Financial_Analytics.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy import stats\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.decomposition import PCA\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "XCeJ1EayxURq",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "competitor_data = pd.read_csv(r\"/content/gdrive/MyDrive/Financial Analytics data.csv\")\n",
        "competitor_data.head(5)"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   S.No.            Name  Mar Cap - Crore  Sales Qtr - Crore  Unnamed: 4\n",
              "0      1  Reliance Inds.        583436.72           99810.00         NaN\n",
              "1      2             TCS        563709.84           30904.00         NaN\n",
              "2      3       HDFC Bank        482953.59           20581.27         NaN\n",
              "3      4             ITC        320985.27            9772.02         NaN\n",
              "4      5         H D F C        289497.37           16840.51         NaN"
            ],
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              "      <th>S.No.</th>\n",
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              "      <td>Reliance Inds.</td>\n",
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          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "competitor_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XDAhHSki1SwU",
        "outputId": "3f030e54-4334-474b-87c2-660628b8aa1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 488 entries, 0 to 487\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   S.No.              488 non-null    int64  \n",
            " 1   Name               488 non-null    object \n",
            " 2   Mar Cap - Crore    479 non-null    float64\n",
            " 3   Sales Qtr - Crore  365 non-null    float64\n",
            " 4   Unnamed: 4         94 non-null     float64\n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 19.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#some of missing Sales Qtr - Crore are shifted to column 'Unnamed: 4' and we want that back\n",
        "null_indexes = []\n",
        "for i in range(len(competitor_data['Mar Cap - Crore'].isna())):\n",
        "  if competitor_data['Mar Cap - Crore'].isna()[i] == True:\n",
        "    null_indexes.append(i)\n",
        "\n",
        "competitor_data.drop(null_indexes , axis = 0 , inplace = True)\n",
        "competitor_data.reset_index(drop=True, inplace=True)\n",
        "\n",
        "competitor_data.info()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5cT8dMmt1nfR",
        "outputId": "1dadff32-d3d2-467b-a419-50eec5fbee1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 479 entries, 0 to 478\n",
            "Data columns (total 5 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   S.No.              479 non-null    int64  \n",
            " 1   Name               479 non-null    object \n",
            " 2   Mar Cap - Crore    479 non-null    float64\n",
            " 3   Sales Qtr - Crore  365 non-null    float64\n",
            " 4   Unnamed: 4         94 non-null     float64\n",
            "dtypes: float64(3), int64(1), object(1)\n",
            "memory usage: 18.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_entries = []\n",
        "present_entries = []\n",
        "for j in range(len(competitor_data['Sales Qtr - Crore'].isna())):\n",
        "  if competitor_data['Sales Qtr - Crore'].isna()[j] == True:\n",
        "    missing_entries.append(j)\n",
        "\n",
        "for k in range(len(competitor_data['Unnamed: 4'].isna())):\n",
        "  if competitor_data['Unnamed: 4'].isna()[k] == False:\n",
        "    present_entries.append(k)\n",
        "\n",
        "print(missing_entries)\n",
        "print(present_entries)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkoTByLt2ERQ",
        "outputId": "7c0daf9f-a01c-4bd1-c366-e1dd9a304177"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[49, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 169, 174, 183, 187, 193, 202, 211, 212, 215, 216, 217, 218, 219, 220, 221, 224, 225, 227, 228, 229, 230, 231, 234, 236, 238, 240, 241, 243, 245, 254, 309, 327, 332, 362, 364, 365, 366, 367, 368, 369, 372, 373, 374, 375, 376, 377, 379, 380, 381, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 394, 395, 396, 397, 400, 401, 402, 410, 411, 415, 417, 420, 421, 422, 438, 443, 451]\n",
            "[60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 174, 187, 193, 202, 211, 212, 215, 216, 217, 218, 219, 220, 224, 225, 227, 228, 229, 230, 231, 234, 236, 240, 241, 243, 245, 362, 365, 366, 367, 369, 372, 374, 375, 376, 377, 379, 380, 381, 383, 384, 385, 386, 387, 388, 390, 392, 394, 395, 396, 397, 400, 401, 410, 415, 420, 421, 422]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for m in present_entries:\n",
        "  competitor_data['Sales Qtr - Crore'][m] = competitor_data['Unnamed: 4'][m]\n",
        "\n",
        "competitor_data.drop(columns = 'Unnamed: 4' , axis = 1, inplace = True)\n",
        "# competitior_data = competitor_data[competitor_data['Sales Qtr - Crore'].isna() == False]\n",
        "competitor_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w7Umlcdm3weG",
        "outputId": "2a7c8ef3-693c-45ad-f3b6-9b385eb88b1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 479 entries, 0 to 478\n",
            "Data columns (total 4 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   S.No.              479 non-null    int64  \n",
            " 1   Name               479 non-null    object \n",
            " 2   Mar Cap - Crore    479 non-null    float64\n",
            " 3   Sales Qtr - Crore  459 non-null    float64\n",
            "dtypes: float64(2), int64(1), object(1)\n",
            "memory usage: 15.1+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-61-d149132ad2b5>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  competitor_data['Sales Qtr - Crore'][m] = competitor_data['Unnamed: 4'][m]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " competitor_data = competitor_data[competitor_data['Sales Qtr - Crore'].isna() == False]\n",
        " competitor_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "6SKlqs6oBwuA",
        "outputId": "3b76e930-3d01-4a77-8de2-cfb7a83f37e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 459 entries, 0 to 478\n",
            "Data columns (total 4 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   S.No.              459 non-null    int64  \n",
            " 1   Name               459 non-null    object \n",
            " 2   Mar Cap - Crore    459 non-null    float64\n",
            " 3   Sales Qtr - Crore  459 non-null    float64\n",
            "dtypes: float64(2), int64(1), object(1)\n",
            "memory usage: 17.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking duplicate data like labels or features or client id/number\n",
        "print(competitor_data['Name'].is_unique)\n",
        "print(competitor_data.columns.is_unique)"
      ],
      "metadata": {
        "id": "3dp5dm7ZDZlX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a92d4772-8d5b-4069-ba51-09a8098e3345"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#HERE NO NEED OF FURTHER DATA CLEANING REQUIRED SO WE DOWNLAOD AS .xlsx file TO\n",
        "#FURTHER ANALYSIS AND KPI METRICS IN TABLEAU\n",
        "\n",
        "excel_file_path = \"financial_cleaned_data.xlsx\"\n",
        "competitor_data.to_excel(excel_file_path , index = False)\n",
        "\n",
        "from google.colab import files\n",
        "files.download(excel_file_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "87c-KrYzFG94",
        "outputId": "7202db21-d858-4d00-8274-bf8053cb741f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_97758714-d03e-4848-ab03-55f5a69aa3ea\", \"financial_cleaned_data.xlsx\", 21596)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "n84iqCQ5H4K7"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}