OneProductData_GET.ipynb

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        "# @title Setup\n",
        "from google.colab import auth\n",
        "from google.cloud import bigquery\n",
        "from google.colab import data_table\n",
        "\n",
        "project = 'itg-bpma-gbl-ww-dv' # Project ID inserted based on the query results selected to explore\n",
        "location = 'EU' # Location inserted based on the query results selected to explore\n",
        "client = bigquery.Client(project=project, location=location)\n",
        "data_table.enable_dataframe_formatter()\n",
        "auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "6T1MvOQlyue3"
      },
      "execution_count": 126,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "#Define Client Chanel_type and Product\n",
        "client_name = 'BOOTS'\n",
        "Channel_type = 'Online'\n",
        "Product_ean = '800897813727'"
      ],
      "metadata": {
        "id": "a0HFSD6Fubem"
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      "execution_count": 36,
      "outputs": []
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        "# Define parameters for the filter\n",
        "params = {\n",
        "    'client_name': client_name,\n",
        "    'ean_code': Product_ean\n",
        "}\n",
        "\n",
        "query = \"\"\"\n",
        "SELECT *\n",
        "FROM bpma_ds_c2_exposed_eu_dv.sellout_and_promo_event_pivoted_V1\n",
        "Where\n",
        "client = @client_name AND\n",
        "ean = @ean_code AND global_channel_type='Online'\n",
        "\"\"\"\n",
        "\n",
        "# Execute the query and read the result into a Pandas DataFrame\n",
        "job_config = bigquery.QueryJobConfig(\n",
        "    query_parameters=[\n",
        "        bigquery.ScalarQueryParameter('client_name', 'STRING', params['client_name']),\n",
        "        bigquery.ScalarQueryParameter('ean_code', 'STRING', params['ean_code'])\n",
        "    ]\n",
        ")\n",
        "\n",
        "result_df = client.query(query, job_config=job_config).to_dataframe()"
      ],
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        "id": "2R1FpBRtRpNm"
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      "execution_count": 37,
      "outputs": []
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      "source": [
        "result_df.head()"
      ],
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      "execution_count": 38,
      "outputs": [
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          "text": [
            "Warning: Total number of columns (132) exceeds max_columns (20). Falling back to pandas display.\n"
          ]
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          "data": {
            "text/plain": [
              "            ean country_id global_channel_type client     sold_units  \\\n",
              "0  800897813727         GB              Online  BOOTS  112.000000000   \n",
              "1  800897813727         GB              Online  BOOTS   99.000000000   \n",
              "2  800897813727         GB              Online  BOOTS   82.000000000   \n",
              "3  800897813727         GB              Online  BOOTS  146.000000000   \n",
              "4  800897813727         GB              Online  BOOTS  135.000000000   \n",
              "\n",
              "   sold_value_loc  start_date    end_date    mid_date  is_promo  ...  \\\n",
              "0          887.30  2021-11-14  2021-11-20  2021-11-17     False  ...   \n",
              "1          789.45  2021-05-30  2021-06-05  2021-06-02     False  ...   \n",
              "2          528.68  2020-01-19  2020-01-25  2020-01-22     False  ...   \n",
              "3          979.24  2024-03-10  2024-03-16  2024-03-13      True  ...   \n",
              "4          997.10  2022-02-06  2022-02-12  2022-02-09     False  ...   \n",
              "\n",
              "   DEFENSIVE ON Before  SHOPPER INVESTMENT ON Before  RETURNS During  \\\n",
              "0                  NaN                           NaN             NaN   \n",
              "1                  NaN                           NaN             NaN   \n",
              "2                  NaN                           NaN             NaN   \n",
              "3                  NaN                           NaN             NaN   \n",
              "4                  NaN                           NaN             NaN   \n",
              "\n",
              "  STD COST OF SALES AND VARIANCES Before  CONSO NET SALES Before  \\\n",
              "0                                    NaN                     NaN   \n",
              "1                                    NaN                     NaN   \n",
              "2                                    NaN                     NaN   \n",
              "3                                    NaN                     NaN   \n",
              "4                                    NaN                     NaN   \n",
              "\n",
              "   PHYSICAL DISTRIBUTION During  NG NET SALES Incremental  \\\n",
              "0                           NaN                       NaN   \n",
              "1                           NaN                       NaN   \n",
              "2                           NaN                       NaN   \n",
              "3                           NaN                       NaN   \n",
              "4                           NaN                       NaN   \n",
              "\n",
              "  ACTIVE SUPPORT OFF Incremental INVOICED SALES Before hierarchy_is_ean  \n",
              "0                            NaN                   NaN             <NA>  \n",
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              "2                            NaN                   NaN             <NA>  \n",
              "3                            NaN                   NaN             <NA>  \n",
              "4                            NaN                   NaN             <NA>  \n",
              "\n",
              "[5 rows x 132 columns]"
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      "cell_type": "code",
      "source": [
        "result_df.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "G6TcHHMA5IQ6"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Supprimer les lignes où 'id_event' est null et 'is_promo' est True\n",
        "result_df = result_df.drop(result_df[(result_df['event_id'].isnull()) & (result_df['is_promo'])].index)"
      ],
      "metadata": {
        "id": "CV20yOoV5RDy"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result_df['start_date'].unique().shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAq2dbM4Ng5v",
        "outputId": "352ba739-188a-4f17-b5c8-b7d442753362"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "222"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "events_columns_to_delete = [ 'OPERATIONAL_SUB_AXE_LABEL', 'event_id', 'event_start_date', 'event_end_date','country_id','promo_cluster_event',\n",
        "                            'promo_cluster_event_ean', 'event_status', 'event_description', 'ean_event_id', 'hierarchy_is_ean']\n",
        "\n",
        "\n",
        "columns_relevant_to_product = ['hierarchy_code', 'client', 'ean'\n",
        "                               ]"
      ],
      "metadata": {
        "id": "fpmgni75Sqqx"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = result_df.drop(events_columns_to_delete+columns_relevant_to_product, axis=1)"
      ],
      "metadata": {
        "id": "mbEYVocnu1NO"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#let's eliminate tactics and subtatics for further analysis\n",
        "#data = df.drop(['sub_tactic', 'tactic', 'depth_of_discount_range', 'price_range'], axis=1)"
      ],
      "metadata": {
        "id": "3Bt-PWM-UBVa"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to convert percentage range strings to numeric average\n",
        "def convert_to_average(range_str):\n",
        "    if range_str != None :\n",
        "\n",
        "      low, high = map(lambda x: float(x.replace('%', '')), range_str.split('-'))\n",
        "      return (low + high) / 2\n",
        "    return None\n",
        "\n",
        "# Applying the conversion to the 'depth_of_discount_range' column\n",
        "df['avg_discount'] = df['depth_of_discount_range'].apply(convert_to_average)"
      ],
      "metadata": {
        "id": "XcCjMFSu-ALr"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pnls = ['DEFENSIVE OFF Incremental', 'DEFENSIVE ON EXCL ROLLBACK During', 'NG NET SALES During', 'PREPACK DISPLAYS During', 'ACTIVE SUPPORT OFF CUST AGREM Incremental',\n",
        " 'SELL OUT VALUE Before', 'INTERNATIONAL CUSTOMER INVESTMENT Before', 'GROSS SALES Incremental', 'SERVICES Before', 'DEFENSIVE ON Incremental',\n",
        " 'SERVICES During', 'ACTIVE SUPPORT OFF CUST AGREM Before', 'ACTIVE SUPPORT OFF EXCL CUST AGREM Before', 'FINANCE OFF Before', 'SHOPPER INVESTMENT OFF Before',\n",
        " 'ACTIVE SUPPORT OFF EXCL CUST AGREM Incremental', 'ACTIVE SUPPORT OFF CUST AGREM During', 'TOTAL COST During', 'FINANCE OFF During',\n",
        " 'GROSS SALES Before', 'ACTIVE SUPPORT OFF During', 'TOTAL COST Before', 'INVOICED SALES During', 'INVOICED SALES Incremental', 'CLEARANCES AND RETURNS Before',\n",
        " 'RETURNS Incremental', 'SHOPPER INVESTMENT OFF During', 'CLEARANCES AND RETURNS Incremental', 'PNL CHECK Before', 'SERVICES Incremental', 'ROLLBACK Incremental',\n",
        " 'ACTIVE SUPPORT ON Incremental', 'ROLLBACK During', 'STD COST OF SALES AND VARIANCES During', 'EFFICIENCY ON Incremental', 'COST OF SALES Before',\n",
        " 'GROSS SALES During', 'THEORETICAL SELLOUT VALUE Before', 'INTERNATIONAL CUSTOMER INVESTMENT During', 'ACTIVE SUPPORT OFF Before', 'GROWTH INCENTIVE Incremental',\n",
        " 'CLEARANCES AND RETURNS During', 'EFFICIENCY OFF During', 'DEFENSIVE OFF During', 'SELL OUT VALUE Incremental', 'EFFICIENCY OFF Incremental',\n",
        " 'PNL CHECK Incremental', 'EFFICIENCY ON Before', 'ROYAL TA MS Before', 'PHYSICAL DISTRIBUTION Before', 'OBSOLETE SLOW MOVING RETURNS Before',\n",
        " 'EFFICIENCY ON During', 'SHOPPER INVESTMENT ON During', 'DEFENSIVE ON During', 'UNITS Incremental', 'FINANCE OFF Incremental', 'INTERNATIONAL CUSTOMER INVESTMENT Incremental',\n",
        " 'SPECIAL OPS COST During', 'SHOPPER INVESTMENT ON Incremental', 'CLEARANCES During', 'GROSS MARGIN Incremental', 'CLEARANCES Incremental',\n",
        " 'CONSO NET SALES Incremental', 'NG NET SALES Before', 'RETURNS Before', 'UNITS During', 'OBSOLETE SLOW MOVING RETURNS During', 'TOTAL COST Incremental',\n",
        " 'CONSO NET SALES During', 'GROSS MARGIN During', 'DEFENSIVE ON EXCL ROLLBACK Before', 'COST OF SALES During', 'ACTIVE SUPPORT OFF EXCL CUST AGREM During',\n",
        " 'ACTIVE SUPPORT ON During', 'ROLLBACK Before', 'CLEARANCES Before', 'EFFICIENCY OFF Before', 'UNITS Before', 'SELL OUT VALUE During', 'PNL CHECK During',\n",
        " 'COST OF SALES Incremental', 'GROWTH INCENTIVE During', 'PREPACK DISPLAYS Before', 'ROYAL TA MS During', 'SPECIAL OPS COST Before',\n",
        " 'DEFENSIVE OFF Before', 'ACTIVE SUPPORT ON Before', 'GROWTH INCENTIVE Before', 'DEFENSIVE ON EXCL ROLLBACK Incremental', 'GROSS MARGIN Before',\n",
        " 'SHOPPER INVESTMENT OFF Incremental', 'DEFENSIVE ON Before', 'SHOPPER INVESTMENT ON Before', 'RETURNS During', 'STD COST OF SALES AND VARIANCES Before',\n",
        " 'CONSO NET SALES Before', 'PHYSICAL DISTRIBUTION During', 'NG NET SALES Incremental', 'ACTIVE SUPPORT OFF Incremental', 'INVOICED SALES Before']"
      ],
      "metadata": {
        "id": "M2vRo8-B_fyI"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=df.copy()"
      ],
      "metadata": {
        "id": "F4XOVSxEOfdp"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = df.drop(pnls, axis=1)"
      ],
      "metadata": {
        "id": "Byz_vob8_h3C"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop('depth_of_discount_range',axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "T08JxrKi_rce"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "Xpmfxuh0AKlD"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "EJTujROs0EL7",
        "outputId": "c680bdcc-dc42-49b8-f635-4e38ae3bc13c"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    global_channel_type     sold_units  sold_value_loc  start_date  \\\n",
              "441              Online  307.000000000         1940.00  2023-05-28   \n",
              "443              Online  131.000000000         1036.46  2023-04-30   \n",
              "444              Online  152.000000000          915.77  2023-09-10   \n",
              "454              Online  250.000000000         1282.60  2023-10-22   \n",
              "456              Online  145.000000000         1010.09  2024-02-04   \n",
              "\n",
              "       end_date    mid_date  is_promo  week  year  Final_ISP     sub_axis  \\\n",
              "441  2023-06-03  2023-05-31      True    22  2023        8.0  Face Makeup   \n",
              "443  2023-05-06  2023-05-03      True    18  2023        8.0  Face Makeup   \n",
              "444  2023-09-16  2023-09-13      True    37  2023        8.0  Face Makeup   \n",
              "454  2023-10-28  2023-10-25      True    43  2023        8.0  Face Makeup   \n",
              "456  2024-02-10  2024-02-07      True     6  2024        8.0  Face Makeup   \n",
              "\n",
              "     Filled_Final_ISP  estimated_price  seasonality_index  price_range  \\\n",
              "441               8.0         6.319218           1.016535          6.3   \n",
              "443               8.0         7.911908           1.037166          7.7   \n",
              "444               8.0         6.024803           0.967239          6.0   \n",
              "454               8.0         5.130400           0.988779          5.5   \n",
              "456               8.0         6.966138           0.994004          6.5   \n",
              "\n",
              "                               tactic                   sub_tactic  \\\n",
              "441  Promotion OCA , Shopper Discount     Online , Save , Site Fee   \n",
              "443  Promotion OCA , Shopper Discount     Online , Save , Site Fee   \n",
              "444          MultiBuy , Promotion OCA  3 for 2 , Online , Site Fee   \n",
              "454  Promotion OCA , Shopper Discount     Online , Save , Site Fee   \n",
              "456  Promotion OCA , Shopper Discount              Save , Site Fee   \n",
              "\n",
              "     avg_discount  \n",
              "441          22.5  \n",
              "443           2.5  \n",
              "444          27.5  \n",
              "454          32.5  \n",
              "456          17.5  "
            ],
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              "\n",
              "  <div id=\"df-8aac9e4d-a89d-4adf-81d8-df6d9c7136e8\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>global_channel_type</th>\n",
              "      <th>sold_units</th>\n",
              "      <th>sold_value_loc</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>mid_date</th>\n",
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              "      <th>year</th>\n",
              "      <th>Final_ISP</th>\n",
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              "      <th>Filled_Final_ISP</th>\n",
              "      <th>estimated_price</th>\n",
              "      <th>seasonality_index</th>\n",
              "      <th>price_range</th>\n",
              "      <th>tactic</th>\n",
              "      <th>sub_tactic</th>\n",
              "      <th>avg_discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>441</th>\n",
              "      <td>Online</td>\n",
              "      <td>307.000000000</td>\n",
              "      <td>1940.00</td>\n",
              "      <td>2023-05-28</td>\n",
              "      <td>2023-06-03</td>\n",
              "      <td>2023-05-31</td>\n",
              "      <td>True</td>\n",
              "      <td>22</td>\n",
              "      <td>2023</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Face Makeup</td>\n",
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              "      <td>6.319218</td>\n",
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              "      <td>6.3</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Online , Save , Site Fee</td>\n",
              "      <td>22.5</td>\n",
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              "    <tr>\n",
              "      <th>443</th>\n",
              "      <td>Online</td>\n",
              "      <td>131.000000000</td>\n",
              "      <td>1036.46</td>\n",
              "      <td>2023-04-30</td>\n",
              "      <td>2023-05-06</td>\n",
              "      <td>2023-05-03</td>\n",
              "      <td>True</td>\n",
              "      <td>18</td>\n",
              "      <td>2023</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.911908</td>\n",
              "      <td>1.037166</td>\n",
              "      <td>7.7</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Online , Save , Site Fee</td>\n",
              "      <td>2.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>444</th>\n",
              "      <td>Online</td>\n",
              "      <td>152.000000000</td>\n",
              "      <td>915.77</td>\n",
              "      <td>2023-09-10</td>\n",
              "      <td>2023-09-16</td>\n",
              "      <td>2023-09-13</td>\n",
              "      <td>True</td>\n",
              "      <td>37</td>\n",
              "      <td>2023</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.024803</td>\n",
              "      <td>0.967239</td>\n",
              "      <td>6.0</td>\n",
              "      <td>MultiBuy , Promotion OCA</td>\n",
              "      <td>3 for 2 , Online , Site Fee</td>\n",
              "      <td>27.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454</th>\n",
              "      <td>Online</td>\n",
              "      <td>250.000000000</td>\n",
              "      <td>1282.60</td>\n",
              "      <td>2023-10-22</td>\n",
              "      <td>2023-10-28</td>\n",
              "      <td>2023-10-25</td>\n",
              "      <td>True</td>\n",
              "      <td>43</td>\n",
              "      <td>2023</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>5.130400</td>\n",
              "      <td>0.988779</td>\n",
              "      <td>5.5</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Online , Save , Site Fee</td>\n",
              "      <td>32.5</td>\n",
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              "      <td>Online</td>\n",
              "      <td>145.000000000</td>\n",
              "      <td>1010.09</td>\n",
              "      <td>2024-02-04</td>\n",
              "      <td>2024-02-10</td>\n",
              "      <td>2024-02-07</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>6.966138</td>\n",
              "      <td>0.994004</td>\n",
              "      <td>6.5</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Save , Site Fee</td>\n",
              "      <td>17.5</td>\n",
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              "  .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-b8e1060d-d556-47fd-a155-8067ed8adadb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"global_channel_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Online\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sold_units\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"131.000000000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sold_value_loc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 415.68663417290674,\n        \"min\": 915.77,\n        \"max\": 1940.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1036.46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"start_date\",\n      \"properties\": {\n        \"dtype\": \"dbdate\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-04-30\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"end_date\",\n      \"properties\": {\n        \"dtype\": \"dbdate\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-05-06\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mid_date\",\n      \"properties\": {\n        \"dtype\": \"dbdate\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2023-05-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_promo\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final_ISP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 8.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sub_axis\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Face Makeup\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Filled_Final_ISP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 8.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimated_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0417191860170165,\n        \"min\": 5.1304,\n        \"max\": 7.911908396946565,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.911908396946565\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.026865803155916034,\n        \"min\": 0.967239167,\n        \"max\": 1.037166105,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.037166105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_range\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8185352771872451,\n        \"min\": 5.5,\n        \"max\": 7.7,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tactic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"MultiBuy , Promotion OCA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sub_tactic\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Online , Save , Site Fee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.510864433221338,\n        \"min\": 2.5,\n        \"max\": 32.5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 441,\n            'f': \"441\",\n        },\n\"Online\",\n{\n            'v': \"307.000000000\",\n            'f': \"\\\"307.000000000\\\"\",\n        },\n{\n            'v': 1940.0,\n            'f': \"1940.0\",\n        },\n\"2023-05-28\",\n\"2023-06-03\",\n\"2023-05-31\",\ntrue,\n{\n            'v': 22,\n            'f': \"22\",\n        },\n{\n            'v': 2023,\n            'f': \"2023\",\n        },\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 6.319218241042345,\n            'f': \"6.319218241042345\",\n        },\n{\n            'v': 1.016535221,\n            'f': \"1.016535221\",\n        },\n{\n            'v': 6.3,\n            'f': \"6.3\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\",\n{\n            'v': 22.5,\n            'f': \"22.5\",\n        }],\n [{\n            'v': 443,\n            'f': \"443\",\n        },\n\"Online\",\n{\n            'v': \"131.000000000\",\n            'f': \"\\\"131.000000000\\\"\",\n        },\n{\n            'v': 1036.46,\n            'f': \"1036.46\",\n        },\n\"2023-04-30\",\n\"2023-05-06\",\n\"2023-05-03\",\ntrue,\n{\n            'v': 18,\n            'f': \"18\",\n        },\n{\n            'v': 2023,\n            'f': \"2023\",\n        },\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 7.911908396946565,\n            'f': \"7.911908396946565\",\n        },\n{\n            'v': 1.037166105,\n            'f': \"1.037166105\",\n        },\n{\n            'v': 7.7,\n            'f': \"7.7\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\",\n{\n            'v': 2.5,\n            'f': \"2.5\",\n        }],\n [{\n            'v': 444,\n            'f': \"444\",\n        },\n\"Online\",\n{\n            'v': \"152.000000000\",\n            'f': \"\\\"152.000000000\\\"\",\n        },\n{\n            'v': 915.77,\n            'f': \"915.77\",\n        },\n\"2023-09-10\",\n\"2023-09-16\",\n\"2023-09-13\",\ntrue,\n{\n            'v': 37,\n            'f': \"37\",\n        },\n{\n            'v': 2023,\n            'f': \"2023\",\n        },\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 6.024802631578948,\n            'f': \"6.024802631578948\",\n        },\n{\n            'v': 0.967239167,\n            'f': \"0.967239167\",\n        },\n{\n            'v': 6.0,\n            'f': \"6.0\",\n        },\n\"MultiBuy , Promotion OCA\",\n\"3 for 2 , Online , Site Fee\",\n{\n            'v': 27.5,\n            'f': \"27.5\",\n        }],\n [{\n            'v': 454,\n            'f': \"454\",\n        },\n\"Online\",\n{\n            'v': \"250.000000000\",\n            'f': \"\\\"250.000000000\\\"\",\n        },\n{\n            'v': 1282.6,\n            'f': \"1282.6\",\n        },\n\"2023-10-22\",\n\"2023-10-28\",\n\"2023-10-25\",\ntrue,\n{\n            'v': 43,\n            'f': \"43\",\n        },\n{\n            'v': 2023,\n            'f': \"2023\",\n        },\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 5.1304,\n            'f': \"5.1304\",\n        },\n{\n            'v': 0.988779444,\n            'f': \"0.988779444\",\n        },\n{\n            'v': 5.5,\n            'f': \"5.5\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\",\n{\n            'v': 32.5,\n            'f': \"32.5\",\n        }],\n [{\n            'v': 456,\n            'f': \"456\",\n        },\n\"Online\",\n{\n            'v': \"145.000000000\",\n            'f': \"\\\"145.000000000\\\"\",\n        },\n{\n            'v': 1010.09,\n            'f': \"1010.09\",\n        },\n\"2024-02-04\",\n\"2024-02-10\",\n\"2024-02-07\",\ntrue,\n{\n            'v': 6,\n            'f': \"6\",\n        },\n{\n            'v': 2024,\n            'f': \"2024\",\n        },\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 6.966137931034483,\n            'f': \"6.966137931034483\",\n        },\n{\n            'v': 0.994003919,\n            'f': \"0.994003919\",\n        },\n{\n            'v': 6.5,\n            'f': \"6.5\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Save , Site Fee\",\n{\n            'v': 17.5,\n            'f': \"17.5\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"global_channel_type\"], [\"number\", \"sold_units\"], [\"number\", \"sold_value_loc\"], [\"string\", \"start_date\"], [\"string\", \"end_date\"], [\"string\", \"mid_date\"], [\"string\", \"is_promo\"], [\"number\", \"week\"], [\"number\", \"year\"], [\"number\", \"Final_ISP\"], [\"string\", \"sub_axis\"], [\"number\", \"Filled_Final_ISP\"], [\"number\", \"estimated_price\"], [\"number\", \"seasonality_index\"], [\"number\", \"price_range\"], [\"string\", \"tactic\"], [\"string\", \"sub_tactic\"], [\"number\", \"avg_discount\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-b62dcc19-d4ca-42be-aeb1-08afd913e671\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b62dcc19-d4ca-42be-aeb1-08afd913e671')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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-b62dcc19-d4ca-42be-aeb1-08afd913e671 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data.columns"
      ],
      "metadata": {
        "id": "9P0A0Jllu-Qi"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('start_date').size().reset_index(name='counts')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "ZPucU8I1wwmT",
        "outputId": "ac2cd5bc-81ce-4f5f-a334-88e8606a3cb9"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     start_date  counts\n",
              "0    2019-12-29       1\n",
              "1    2020-01-05       1\n",
              "2    2020-01-12       1\n",
              "3    2020-01-19       1\n",
              "4    2020-01-26       1\n",
              "..          ...     ...\n",
              "216  2024-02-18       1\n",
              "217  2024-02-25       1\n",
              "218  2024-03-03       1\n",
              "219  2024-03-10       2\n",
              "220  2024-03-17       1\n",
              "\n",
              "[221 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-49bc293e-bd8e-4545-9960-7cea8d3921e7\" class=\"colab-df-container\">\n",
              "    <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>start_date</th>\n",
              "      <th>counts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-12-29</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-01-05</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-19</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2024-02-18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2024-02-25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2024-03-03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2024-03-10</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>221 rows × 2 columns</p>\n",
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              "        document.querySelector('#df-49bc293e-bd8e-4545-9960-7cea8d3921e7 button.colab-df-convert');\n",
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              "        const dataTable =\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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}],\n [{\n            'v': 212,\n            'f': \"212\",\n        },\n\"2024-01-21\",\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 213,\n            'f': \"213\",\n        },\n\"2024-01-28\",\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 214,\n            'f': \"214\",\n        },\n\"2024-02-04\",\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 215,\n            'f': \"215\",\n        },\n\"2024-02-11\",\n{\n            'v': 2,\n            'f': \"2\",\n        }],\n [{\n            'v': 216,\n            'f': \"216\",\n        },\n\"2024-02-18\",\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 217,\n            'f': \"217\",\n        },\n\"2024-02-25\",\n{\n            'v': 1,\n            'f': \"1\",\n        }],\n [{\n            'v': 218,\n            'f': \"218\",\n        },\n\"2024-03-03\",\n{\n            'v': 1,\n            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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: 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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-7ce4feaa-af42-4db5-8709-ff4b8583027e button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_data = data[data['start_date']==pd.Timestamp('2023-07-30')]\n",
        "date_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gxd9pnKhwH6L",
        "outputId": "17571e80-d773-4362-b39e-3ce5f1c7b9b0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in data.columns.to_list():\n",
        "  count = date_data[col].unique().shape[0]\n",
        "  if count>1:\n",
        "    print(col, count )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaAPLullyBXU",
        "outputId": "f20b6190-b8da-44f6-856e-315d2853b2e6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "price_range 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_data[['sold_units', 'avg_discount', 'price_range', 'tactic', 'sub_tactic']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "_caAx6o1OrXC",
        "outputId": "236b1853-c10b-4b05-817a-f788b530a038"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sold_units  avg_discount  price_range  \\\n",
              "318  177.000000000           7.5          7.4   \n",
              "319  177.000000000           7.5          7.5   \n",
              "\n",
              "                               tactic                sub_tactic  \n",
              "318  Promotion OCA , Shopper Discount  Online , Save , Site Fee  \n",
              "319  Promotion OCA , Shopper Discount  Online , Save , Site Fee  "
            ],
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              "      <th>sold_units</th>\n",
              "      <th>avg_discount</th>\n",
              "      <th>price_range</th>\n",
              "      <th>tactic</th>\n",
              "      <th>sub_tactic</th>\n",
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              "    <tr>\n",
              "      <th>318</th>\n",
              "      <td>177.000000000</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.4</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
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              "    <tr>\n",
              "      <th>319</th>\n",
              "      <td>177.000000000</td>\n",
              "      <td>7.5</td>\n",
              "      <td>7.5</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-42dc98d7-fe58-46df-95ac-43984fae79a5 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-42dc98d7-fe58-46df-95ac-43984fae79a5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "</svg>\n",
              "  </button>\n",
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              "  }\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",
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              "      border-top-color: var(--fill-color);\n",
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              "      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",
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              "      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",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"date_data[['sold_units', 'avg_discount', 'price_range', 'tactic', 'sub_tactic']]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"sold_units\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"177.000000000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7.5,\n        \"max\": 7.5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_range\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0707106781186545,\n        \"min\": 7.4,\n        \"max\": 7.5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tactic\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Promotion OCA , Shopper Discount\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sub_tactic\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Online , Save , Site Fee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 318,\n            'f': \"318\",\n        },\n{\n            'v': \"177.000000000\",\n            'f': \"\\\"177.000000000\\\"\",\n        },\n{\n            'v': 7.5,\n            'f': \"7.5\",\n        },\n{\n            'v': 7.4,\n            'f': \"7.4\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"],\n [{\n            'v': 319,\n            'f': \"319\",\n        },\n{\n            'v': \"177.000000000\",\n            'f': \"\\\"177.000000000\\\"\",\n        },\n{\n            'v': 7.5,\n            'f': \"7.5\",\n        },\n{\n            'v': 7.5,\n            'f': \"7.5\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"]],\n        columns: [[\"number\", \"index\"], [\"number\", \"sold_units\"], [\"number\", \"avg_discount\"], [\"number\", \"price_range\"], [\"string\", \"tactic\"], [\"string\", \"sub_tactic\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-c6f65daf-a042-4c9c-aacb-b71d5c4f8bd0\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6f65daf-a042-4c9c-aacb-b71d5c4f8bd0')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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-c6f65daf-a042-4c9c-aacb-b71d5c4f8bd0 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result = data.groupby('start_date').agg({\n",
        "    'avg_discount': lambda x: x.mean() if len(x) > 1 else x.iloc[0],\n",
        "    'price_range': lambda x: x.mean() if len(x) > 1 else x.iloc[0],\n",
        "    'tactic': lambda x: ', '.join(x) if len(x) > 1 else x.iloc[0],\n",
        "    'sub_tactic': lambda x: ', '.join(x) if len(x) > 1 else x.iloc[0]\n",
        "}).reset_index()"
      ],
      "metadata": {
        "id": "HBfxTPoWAUMW"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "HLrbmuMnAbv8",
        "outputId": "ac8a2db3-1705-4e4b-9d4b-e6895982de92"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     start_date  avg_discount  price_range  \\\n",
              "216  2024-02-18          17.5          6.7   \n",
              "217  2024-02-25          17.5          6.7   \n",
              "218  2024-03-03          17.5          6.7   \n",
              "219  2024-03-10          15.0          6.9   \n",
              "220  2024-03-17          12.5          7.1   \n",
              "\n",
              "                                                tactic  \\\n",
              "216                   Promotion OCA , Shopper Discount   \n",
              "217                   Promotion OCA , Shopper Discount   \n",
              "218                   Promotion OCA , Shopper Discount   \n",
              "219  Promotion OCA , Shopper Discount, Promotion OC...   \n",
              "220                   Promotion OCA , Shopper Discount   \n",
              "\n",
              "                                            sub_tactic  \n",
              "216                           Online , Save , Site Fee  \n",
              "217                           Online , Save , Site Fee  \n",
              "218                           Online , Save , Site Fee  \n",
              "219  Online , Save , Site Fee, Online , Save , Site...  \n",
              "220                           Online , Save , Site Fee  "
            ],
            "text/html": [
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              "      <th>avg_discount</th>\n",
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              "      <th>tactic</th>\n",
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              "      <th>216</th>\n",
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              "    <tr>\n",
              "      <th>217</th>\n",
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              "      <td>17.5</td>\n",
              "      <td>6.7</td>\n",
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              "      <td>Online , Save , Site Fee</td>\n",
              "    </tr>\n",
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              "      <td>2024-03-03</td>\n",
              "      <td>17.5</td>\n",
              "      <td>6.7</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Online , Save , Site Fee</td>\n",
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              "      <td>2024-03-10</td>\n",
              "      <td>15.0</td>\n",
              "      <td>6.9</td>\n",
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              "      <th>220</th>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>12.5</td>\n",
              "      <td>7.1</td>\n",
              "      <td>Promotion OCA , Shopper Discount</td>\n",
              "      <td>Online , Save , Site Fee</td>\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a92e5a67-00c3-491d-8ae0-e0b0c4931f4a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a92e5a67-00c3-491d-8ae0-e0b0c4931f4a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "</svg>\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    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",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    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",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"result\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"start_date\",\n      \"properties\": {\n        \"dtype\": \"dbdate\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2024-02-25\",\n          \"2024-03-17\",\n          \"2024-03-03\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.23606797749979,\n        \"min\": 12.5,\n        \"max\": 17.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          17.5,\n          15.0,\n          12.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_range\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.17888543819998298,\n        \"min\": 6.7,\n        \"max\": 7.1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6.7,\n          6.9,\n          7.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"tactic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Promotion OCA , Shopper Discount, Promotion OCA , Shopper Discount\",\n          \"Promotion OCA , Shopper Discount\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sub_tactic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Online , Save , Site Fee, Online , Save , Site Fee\",\n          \"Online , Save , Site Fee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 216,\n            'f': \"216\",\n        },\n\"2024-02-18\",\n{\n            'v': 17.5,\n            'f': \"17.5\",\n        },\n{\n            'v': 6.7,\n            'f': \"6.7\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"],\n [{\n            'v': 217,\n            'f': \"217\",\n        },\n\"2024-02-25\",\n{\n            'v': 17.5,\n            'f': \"17.5\",\n        },\n{\n            'v': 6.7,\n            'f': \"6.7\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"],\n [{\n            'v': 218,\n            'f': \"218\",\n        },\n\"2024-03-03\",\n{\n            'v': 17.5,\n            'f': \"17.5\",\n        },\n{\n            'v': 6.7,\n            'f': \"6.7\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"],\n [{\n            'v': 219,\n            'f': \"219\",\n        },\n\"2024-03-10\",\n{\n            'v': 15.0,\n            'f': \"15.0\",\n        },\n{\n            'v': 6.9,\n            'f': \"6.9\",\n        },\n\"Promotion OCA , Shopper Discount, Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee, Online , Save , Site Fee\"],\n [{\n            'v': 220,\n            'f': \"220\",\n        },\n\"2024-03-17\",\n{\n            'v': 12.5,\n            'f': \"12.5\",\n        },\n{\n            'v': 7.1,\n            'f': \"7.1\",\n        },\n\"Promotion OCA , Shopper Discount\",\n\"Online , Save , Site Fee\"]],\n        columns: [[\"number\", \"index\"], [\"string\", \"start_date\"], [\"number\", \"avg_discount\"], [\"number\", \"price_range\"], [\"string\", \"tactic\"], [\"string\", \"sub_tactic\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-3794b0a2-8ff9-4de8-aa15-41ecf9d76aa7\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3794b0a2-8ff9-4de8-aa15-41ecf9d76aa7')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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    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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-3794b0a2-8ff9-4de8-aa15-41ecf9d76aa7 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fusionner les résultats agrégés avec le DataFrame original en utilisant 'start_date'\n",
        "merged_data = pd.merge(data, result, on='start_date', suffixes=('', '_agg'))\n",
        "\n",
        "# Sélectionner les colonnes et remplacer les valeurs originales par les agrégées si nécessaire\n",
        "for column in ['avg_discount', 'price_range', 'tactic', 'sub_tactic']:\n",
        "    merged_data[column] = merged_data[column + '_agg']\n",
        "    merged_data.drop(column + '_agg', axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "32-BibHqBSe9"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_data.drop_duplicates(inplace=True)"
      ],
      "metadata": {
        "id": "kei1Xq4NBXRF"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_data.groupby('start_date').size().reset_index(name='counts')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 660
        },
        "id": "qVWX-630Bhsh",
        "outputId": "f0bd6898-36c9-4f83-cfb7-e4ca27ff873b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     start_date  counts\n",
              "0    2019-12-29       1\n",
              "1    2020-01-05       1\n",
              "2    2020-01-12       1\n",
              "3    2020-01-19       1\n",
              "4    2020-01-26       1\n",
              "..          ...     ...\n",
              "216  2024-02-18       1\n",
              "217  2024-02-25       1\n",
              "218  2024-03-03       1\n",
              "219  2024-03-10       1\n",
              "220  2024-03-17       1\n",
              "\n",
              "[221 rows x 2 columns]"
            ],
            "text/html": [
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              "        vertical-align: middle;\n",
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              "      <th>start_date</th>\n",
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              "      <td>2019-12-29</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>2024-02-18</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>2024-02-25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>2024-03-03</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>2024-03-10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>2024-03-17</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>221 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-4bb107d1-17fe-4bc7-bb8e-5b42b80b0a26 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4bb107d1-17fe-4bc7-bb8e-5b42b80b0a26');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    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",
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              "  }\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",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    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",
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              "        document.querySelector('#df-f780fa3d-9780-43d3-b28c-39ad8285440c button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"merged_data\",\n  \"rows\": 221,\n  \"fields\": [\n    {\n      \"column\": \"start_date\",\n      \"properties\": {\n        \"dtype\": \"dbdate\",\n        \"num_unique_values\": 221,\n        \"samples\": [\n          \"2022-07-10\",\n          \"2022-10-30\",\n          \"2021-10-10\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"counts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_duplicates_from_string(input_string):\n",
        "  if input_string==None : return None\n",
        "  unique_items = set(input_string.split(', '))\n",
        "  cleaned_string = ', '.join(sorted(unique_items))\n",
        "  return cleaned_string\n",
        "\n",
        "merged_data['tactic'] = merged_data['tactic'].apply(remove_duplicates_from_string)\n",
        "merged_data['sub_tactic'] = merged_data['sub_tactic'].apply(remove_duplicates_from_string)"
      ],
      "metadata": {
        "id": "ZHDFe4JrCWeX"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 325
        },
        "id": "0fDgnVsRCerz",
        "outputId": "7fd054fc-5b8d-48ca-bcbf-ac033d303b66"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  global_channel_type     sold_units  sold_value_loc  start_date    end_date  \\\n",
              "0              Online  112.000000000          887.30  2021-11-14  2021-11-20   \n",
              "1              Online   99.000000000          789.45  2021-05-30  2021-06-05   \n",
              "2              Online   82.000000000          528.68  2020-01-19  2020-01-25   \n",
              "3              Online  135.000000000          997.10  2022-02-06  2022-02-12   \n",
              "4              Online  192.000000000         1517.45  2021-08-01  2021-08-07   \n",
              "\n",
              "     mid_date  is_promo  week  year  Final_ISP     sub_axis  Filled_Final_ISP  \\\n",
              "0  2021-11-17     False    46  2021        NaN  Face Makeup               8.0   \n",
              "1  2021-06-02     False    22  2021        NaN  Face Makeup               8.0   \n",
              "2  2020-01-22     False     3  2020        NaN  Face Makeup               8.0   \n",
              "3  2022-02-09     False     6  2022        8.0  Face Makeup               8.0   \n",
              "4  2021-08-04     False    31  2021        NaN  Face Makeup               8.0   \n",
              "\n",
              "   estimated_price  seasonality_index  price_range tactic sub_tactic  \\\n",
              "0         7.922321           1.038199          NaN   None       None   \n",
              "1         7.974242           1.074688          NaN   None       None   \n",
              "2         6.447317           1.000000          NaN   None       None   \n",
              "3         7.385926           1.043246          NaN   None       None   \n",
              "4         7.903385           1.031296          NaN   None       None   \n",
              "\n",
              "   avg_discount  \n",
              "0           NaN  \n",
              "1           NaN  \n",
              "2           NaN  \n",
              "3           NaN  \n",
              "4           NaN  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8bd7cd48-8b08-47f5-81b4-9f7192a66d1d\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>global_channel_type</th>\n",
              "      <th>sold_units</th>\n",
              "      <th>sold_value_loc</th>\n",
              "      <th>start_date</th>\n",
              "      <th>end_date</th>\n",
              "      <th>mid_date</th>\n",
              "      <th>is_promo</th>\n",
              "      <th>week</th>\n",
              "      <th>year</th>\n",
              "      <th>Final_ISP</th>\n",
              "      <th>sub_axis</th>\n",
              "      <th>Filled_Final_ISP</th>\n",
              "      <th>estimated_price</th>\n",
              "      <th>seasonality_index</th>\n",
              "      <th>price_range</th>\n",
              "      <th>tactic</th>\n",
              "      <th>sub_tactic</th>\n",
              "      <th>avg_discount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Online</td>\n",
              "      <td>112.000000000</td>\n",
              "      <td>887.30</td>\n",
              "      <td>2021-11-14</td>\n",
              "      <td>2021-11-20</td>\n",
              "      <td>2021-11-17</td>\n",
              "      <td>False</td>\n",
              "      <td>46</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.922321</td>\n",
              "      <td>1.038199</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Online</td>\n",
              "      <td>99.000000000</td>\n",
              "      <td>789.45</td>\n",
              "      <td>2021-05-30</td>\n",
              "      <td>2021-06-05</td>\n",
              "      <td>2021-06-02</td>\n",
              "      <td>False</td>\n",
              "      <td>22</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.974242</td>\n",
              "      <td>1.074688</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Online</td>\n",
              "      <td>82.000000000</td>\n",
              "      <td>528.68</td>\n",
              "      <td>2020-01-19</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>False</td>\n",
              "      <td>3</td>\n",
              "      <td>2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>6.447317</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Online</td>\n",
              "      <td>135.000000000</td>\n",
              "      <td>997.10</td>\n",
              "      <td>2022-02-06</td>\n",
              "      <td>2022-02-12</td>\n",
              "      <td>2022-02-09</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
              "      <td>2022</td>\n",
              "      <td>8.0</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.385926</td>\n",
              "      <td>1.043246</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Online</td>\n",
              "      <td>192.000000000</td>\n",
              "      <td>1517.45</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>2021-08-07</td>\n",
              "      <td>2021-08-04</td>\n",
              "      <td>False</td>\n",
              "      <td>31</td>\n",
              "      <td>2021</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Face Makeup</td>\n",
              "      <td>8.0</td>\n",
              "      <td>7.903385</td>\n",
              "      <td>1.031296</td>\n",
              "      <td>NaN</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
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              "summary": "{\n  \"name\": \"merged_data\",\n  \"rows\": 221,\n  \"fields\": [\n    {\n      \"column\": \"global_channel_type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Online\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sold_units\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"251.000000000\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sold_value_loc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 501.65566930576045,\n        \"min\": 0.0,\n        \"max\": 4470.21,\n        \"num_unique_values\": 221,\n        \"samples\": [\n          722.17\n        ],\n        \"semantic_type\": \"\",\n        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\"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 52,\n        \"samples\": [\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"Int64\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          2021\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Final_ISP\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 8.0,\n        \"max\": 8.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n 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 \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sub_tactic\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Save , Site Fee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.676616169611394,\n        \"min\": 2.5,\n        \"max\": 32.5,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          12.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"Online\",\n{\n            'v': \"112.000000000\",\n            'f': \"\\\"112.000000000\\\"\",\n        },\n{\n            'v': 887.3,\n            'f': \"887.3\",\n        },\n\"2021-11-14\",\n\"2021-11-20\",\n\"2021-11-17\",\nfalse,\n{\n            'v': 46,\n            'f': \"46\",\n        },\n{\n            'v': 2021,\n            'f': \"2021\",\n        },\n{\n            'v': NaN,\n            'f': \"NaN\",\n        },\n\"Face Makeup\",\n{\n            'v': 8.0,\n            'f': \"8.0\",\n        },\n{\n            'v': 7.922321428571428,\n            'f': \"7.922321428571428\",\n        },\n{\n            'v': 1.038199109,\n            'f': \"1.038199109\",\n        },\n{\n            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          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = merged_data.copy()"
      ],
      "metadata": {
        "id": "RK9KF-YhvGrh"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "unecessary_columns = ['global_channel_type', 'start_date', 'mid_date', 'Final_ISP', 'sub_axis', 'Filled_Final_ISP']\n",
        "data.drop(unecessary_columns, axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "3n9Z5WQyDEJx"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = data.sort_values(by='end_date')"
      ],
      "metadata": {
        "id": "qKX48KRdCJE2"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace nulls in 'price_range' and 'avrg_discount' with zero\n",
        "data.loc[(data['is_promo'] == False) & (data['price_range'].isnull()), 'price_range'] = 0\n",
        "data.loc[(data['is_promo'] == False) & (data['avg_discount'].isnull()), 'avg_discount'] = 0\n",
        "data.loc[(data['is_promo'] == False) & (data['estimated_price'].isnull()), 'estimated_price'] = 0\n",
        "# Replace nulls in 'tactic' and 'subtactic' with empty strings\n",
        "data.loc[(data['is_promo'] == False) & (data['tactic'].isnull()), 'tactic'] = ''\n",
        "data.loc[(data['is_promo'] == False) & (data['sub_tactic'].isnull()), 'sub_tactic'] = ''"
      ],
      "metadata": {
        "id": "XubDCBCmCNFy"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['sold_units'] = pd.to_numeric(data['sold_units'], errors='coerce')"
      ],
      "metadata": {
        "id": "MWZ4L5flGvTz"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "OqDSnSPVCvfP",
        "outputId": "f3aa8dff-689d-4dc6-dcb4-f2efe183a24d"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     sold_units  sold_value_loc    end_date  is_promo  week  year  \\\n",
              "19         89.0          617.95  2020-01-04     False    52  2019   \n",
              "87         89.0          562.91  2020-01-11     False     1  2020   \n",
              "100        67.0          417.47  2020-01-18     False     2  2020   \n",
              "2          82.0          528.68  2020-01-25     False     3  2020   \n",
              "104        96.0          593.42  2020-02-01     False     4  2020   \n",
              "\n",
              "     estimated_price  seasonality_index  price_range tactic sub_tactic  \\\n",
              "19          6.943258                1.0          0.0                     \n",
              "87          6.324831                1.0          0.0                     \n",
              "100         6.230896                1.0          0.0                     \n",
              "2           6.447317                1.0          0.0                     \n",
              "104         6.181458                1.0          0.0                     \n",
              "\n",
              "     avg_discount  \n",
              "19            0.0  \n",
              "87            0.0  \n",
              "100           0.0  \n",
              "2             0.0  \n",
              "104           0.0  "
            ],
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              "      --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",
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              "      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-7c3868d0-985b-4486-bca6-6b5702817f28 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
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            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[{\n            'v': 19,\n            'f': \"19\",\n        },\n{\n            'v': 89.0,\n            'f': \"89.0\",\n        },\n{\n            'v': 617.95,\n            'f': \"617.95\",\n        },\n\"2020-01-04\",\nfalse,\n{\n            'v': 52,\n            'f': \"52\",\n        },\n{\n            'v': 2019,\n            'f': \"2019\",\n        },\n{\n            'v': 6.943258426966293,\n            'f': \"6.943258426966293\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n\"\",\n\"\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }],\n [{\n            'v': 87,\n            'f': \"87\",\n        },\n{\n            'v': 89.0,\n            'f': \"89.0\",\n        },\n{\n            'v': 562.91,\n            'f': \"562.91\",\n        },\n\"2020-01-11\",\nfalse,\n{\n            'v': 1,\n            'f': \"1\",\n        },\n{\n            'v': 2020,\n            'f': \"2020\",\n        },\n{\n            'v': 6.324831460674157,\n            'f': \"6.324831460674157\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n\"\",\n\"\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }],\n [{\n            'v': 100,\n            'f': \"100\",\n        },\n{\n            'v': 67.0,\n            'f': \"67.0\",\n        },\n{\n            'v': 417.47,\n            'f': \"417.47\",\n        },\n\"2020-01-18\",\nfalse,\n{\n            'v': 2,\n            'f': \"2\",\n        },\n{\n            'v': 2020,\n            'f': \"2020\",\n        },\n{\n            'v': 6.23089552238806,\n            'f': \"6.23089552238806\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n\"\",\n\"\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }],\n [{\n            'v': 2,\n            'f': \"2\",\n        },\n{\n            'v': 82.0,\n            'f': \"82.0\",\n        },\n{\n            'v': 528.68,\n            'f': \"528.68\",\n        },\n\"2020-01-25\",\nfalse,\n{\n            'v': 3,\n            'f': \"3\",\n        },\n{\n            'v': 2020,\n            'f': \"2020\",\n        },\n{\n            'v': 6.447317073170731,\n            'f': \"6.447317073170731\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n\"\",\n\"\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }],\n [{\n            'v': 104,\n            'f': \"104\",\n        },\n{\n            'v': 96.0,\n            'f': \"96.0\",\n        },\n{\n            'v': 593.42,\n            'f': \"593.42\",\n        },\n\"2020-02-01\",\nfalse,\n{\n            'v': 4,\n            'f': \"4\",\n        },\n{\n            'v': 2020,\n            'f': \"2020\",\n        },\n{\n            'v': 6.181458333333333,\n            'f': \"6.181458333333333\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        },\n\"\",\n\"\",\n{\n            'v': 0.0,\n            'f': \"0.0\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"number\", \"sold_units\"], [\"number\", \"sold_value_loc\"], [\"string\", \"end_date\"], [\"string\", \"is_promo\"], [\"number\", \"week\"], [\"number\", \"year\"], [\"number\", \"estimated_price\"], [\"number\", \"seasonality_index\"], [\"number\", \"price_range\"], [\"string\", \"tactic\"], [\"string\", \"sub_tactic\"], [\"number\", \"avg_discount\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-60d82b49-174a-4b99-9120-a918d0c79809\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-60d82b49-174a-4b99-9120-a918d0c79809')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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: 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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-60d82b49-174a-4b99-9120-a918d0c79809 button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 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          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "neukZBdFDAxB",
        "outputId": "daef8786-e6df-4701-bfd4-44e03503b971"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sold_units           0\n",
              "sold_value_loc       0\n",
              "end_date             0\n",
              "is_promo             0\n",
              "week                 0\n",
              "year                 0\n",
              "estimated_price      0\n",
              "seasonality_index    0\n",
              "price_range          0\n",
              "tactic               0\n",
              "sub_tactic           0\n",
              "avg_discount         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TUws5RCiQ9N9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = data.select_dtypes(include=[np.number])"
      ],
      "metadata": {
        "id": "NpegJLjLDwqu"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "XwiqvxD3ElZp",
        "outputId": "b621af6b-e1ff-4253-8933-a3db5ce70436"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   sold_units  sold_value_loc      week      year  \\\n",
              "sold_units           1.000000        0.944361  0.211422  0.004811   \n",
              "sold_value_loc       0.944361        1.000000  0.258911  0.111751   \n",
              "week                 0.211422        0.258911  1.000000 -0.151650   \n",
              "year                 0.004811        0.111751 -0.151650  1.000000   \n",
              "estimated_price     -0.422645       -0.183349  0.035787  0.252688   \n",
              "seasonality_index   -0.118065       -0.019144  0.034865  0.142106   \n",
              "price_range          0.026121        0.080321 -0.133266  0.800410   \n",
              "avg_discount         0.140345        0.131906  0.013437  0.674841   \n",
              "\n",
              "                   estimated_price  seasonality_index  price_range  \\\n",
              "sold_units               -0.422645          -0.118065     0.026121   \n",
              "sold_value_loc           -0.183349          -0.019144     0.080321   \n",
              "week                      0.035787           0.034865    -0.133266   \n",
              "year                      0.252688           0.142106     0.800410   \n",
              "estimated_price           1.000000           0.367248     0.066157   \n",
              "seasonality_index         0.367248           1.000000     0.042650   \n",
              "price_range               0.066157           0.042650     1.000000   \n",
              "avg_discount             -0.170212          -0.249159     0.763723   \n",
              "\n",
              "                   avg_discount  \n",
              "sold_units             0.140345  \n",
              "sold_value_loc         0.131906  \n",
              "week                   0.013437  \n",
              "year                   0.674841  \n",
              "estimated_price       -0.170212  \n",
              "seasonality_index     -0.249159  \n",
              "price_range            0.763723  \n",
              "avg_discount           1.000000  "
            ],
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              "      <th>sold_units</th>\n",
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              "      <th>estimated_price</th>\n",
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              "      <th>sold_units</th>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.422645</td>\n",
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              "      <td>0.026121</td>\n",
              "      <td>0.140345</td>\n",
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              "    <tr>\n",
              "      <th>sold_value_loc</th>\n",
              "      <td>0.944361</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.111751</td>\n",
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              "      <td>-0.019144</td>\n",
              "      <td>0.080321</td>\n",
              "      <td>0.131906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>week</th>\n",
              "      <td>0.211422</td>\n",
              "      <td>0.258911</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.151650</td>\n",
              "      <td>0.035787</td>\n",
              "      <td>0.034865</td>\n",
              "      <td>-0.133266</td>\n",
              "      <td>0.013437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0.004811</td>\n",
              "      <td>0.111751</td>\n",
              "      <td>-0.151650</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.252688</td>\n",
              "      <td>0.142106</td>\n",
              "      <td>0.800410</td>\n",
              "      <td>0.674841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>estimated_price</th>\n",
              "      <td>-0.422645</td>\n",
              "      <td>-0.183349</td>\n",
              "      <td>0.035787</td>\n",
              "      <td>0.252688</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.367248</td>\n",
              "      <td>0.066157</td>\n",
              "      <td>-0.170212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seasonality_index</th>\n",
              "      <td>-0.118065</td>\n",
              "      <td>-0.019144</td>\n",
              "      <td>0.034865</td>\n",
              "      <td>0.142106</td>\n",
              "      <td>0.367248</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.042650</td>\n",
              "      <td>-0.249159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>price_range</th>\n",
              "      <td>0.026121</td>\n",
              "      <td>0.080321</td>\n",
              "      <td>-0.133266</td>\n",
              "      <td>0.800410</td>\n",
              "      <td>0.066157</td>\n",
              "      <td>0.042650</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.763723</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_discount</th>\n",
              "      <td>0.140345</td>\n",
              "      <td>0.131906</td>\n",
              "      <td>0.013437</td>\n",
              "      <td>0.674841</td>\n",
              "      <td>-0.170212</td>\n",
              "      <td>-0.249159</td>\n",
              "      <td>0.763723</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
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appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-c6e5ed1c-7fd0-42bc-9040-1b197484b2f2\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6e5ed1c-7fd0-42bc-9040-1b197484b2f2')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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      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          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_m = data.copy()\n",
        "data_m['is_promo'] = data_m['is_promo'].astype(int)"
      ],
      "metadata": {
        "id": "Sid6m_40GmhE"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_m['is_promo']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4heJlR2KWS__",
        "outputId": "7a1a489b-14ae-4d97-fa58-f0a4d3b80268"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19     0\n",
              "87     0\n",
              "100    0\n",
              "2      0\n",
              "104    0\n",
              "      ..\n",
              "194    1\n",
              "214    1\n",
              "219    1\n",
              "157    1\n",
              "221    1\n",
              "Name: is_promo, Length: 221, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_data = data_m.select_dtypes(include=[np.number])\n",
        "numeric_data.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 327
        },
        "id": "mBzD6fd_GoBp",
        "outputId": "027b6fc2-aa0f-4f59-cab0-41c74aa7280e"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   sold_units  sold_value_loc  is_promo      week      year  \\\n",
              "sold_units           1.000000        0.944361  0.045855  0.211422  0.004811   \n",
              "sold_value_loc       0.944361        1.000000  0.091183  0.258911  0.111751   \n",
              "is_promo             0.045855        0.091183  1.000000 -0.115425  0.807171   \n",
              "week                 0.211422        0.258911 -0.115425  1.000000 -0.151650   \n",
              "year                 0.004811        0.111751  0.807171 -0.151650  1.000000   \n",
              "estimated_price     -0.422645       -0.183349  0.028254  0.035787  0.252688   \n",
              "seasonality_index   -0.118065       -0.019144 -0.005762  0.034865  0.142106   \n",
              "price_range          0.026121        0.080321  0.994141 -0.133266  0.800410   \n",
              "avg_discount         0.140345        0.131906  0.828608  0.013437  0.674841   \n",
              "\n",
              "                   estimated_price  seasonality_index  price_range  \\\n",
              "sold_units               -0.422645          -0.118065     0.026121   \n",
              "sold_value_loc           -0.183349          -0.019144     0.080321   \n",
              "is_promo                  0.028254          -0.005762     0.994141   \n",
              "week                      0.035787           0.034865    -0.133266   \n",
              "year                      0.252688           0.142106     0.800410   \n",
              "estimated_price           1.000000           0.367248     0.066157   \n",
              "seasonality_index         0.367248           1.000000     0.042650   \n",
              "price_range               0.066157           0.042650     1.000000   \n",
              "avg_discount             -0.170212          -0.249159     0.763723   \n",
              "\n",
              "                   avg_discount  \n",
              "sold_units             0.140345  \n",
              "sold_value_loc         0.131906  \n",
              "is_promo               0.828608  \n",
              "week                   0.013437  \n",
              "year                   0.674841  \n",
              "estimated_price       -0.170212  \n",
              "seasonality_index     -0.249159  \n",
              "price_range            0.763723  \n",
              "avg_discount           1.000000  "
            ],
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              "      <th>sold_units</th>\n",
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              "      <td>-0.118065</td>\n",
              "      <td>-0.019144</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      --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-b2aa1292-6d78-41d1-99b6-0ce40d4843da button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"numeric_data\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"sold_units\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.471512592479754,\n        \"min\": -0.4226451087816284,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.02612078162297054,\n          0.9443610806535778,\n          -0.4226451087816284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sold_value_loc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.41667730396317654,\n        \"min\": -0.18334899267452595,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.08032131932811594,\n          1.0,\n          -0.18334899267452595\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_promo\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4810372736083687,\n        \"min\": -0.11542498611109206,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.9941409090742627,\n          0.09118259745972261,\n          0.02825446618602065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"week\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3571593044647669,\n        \"min\": -0.1516499673566177,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          -0.13326572359457264,\n          0.25891119246584204,\n          0.035787198071794646\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4173198850534921,\n        \"min\": -0.1516499673566177,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.8004103899375995,\n          0.11175126448087695,\n          0.2526878486011723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"estimated_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.40942079207200854,\n        \"min\": -0.4226451087816284,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.06615669489785923,\n          -0.18334899267452595,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonality_index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3667286130380635,\n        \"min\": -0.2491587821505684,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.042649844747114904,\n          -0.01914421524945657,\n          0.3672484754756403\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price_range\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47049834642053207,\n        \"min\": -0.13326572359457264,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          1.0,\n          0.08032131932811594,\n          0.06615669489785923\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"avg_discount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4693919639220586,\n        \"min\": -0.2491587821505684,\n        \"max\": 1.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.7637234952573063,\n          0.13190609678166698,\n          -0.17021175305775815\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            },
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/881c4a0d49046431/data_table.js\";\n\n      const table = window.createDataTable({\n        data: [[\"sold_units\",\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.9443610806535778,\n            'f': \"0.9443610806535778\",\n        },\n{\n            'v': 0.045855372006394196,\n            'f': \"0.045855372006394196\",\n        },\n{\n            'v': 0.21142201657479795,\n            'f': \"0.21142201657479795\",\n        },\n{\n            'v': 0.004810674814255355,\n            'f': \"0.004810674814255355\",\n        },\n{\n            'v': -0.4226451087816284,\n            'f': \"-0.4226451087816284\",\n        },\n{\n            'v': -0.11806497729761947,\n            'f': \"-0.11806497729761947\",\n        },\n{\n            'v': 0.02612078162297054,\n            'f': \"0.02612078162297054\",\n        },\n{\n            'v': 0.14034535368095938,\n            'f': \"0.14034535368095938\",\n        }],\n [\"sold_value_loc\",\n{\n            'v': 0.9443610806535778,\n            'f': \"0.9443610806535778\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.09118259745972261,\n            'f': \"0.09118259745972261\",\n        },\n{\n            'v': 0.25891119246584204,\n            'f': \"0.25891119246584204\",\n        },\n{\n            'v': 0.11175126448087695,\n            'f': \"0.11175126448087695\",\n        },\n{\n            'v': -0.18334899267452595,\n            'f': \"-0.18334899267452595\",\n        },\n{\n            'v': -0.01914421524945657,\n            'f': \"-0.01914421524945657\",\n        },\n{\n            'v': 0.08032131932811594,\n            'f': \"0.08032131932811594\",\n        },\n{\n            'v': 0.13190609678166698,\n            'f': \"0.13190609678166698\",\n        }],\n [\"is_promo\",\n{\n            'v': 0.045855372006394196,\n            'f': \"0.045855372006394196\",\n        },\n{\n            'v': 0.09118259745972261,\n            'f': \"0.09118259745972261\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.11542498611109206,\n            'f': \"-0.11542498611109206\",\n        },\n{\n            'v': 0.8071711853371548,\n            'f': \"0.8071711853371548\",\n        },\n{\n            'v': 0.02825446618602065,\n            'f': \"0.02825446618602065\",\n        },\n{\n            'v': -0.0057617809332895144,\n            'f': \"-0.0057617809332895144\",\n        },\n{\n            'v': 0.9941409090742627,\n            'f': \"0.9941409090742627\",\n        },\n{\n            'v': 0.8286082837650001,\n            'f': \"0.8286082837650001\",\n        }],\n [\"week\",\n{\n            'v': 0.21142201657479795,\n            'f': \"0.21142201657479795\",\n        },\n{\n            'v': 0.25891119246584204,\n            'f': \"0.25891119246584204\",\n        },\n{\n            'v': -0.11542498611109206,\n            'f': \"-0.11542498611109206\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': -0.1516499673566177,\n            'f': \"-0.1516499673566177\",\n        },\n{\n            'v': 0.035787198071794646,\n            'f': \"0.035787198071794646\",\n        },\n{\n            'v': 0.03486484688604691,\n            'f': \"0.03486484688604691\",\n        },\n{\n            'v': -0.13326572359457264,\n            'f': \"-0.13326572359457264\",\n        },\n{\n            'v': 0.013436941552207978,\n            'f': \"0.013436941552207978\",\n        }],\n [\"year\",\n{\n            'v': 0.004810674814255355,\n            'f': \"0.004810674814255355\",\n        },\n{\n            'v': 0.11175126448087695,\n            'f': \"0.11175126448087695\",\n        },\n{\n            'v': 0.8071711853371548,\n            'f': \"0.8071711853371548\",\n        },\n{\n            'v': -0.1516499673566177,\n            'f': \"-0.1516499673566177\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.2526878486011723,\n            'f': \"0.2526878486011723\",\n        },\n{\n            'v': 0.1421057200934621,\n            'f': \"0.1421057200934621\",\n        },\n{\n            'v': 0.8004103899375995,\n            'f': \"0.8004103899375995\",\n        },\n{\n            'v': 0.6748408281183387,\n            'f': \"0.6748408281183387\",\n        }],\n [\"estimated_price\",\n{\n            'v': -0.4226451087816284,\n            'f': \"-0.4226451087816284\",\n        },\n{\n            'v': -0.18334899267452595,\n            'f': \"-0.18334899267452595\",\n        },\n{\n            'v': 0.02825446618602065,\n            'f': \"0.02825446618602065\",\n        },\n{\n            'v': 0.035787198071794646,\n            'f': \"0.035787198071794646\",\n        },\n{\n            'v': 0.2526878486011723,\n            'f': \"0.2526878486011723\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.3672484754756403,\n            'f': \"0.3672484754756403\",\n        },\n{\n            'v': 0.06615669489785923,\n            'f': \"0.06615669489785923\",\n        },\n{\n            'v': -0.17021175305775815,\n            'f': \"-0.17021175305775815\",\n        }],\n [\"seasonality_index\",\n{\n            'v': -0.11806497729761947,\n            'f': \"-0.11806497729761947\",\n        },\n{\n            'v': -0.01914421524945657,\n            'f': \"-0.01914421524945657\",\n        },\n{\n            'v': -0.0057617809332895144,\n            'f': \"-0.0057617809332895144\",\n        },\n{\n            'v': 0.03486484688604691,\n            'f': \"0.03486484688604691\",\n        },\n{\n            'v': 0.1421057200934621,\n            'f': \"0.1421057200934621\",\n        },\n{\n            'v': 0.3672484754756403,\n            'f': \"0.3672484754756403\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.042649844747114904,\n            'f': \"0.042649844747114904\",\n        },\n{\n            'v': -0.2491587821505684,\n            'f': \"-0.2491587821505684\",\n        }],\n [\"price_range\",\n{\n            'v': 0.02612078162297054,\n            'f': \"0.02612078162297054\",\n        },\n{\n            'v': 0.08032131932811594,\n            'f': \"0.08032131932811594\",\n        },\n{\n            'v': 0.9941409090742627,\n            'f': \"0.9941409090742627\",\n        },\n{\n            'v': -0.13326572359457264,\n            'f': \"-0.13326572359457264\",\n        },\n{\n            'v': 0.8004103899375995,\n            'f': \"0.8004103899375995\",\n        },\n{\n            'v': 0.06615669489785923,\n            'f': \"0.06615669489785923\",\n        },\n{\n            'v': 0.042649844747114904,\n            'f': \"0.042649844747114904\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        },\n{\n            'v': 0.7637234952573063,\n            'f': \"0.7637234952573063\",\n        }],\n [\"avg_discount\",\n{\n            'v': 0.14034535368095938,\n            'f': \"0.14034535368095938\",\n        },\n{\n            'v': 0.13190609678166698,\n            'f': \"0.13190609678166698\",\n        },\n{\n            'v': 0.8286082837650001,\n            'f': \"0.8286082837650001\",\n        },\n{\n            'v': 0.013436941552207978,\n            'f': \"0.013436941552207978\",\n        },\n{\n            'v': 0.6748408281183387,\n            'f': \"0.6748408281183387\",\n        },\n{\n            'v': -0.17021175305775815,\n            'f': \"-0.17021175305775815\",\n        },\n{\n            'v': -0.2491587821505684,\n            'f': \"-0.2491587821505684\",\n        },\n{\n            'v': 0.7637234952573063,\n            'f': \"0.7637234952573063\",\n        },\n{\n            'v': 1.0,\n            'f': \"1.0\",\n        }]],\n        columns: [[\"string\", \"index\"], [\"number\", \"sold_units\"], [\"number\", \"sold_value_loc\"], [\"number\", \"is_promo\"], [\"number\", \"week\"], [\"number\", \"year\"], [\"number\", \"estimated_price\"], [\"number\", \"seasonality_index\"], [\"number\", \"price_range\"], [\"number\", \"avg_discount\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n\n      function appendQuickchartButton(parentElement) {\n        let quickchartButtonContainerElement = document.createElement('div');\n        quickchartButtonContainerElement.innerHTML = `\n<div id=\"df-d702b1f8-6229-4489-b4da-1408fded6bdb\">\n  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d702b1f8-6229-4489-b4da-1408fded6bdb')\"\n            title=\"Suggest charts\"\n            style=\"display:none;\">\n    \n<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n     width=\"24px\">\n    <g>\n        <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-d702b1f8-6229-4489-b4da-1408fded6bdb button');\n      quickchartButtonEl.style.display =\n        google.colab.kernel.accessAllowed ? 'block' : 'none';\n    })();\n  </script>\n</div>`;\n        parentElement.appendChild(quickchartButtonContainerElement);\n      }\n\n      appendQuickchartButton(table);\n    "
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_m.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4iRzxP-aWPc0",
        "outputId": "869ba581-da9b-4b9c-d45b-596843678a95"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 221 entries, 19 to 221\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   sold_units         221 non-null    float64\n",
            " 1   sold_value_loc     221 non-null    float64\n",
            " 2   end_date           221 non-null    dbdate \n",
            " 3   is_promo           221 non-null    int64  \n",
            " 4   week               221 non-null    Int64  \n",
            " 5   year               221 non-null    Int64  \n",
            " 6   estimated_price    221 non-null    float64\n",
            " 7   seasonality_index  221 non-null    float64\n",
            " 8   price_range        221 non-null    float64\n",
            " 9   tactic             221 non-null    object \n",
            " 10  sub_tactic         221 non-null    object \n",
            " 11  avg_discount       221 non-null    float64\n",
            "dtypes: Int64(2), dbdate(1), float64(6), int64(1), object(2)\n",
            "memory usage: 22.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sold_unit, end_date, /estimated_price, seasonality_index, avg_discount, sub_tactic"
      ],
      "metadata": {
        "id": "5Y5rsrJnWxDc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}