Page-Performance-Slope.txt

page-performance-slope-txt.ipynb

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        "# Step 1: Import necessary libraries\n",
        "import pandas as pd\n",
        "from google.colab import files"
      ]
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        "# Step 2: Upload the Excel file\n",
        "uploaded = files.upload()"
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              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
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          "text": [
            "Saving TLF-Input-Slope-Sample-20231222.xlsx to TLF-Input-Slope-Sample-20231222 (1).xlsx\n"
          ]
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      "source": [
        "# Step 3: Read the uploaded Excel file into a DataFrame\n",
        "file_name = list(uploaded.keys())[0]\n",
        "df = pd.read_excel(file_name)"
      ],
      "metadata": {
        "id": "gSiP2wDTnvAD"
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      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Step 4: Extract unique URLs from Column H\n",
        "unique_urls = df['URL'].unique()\n",
        "\n",
        "# Step 5: Create a new DataFrame for the desired output\n",
        "output_df = pd.DataFrame({'URL': unique_urls})"
      ],
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      "source": [
        "# Step 6: Iterate over unique months and create columns with counts\n",
        "for month in df['Month of year'].unique():\n",
        "    month_data = df[df['Month of year'] == month]\n",
        "    month_count = month_data[month_data['Position'] <= 10].groupby('URL').size()\n",
        "\n",
        "    # Convert numpy datetime64 to Python datetime object\n",
        "    month = pd.Timestamp(month).to_pydatetime()\n",
        "\n",
        "    output_df[month.strftime('%b-%y')] = output_df['URL'].map(month_count).fillna(0)\n"
      ],
      "metadata": {
        "id": "otJ52gC_oNUs"
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Create a column for \"Performance Slope\"\n",
        "monthly_columns = output_df.columns[1:] # Exclude 'URL' and the last column\n",
        "output_df['Performance Slope'] = output_df[monthly_columns].apply(lambda row: row.diff().mean(), axis=1)"
      ],
      "metadata": {
        "id": "oow9jx-moNvo"
      },
      "execution_count": null,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "# Step 8: Create a column for \"% Difference\"\n",
        "output_df['% Difference'] = (output_df.iloc[:, -2] - output_df.iloc[:, 1]) / output_df.iloc[:, 1] * 100\n",
        "\n",
        "# Step 9: Export the final DataFrame to Excel\n",
        "output_file_path = \"Output File.xlsx\"\n",
        "output_df.to_excel(output_file_path, index=False)\n",
        "\n",
        "# Step 10: Download the output file\n",
        "files.download(output_file_path)"
      ],
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