamo il totale di rispondenti che acquista un brand della categoria merceologica, di qualsiasi brand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6pddUYRO3B9U",
        "outputId": "65a5c28e-19d2-42c3-cd65-53152e340ddb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3438"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "totale_consumatori_categoria = len(df[df['Brand'].notnull()])\n",
        "totale_consumatori_categoria"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AukHl4eD3B9U"
      },
      "source": [
        "La penetrazione della categoria sulla popolazione é il rapporto tra il numero di consumatori che consuma e il totale di consumatori nel campione"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "00MgW3v03B9U",
        "outputId": "f84dae0a-08f9-4eb7-802d-e605d942f638"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.6876"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "penetrazione_categoria = totale_consumatori_categoria / totale_campione\n",
        "penetrazione_categoria"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RU74ykip3B9U"
      },
      "source": [
        "Possiamo calcolare le proporzioni di persone del campione che acquistano ogni brand nell'analisi nel seguente modo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ddgu8hgs3B9V",
        "outputId": "0dfe3780-455d-4d98-b7ea-d03c14e75483"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Brand\n",
              "SeaYum          0.2392\n",
              "TunaKing        0.1714\n",
              "OceanDelight    0.1510\n",
              "BlueFish        0.1260\n",
              "Name: count, dtype: float64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "penetrazione_brand = df['Brand'].value_counts()\n",
        "penetrazione_brand / totale_campione"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eBFEqQAP3B9V"
      },
      "source": [
        "## Calcolo del CDI\n",
        "\n",
        "Il CDI é calcolato il tasso di penetrazione della categoria merceologica su ognuna delle dimensioni in esame, indicizzato a 100.\n",
        "\n",
        "Risponde alla domanda \"Quante persone appartenenti a ognuno dei segmenti target consumano abitualmente tonno in scatola?\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YriDLZhu3B9V"
      },
      "source": [
        "Vediamo i segmenti unici presenti dentro il nostro dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAQN6njO3B9V",
        "outputId": "39e6f800-274f-4d48-dcc5-19c022384175"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Other', 'Health-Conscious', 'Adventurous', 'Budget-Friendly']"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "segmenti_target = df['Segmento'].unique().tolist()\n",
        "segmenti_target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sy7E46nw3B9V"
      },
      "source": [
        "Vediamo il numero di persone appartenenti a ogni segmento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IgAKq8df3B9V",
        "outputId": "f03ca7c3-9d82-407c-8dac-fc89ad063e92"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Segmento\n",
              "Other               3469\n",
              "Budget-Friendly      644\n",
              "Adventurous          628\n",
              "Health-Conscious     259\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "rispondenti_per_segmento = df['Segmento'].value_counts()\n",
        "rispondenti_per_segmento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LWdfjMA73B9V"
      },
      "source": [
        "Vediamoli in un grafico"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "xoPmlcUI3B9V",
        "outputId": "55d52044-e068-4135-d1f1-7e26fc10ec21"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df['Segmento'])\n",
        "plt.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LBNgY5gQ3B9W"
      },
      "source": [
        "Ora dediamo il numero di persone appartenenti a ogni segmento __che acquista tonno in scatola__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HHL83fcQ3B9W",
        "outputId": "8a018e02-0ca6-4558-d2b8-3a3bf2ce7eb1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Segmento\n",
              "Other               2390\n",
              "Adventurous          443\n",
              "Budget-Friendly      433\n",
              "Health-Conscious     172\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "consumatori_per_segmento = df[df['Brand'].notnull()]['Segmento'].value_counts()\n",
        "consumatori_per_segmento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xiFy05Rz3B9W"
      },
      "source": [
        "Salviamo in una variabile la diffusione del tonno in scatola in ogni segmento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZCCp1hA63B9W",
        "outputId": "97f9a163-93f0-4cca-ca2e-d9954611d2a3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Other               0.688959\n",
              "Health-Conscious    0.664093\n",
              "Adventurous         0.705414\n",
              "Budget-Friendly     0.672360\n",
              "dtype: float64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diffusione_per_segmento = pd.Series()\n",
        "for seg in segmenti_target:\n",
        "    diffusione_per_segmento[seg] = consumatori_per_segmento[seg] / rispondenti_per_segmento[seg]\n",
        "\n",
        "diffusione_per_segmento"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kmfji3A3B9W"
      },
      "source": [
        "Per indicizzare un indicatore a 100 si divide ogni numero per la media sulla popolazione (o campione)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HI-sCw0I3B9W",
        "outputId": "53736dd7-7db7-4f45-efaa-a8dc44f08686"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Other               100.197696\n",
              "Health-Conscious     96.581248\n",
              "Adventurous         102.590752\n",
              "Budget-Friendly      97.783631\n",
              "dtype: float64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cdi = 100.0 * diffusione_per_segmento / penetrazione_categoria\n",
        "cdi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5TqQMCA3B9W"
      },
      "source": [
        "# Calcolo del BDI\n",
        "\n",
        "Il Brand Development Index indica quanto ogni marca sia diffusa in una determinata dimensione di analisi (un segmento nel nostro caso) rispetto alla penetrazione del brand nell'intera popolazione. Cominciamo salvando la diffusione del brand nella popolazione con la seguente istruzione:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "he3HC2qn3B9W",
        "outputId": "b7fc9796-ca0c-47b6-adca-d7569c930e07"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Brand\n",
              "SeaYum          0.2392\n",
              "TunaKing        0.1714\n",
              "OceanDelight    0.1510\n",
              "BlueFish        0.1260\n",
              "Name: count, dtype: float64"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "diffusione_brand_popolazione = df['Brand'].value_counts()\n",
        "diffusione_brand_popolazione = diffusione_brand_popolazione / len(df)\n",
        "diffusione_brand_popolazione"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QeF0q-3r3B9W"
      },
      "source": [
        "il seguente codice itera su tutte le permutazioni di brand/segmento, calcolando il BDI (che é la diffusione di un brand in un determinato segmento, indicizzata sulla diffusione del brand nell'intero campione)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "RoiC6Aod3B9W",
        "outputId": "6eee573a-24c8-4dd8-b5a4-5995a60bdd13"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"results_df\",\n  \"rows\": 16,\n  \"fields\": [\n    {\n      \"column\": \"brand\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"SeaYum\",\n          \"BlueFish\",\n          \"OceanDelight\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"segmento\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Health-Conscious\",\n          \"Budget-Friendly\",\n          \"Other\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"consumatori_per_brand_e_segmento\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 35,\n        \"max\": 821,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          543,\n          35,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"consumatori_per_segmento\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": 172,\n        \"max\": 2390,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          172,\n          433,\n          2390\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diffusione_brand_nel_segmento\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.045939444973458775,\n        \"min\": 0.10869565217391304,\n        \"max\": 0.2546583850931677,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          0.15652925915249352,\n          0.13513513513513514,\n          0.23552123552123552\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"diffusione_brand_popolazione\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.043430127791660944,\n        \"min\": 0.126,\n        \"max\": 0.2392,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.2392,\n          0.126,\n          0.151\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.076119910222378,\n        \"min\": 86.2663906142167,\n        \"max\": 116.26731371954301,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          103.6617610281414,\n          89.4934669769107,\n          98.46205498379412\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CDI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.3846927208587196,\n        \"min\": 96.58124841370915,\n        \"max\": 102.59075228895485,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          96.58124841370915,\n          97.78363124595768,\n          100.1976955033119\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "results_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-5ce9a52c-c591-434a-9224-b4b62a035f3b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>brand</th>\n",
              "      <th>segmento</th>\n",
              "      <th>consumatori_per_brand_e_segmento</th>\n",
              "      <th>consumatori_per_segmento</th>\n",
              "      <th>diffusione_brand_nel_segmento</th>\n",
              "      <th>diffusione_brand_popolazione</th>\n",
              "      <th>BDI</th>\n",
              "      <th>CDI</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>OceanDelight</td>\n",
              "      <td>Other</td>\n",
              "      <td>543</td>\n",
              "      <td>2390</td>\n",
              "      <td>0.156529</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>103.661761</td>\n",
              "      <td>100.197696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>OceanDelight</td>\n",
              "      <td>Health-Conscious</td>\n",
              "      <td>35</td>\n",
              "      <td>172</td>\n",
              "      <td>0.135135</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>89.493467</td>\n",
              "      <td>96.581248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>OceanDelight</td>\n",
              "      <td>Adventurous</td>\n",
              "      <td>87</td>\n",
              "      <td>443</td>\n",
              "      <td>0.138535</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>91.745054</td>\n",
              "      <td>102.590752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>OceanDelight</td>\n",
              "      <td>Budget-Friendly</td>\n",
              "      <td>90</td>\n",
              "      <td>433</td>\n",
              "      <td>0.139752</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>92.550697</td>\n",
              "      <td>97.783631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SeaYum</td>\n",
              "      <td>Other</td>\n",
              "      <td>821</td>\n",
              "      <td>2390</td>\n",
              "      <td>0.236668</td>\n",
              "      <td>0.2392</td>\n",
              "      <td>98.941316</td>\n",
              "      <td>100.197696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>SeaYum</td>\n",
              "      <td>Health-Conscious</td>\n",
              "      <td>61</td>\n",
              "      <td>172</td>\n",
              "      <td>0.235521</td>\n",
              "      <td>0.2392</td>\n",
              "      <td>98.462055</td>\n",
              "      <td>96.581248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>SeaYum</td>\n",
              "      <td>Adventurous</td>\n",
              "      <td>150</td>\n",
              "      <td>443</td>\n",
              "      <td>0.238854</td>\n",
              "      <td>0.2392</td>\n",
              "      <td>99.855143</td>\n",
              "      <td>102.590752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>SeaYum</td>\n",
              "      <td>Budget-Friendly</td>\n",
              "      <td>164</td>\n",
              "      <td>433</td>\n",
              "      <td>0.254658</td>\n",
              "      <td>0.2392</td>\n",
              "      <td>106.462536</td>\n",
              "      <td>97.783631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>TunaKing</td>\n",
              "      <td>Other</td>\n",
              "      <td>594</td>\n",
              "      <td>2390</td>\n",
              "      <td>0.171231</td>\n",
              "      <td>0.1714</td>\n",
              "      <td>99.901343</td>\n",
              "      <td>100.197696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>TunaKing</td>\n",
              "      <td>Health-Conscious</td>\n",
              "      <td>40</td>\n",
              "      <td>172</td>\n",
              "      <td>0.154440</td>\n",
              "      <td>0.1714</td>\n",
              "      <td>90.105108</td>\n",
              "      <td>96.581248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>TunaKing</td>\n",
              "      <td>Adventurous</td>\n",
              "      <td>114</td>\n",
              "      <td>443</td>\n",
              "      <td>0.181529</td>\n",
              "      <td>0.1714</td>\n",
              "      <td>105.909371</td>\n",
              "      <td>102.590752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>TunaKing</td>\n",
              "      <td>Budget-Friendly</td>\n",
              "      <td>109</td>\n",
              "      <td>433</td>\n",
              "      <td>0.169255</td>\n",
              "      <td>0.1714</td>\n",
              "      <td>98.748342</td>\n",
              "      <td>97.783631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>BlueFish</td>\n",
              "      <td>Other</td>\n",
              "      <td>432</td>\n",
              "      <td>2390</td>\n",
              "      <td>0.124532</td>\n",
              "      <td>0.1260</td>\n",
              "      <td>98.834576</td>\n",
              "      <td>100.197696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BlueFish</td>\n",
              "      <td>Health-Conscious</td>\n",
              "      <td>36</td>\n",
              "      <td>172</td>\n",
              "      <td>0.138996</td>\n",
              "      <td>0.1260</td>\n",
              "      <td>110.314396</td>\n",
              "      <td>96.581248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>BlueFish</td>\n",
              "      <td>Adventurous</td>\n",
              "      <td>92</td>\n",
              "      <td>443</td>\n",
              "      <td>0.146497</td>\n",
              "      <td>0.1260</td>\n",
              "      <td>116.267314</td>\n",
              "      <td>102.590752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>BlueFish</td>\n",
              "      <td>Budget-Friendly</td>\n",
              "      <td>70</td>\n",
              "      <td>433</td>\n",
              "      <td>0.108696</td>\n",
              "      <td>0.1260</td>\n",
              "      <td>86.266391</td>\n",
              "      <td>97.783631</td>\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c7e4b566-2775-48dd-b8cf-26eebad3f774 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           brand          segmento consumatori_per_brand_e_segmento  \\\n",
              "0   OceanDelight             Other                              543   \n",
              "1   OceanDelight  Health-Conscious                               35   \n",
              "2   OceanDelight       Adventurous                               87   \n",
              "3   OceanDelight   Budget-Friendly                               90   \n",
              "4         SeaYum             Other                              821   \n",
              "5         SeaYum  Health-Conscious                               61   \n",
              "6         SeaYum       Adventurous                              150   \n",
              "7         SeaYum   Budget-Friendly                              164   \n",
              "8       TunaKing             Other                              594   \n",
              "9       TunaKing  Health-Conscious                               40   \n",
              "10      TunaKing       Adventurous                              114   \n",
              "11      TunaKing   Budget-Friendly                              109   \n",
              "12      BlueFish             Other                              432   \n",
              "13      BlueFish  Health-Conscious                               36   \n",
              "14      BlueFish       Adventurous                               92   \n",
              "15      BlueFish   Budget-Friendly                               70   \n",
              "\n",
              "   consumatori_per_segmento  diffusione_brand_nel_segmento  \\\n",
              "0                      2390                       0.156529   \n",
              "1                       172                       0.135135   \n",
              "2                       443                       0.138535   \n",
              "3                       433                       0.139752   \n",
              "4                      2390                       0.236668   \n",
              "5                       172                       0.235521   \n",
              "6                       443                       0.238854   \n",
              "7                       433                       0.254658   \n",
              "8                      2390                       0.171231   \n",
              "9                       172                       0.154440   \n",
              "10                      443                       0.181529   \n",
              "11                      433                       0.169255   \n",
              "12                     2390                       0.124532   \n",
              "13                      172                       0.138996   \n",
              "14                      443                       0.146497   \n",
              "15                      433                       0.108696   \n",
              "\n",
              "    diffusione_brand_popolazione         BDI         CDI  \n",
              "0                         0.1510  103.661761  100.197696  \n",
              "1                         0.1510   89.493467   96.581248  \n",
              "2                         0.1510   91.745054  102.590752  \n",
              "3                         0.1510   92.550697   97.783631  \n",
              "4                         0.2392   98.941316  100.197696  \n",
              "5                         0.2392   98.462055   96.581248  \n",
              "6                         0.2392   99.855143  102.590752  \n",
              "7                         0.2392  106.462536   97.783631  \n",
              "8                         0.1714   99.901343  100.197696  \n",
              "9                         0.1714   90.105108   96.581248  \n",
              "10                        0.1714  105.909371  102.590752  \n",
              "11                        0.1714   98.748342   97.783631  \n",
              "12                        0.1260   98.834576  100.197696  \n",
              "13                        0.1260  110.314396   96.581248  \n",
              "14                        0.1260  116.267314  102.590752  \n",
              "15                        0.1260   86.266391   97.783631  "
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a DataFrame for the results\n",
        "results_df = pd.DataFrame(columns=['brand', 'segmento', 'consumatori_per_brand_e_segmento', 'consumatori_per_segmento', 'diffusione_brand_nel_segmento', 'diffusione_brand_popolazione', 'BDI', 'CDI'])\n",
        "\n",
        "rows_list = []\n",
        "\n",
        "for brand in target_brands:\n",
        "    for segmento in segmenti_target:\n",
        "        # qui prendiamo il numero di consumatori del nostro segmento che acquistano regolarmente il brand\n",
        "        consumatori_per_brand_e_segmento = len(df[(df['Brand'] == brand) & (df['Segmento'] == segmento)])\n",
        "        # il numero di consumatori totali di questo segmento é giá in \"rispondenti_per_segmento\", da qui possiamo sapere la diffusione del brand nel segmento\n",
        "        diffusione_brand_nel_segmento = consumatori_per_brand_e_segmento / rispondenti_per_segmento[segmento]\n",
        "        # la diffusione del brand nell'intera popolazione é in diffusione_brand_popolazione, quindi possiamo calcolare il bdi\n",
        "\n",
        "        bdi = 100.0 * diffusione_brand_nel_segmento / diffusione_brand_popolazione[brand]\n",
        "\n",
        "        row = {\n",
        "            'brand': brand,\n",
        "            'segmento': segmento,\n",
        "            'consumatori_per_brand_e_segmento': consumatori_per_brand_e_segmento,\n",
        "            'consumatori_per_segmento': consumatori_per_segmento[segmento],\n",
        "            'diffusione_brand_nel_segmento': diffusione_brand_nel_segmento,\n",
        "            'diffusione_brand_popolazione': diffusione_brand_popolazione[brand],\n",
        "            'BDI': bdi,\n",
        "            'CDI': cdi[segmento],\n",
        "        }\n",
        "        rows_list.append(row)\n",
        "\n",
        "results_df = pd.concat([results_df, pd.DataFrame(rows_list)], ignore_index=True)\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "3iR7P1ui3B9W"
      },
      "outputs": [],
      "source": [
        "results_df['BDI_CDI'] = results_df.BDI / results_df.CDI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkbWeX313B9X"
      },
      "source": [
        "Come ultima cosa, per esportare in excel, \"pivottiamo\" mantenendo sulle righe i segmenti target e sulle colonne i brand"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "IiMHm-g33B9X",
        "outputId": "b2b60838-6faa-4c3b-fe58-0155002c266d"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pivoted_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": [\n        \"segmento\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Budget-Friendly\",\n          \"Other\",\n          \"Adventurous\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI\",\n        \"BlueFish\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.252185633971589,\n        \"min\": 86.2663906142167,\n        \"max\": 116.26731371954301,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          86.2663906142167,\n          98.8345756290409,\n          116.26731371954301\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI\",\n        \"OceanDelight\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.332913753783618,\n        \"min\": 89.4934669769107,\n        \"max\": 103.6617610281414,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          92.55069721525234,\n          103.6617610281414,\n          91.74505420339983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI\",\n        \"SeaYum\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.7331793123809724,\n        \"min\": 98.46205498379412,\n        \"max\": 106.46253557406676,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          106.46253557406676,\n          98.94131586888552,\n          99.85514347187015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI\",\n        \"TunaKing\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.513784346174835,\n        \"min\": 90.10510760802475,\n        \"max\": 105.90937130710743,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          98.74834211499017,\n          99.90134321896929,\n          105.90937130710743\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI_CDI\",\n        \"BlueFish\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1249749944650862,\n        \"min\": 0.8822170900692841,\n        \"max\": 1.1421926910299003,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8822170900692841,\n          0.9863956963538553,\n          1.1333118348919702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI_CDI\",\n        \"OceanDelight\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06004255422421655,\n        \"min\": 0.8942819129056852,\n        \"max\": 1.0345723073512703,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.9464845602067814,\n          1.0345723073512703,\n          0.8942819129056852\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI_CDI\",\n        \"SeaYum\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05142638050094261,\n        \"min\": 0.9733347425957104,\n        \"max\": 1.088756208145705,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.088756208145705,\n          0.9874609927093099,\n          0.9733347425957104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"BDI_CDI\",\n        \"TunaKing\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04264395215048394,\n        \"min\": 0.9329461887058695,\n        \"max\": 1.0323481302564723,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1.0098657705460532,\n          0.9970423243483397,\n          1.0323481302564723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"CDI\",\n        \"BlueFish\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.666167514644514,\n        \"min\": 96.58124841370915,\n        \"max\": 102.59075228895485,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          97.78363124595768,\n          100.1976955033119,\n          102.59075228895485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"CDI\",\n        \"OceanDelight\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.666167514644514,\n        \"min\": 96.58124841370915,\n        \"max\": 102.59075228895485,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          97.78363124595768,\n          100.1976955033119,\n          102.59075228895485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"CDI\",\n        \"SeaYum\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.666167514644514,\n        \"min\": 96.58124841370915,\n        \"max\": 102.59075228895485,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          97.78363124595768,\n          100.1976955033119,\n          102.59075228895485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"CDI\",\n        \"TunaKing\"\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.666167514644514,\n        \"min\": 96.58124841370915,\n        \"max\": 102.59075228895485,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          97.78363124595768,\n          100.1976955033119,\n          102.59075228895485\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "pivoted_df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-fc60c95e-9a3e-4038-b39a-443eeb79ccb1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">BDI</th>\n",
              "      <th colspan=\"4\" halign=\"left\">BDI_CDI</th>\n",
              "      <th colspan=\"4\" halign=\"left\">CDI</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>brand</th>\n",
              "      <th>BlueFish</th>\n",
              "      <th>OceanDelight</th>\n",
              "      <th>SeaYum</th>\n",
              "      <th>TunaKing</th>\n",
              "      <th>BlueFish</th>\n",
              "      <th>OceanDelight</th>\n",
              "      <th>SeaYum</th>\n",
              "      <th>TunaKing</th>\n",
              "      <th>BlueFish</th>\n",
              "      <th>OceanDelight</th>\n",
              "      <th>SeaYum</th>\n",
              "      <th>TunaKing</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>segmento</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Adventurous</th>\n",
              "      <td>116.267314</td>\n",
              "      <td>91.745054</td>\n",
              "      <td>99.855143</td>\n",
              "      <td>105.909371</td>\n",
              "      <td>1.133312</td>\n",
              "      <td>0.894282</td>\n",
              "      <td>0.973335</td>\n",
              "      <td>1.032348</td>\n",
              "      <td>102.590752</td>\n",
              "      <td>102.590752</td>\n",
              "      <td>102.590752</td>\n",
              "      <td>102.590752</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Budget-Friendly</th>\n",
              "      <td>86.266391</td>\n",
              "      <td>92.550697</td>\n",
              "      <td>106.462536</td>\n",
              "      <td>98.748342</td>\n",
              "      <td>0.882217</td>\n",
              "      <td>0.946485</td>\n",
              "      <td>1.088756</td>\n",
              "      <td>1.009866</td>\n",
              "      <td>97.783631</td>\n",
              "      <td>97.783631</td>\n",
              "      <td>97.783631</td>\n",
              "      <td>97.783631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health-Conscious</th>\n",
              "      <td>110.314396</td>\n",
              "      <td>89.493467</td>\n",
              "      <td>98.462055</td>\n",
              "      <td>90.105108</td>\n",
              "      <td>1.142193</td>\n",
              "      <td>0.926613</td>\n",
              "      <td>1.019474</td>\n",
              "      <td>0.932946</td>\n",
              "      <td>96.581248</td>\n",
              "      <td>96.581248</td>\n",
              "      <td>96.581248</td>\n",
              "      <td>96.581248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>98.834576</td>\n",
              "      <td>103.661761</td>\n",
              "      <td>98.941316</td>\n",
              "      <td>99.901343</td>\n",
              "      <td>0.986396</td>\n",
              "      <td>1.034572</td>\n",
              "      <td>0.987461</td>\n",
              "      <td>0.997042</td>\n",
              "      <td>100.197696</td>\n",
              "      <td>100.197696</td>\n",
              "      <td>100.197696</td>\n",
              "      <td>100.197696</td>\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-462b4d92-7869-43e7-8741-eb01ec74f231 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                         BDI                                        BDI_CDI  \\\n",
              "brand               BlueFish OceanDelight      SeaYum    TunaKing  BlueFish   \n",
              "segmento                                                                      \n",
              "Adventurous       116.267314    91.745054   99.855143  105.909371  1.133312   \n",
              "Budget-Friendly    86.266391    92.550697  106.462536   98.748342  0.882217   \n",
              "Health-Conscious  110.314396    89.493467   98.462055   90.105108  1.142193   \n",
              "Other              98.834576   103.661761   98.941316   99.901343  0.986396   \n",
              "\n",
              "                                                          CDI               \\\n",
              "brand            OceanDelight    SeaYum  TunaKing    BlueFish OceanDelight   \n",
              "segmento                                                                     \n",
              "Adventurous          0.894282  0.973335  1.032348  102.590752   102.590752   \n",
              "Budget-Friendly      0.946485  1.088756  1.009866   97.783631    97.783631   \n",
              "Health-Conscious     0.926613  1.019474  0.932946   96.581248    96.581248   \n",
              "Other                1.034572  0.987461  0.997042  100.197696   100.197696   \n",
              "\n",
              "                                          \n",
              "brand                 SeaYum    TunaKing  \n",
              "segmento                                  \n",
              "Adventurous       102.590752  102.590752  \n",
              "Budget-Friendly    97.783631   97.783631  \n",
              "Health-Conscious   96.581248   96.581248  \n",
              "Other             100.197696  100.197696  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pivoted_df = results_df.pivot_table(index='segmento',\n",
        "                                   columns='brand',\n",
        "                                   values=['BDI', 'CDI', 'BDI_CDI'],\n",
        "                                   aggfunc='first')\n",
        "\n",
        "pivoted_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "ZAndRw-Z3B9X"
      },
      "outputs": [],
      "source": [
        "pivoted_df.to_excel('pivoted_df.xlsx')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.8"
    },
    "orig_nbformat": 4
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  "nbformat": 4,
  "nbformat_minor": 0
}