ipta_slope_analysis_method_lastedit_11_nov2024.ipynb

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      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel('/content/drive/MyDrive/research/Rainfall_1981-2022.xlsx', header=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: get all row from df_city  Where Month =1 , and devide it two part the first half and last half\n",
        "\n",
        "def convert_to_first_and_last_half(df_city):\n",
        "  dataset = []\n",
        "  # Filter rows where Month is 1\n",
        "  for i in range(1,13):\n",
        "    df_month = df_city[df_city['Month'] == i]\n",
        "\n",
        "    # Calculate the midpoint of the filtered DataFrame\n",
        "    midpoint = len(df_month) // 2\n",
        "\n",
        "    # Split the DataFrame into two parts\n",
        "    first_half = df_month.iloc[:midpoint]\n",
        "    last_half = df_month.iloc[midpoint:]\n",
        "\n",
        "    #print(\"First Half:\")\n",
        "    #print(first_half)\n",
        "    sum_first_half = first_half['sum_1_to_31'].sum()\n",
        "\n",
        "    sum_first_half = first_half['sum_1_to_31'].mean()\n",
        "\n",
        "    std_first_half = first_half['sum_1_to_31'].std()\n",
        "    #print(f\"The sum of 'sum_1_to_31' for the first half is: {sum_first_half}\")\n",
        "\n",
        "    #print(\"\\nLast Half:\")\n",
        "    #print(last_half)\n",
        "    sum_last_half = last_half['sum_1_to_31'].sum()\n",
        "    sum_last_half = last_half['sum_1_to_31'].mean()\n",
        "    std_last_half = last_half['sum_1_to_31'].std()\n",
        "    #print(f\"The sum of 'sum_1_to_31' for the last half is: {sum_last_half}\")\n",
        "    dataset.append([i, sum_first_half, sum_last_half,std_first_half,std_last_half])\n",
        "\n",
        "  df_dataset = pd.DataFrame(dataset, columns=['Month', 'Sum_First_Half', 'Sum_Last_Half','std_First_Half','std_Last_Half'])\n",
        "  #print(df_dataset)\n",
        "  return df_dataset\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "wPhXB_s_PSjE"
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      "execution_count": 52,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Function to calculate slope between two points\n",
        "def calculate_slope(x1, y1, x2, y2):\n",
        "    if x2 - x1 != 0:\n",
        "        return (y2 - y1) / (x2 - x1)\n",
        "    else:\n",
        "        return 0\n",
        "\n",
        "def calculate_length(x1, y1, x2, y2):\n",
        "    return ((x2 - x1)**2 + (y2 - y1)**2)**0.5\n",
        "\n",
        "\n",
        "\n",
        "# Function to calculate slopes and plot IPTA\n",
        "def plot_ipta(Month,data_first, data_last):\n",
        "    #data_first_1 will be in first collam Name Sum_First_Half and data_last one will be in second collam and merged\n",
        "    df = pd.concat([Month,data_first, data_last], axis=1)\n",
        "    df.columns = ['Month','Sum_First_Half', 'Sum_Last_Half']\n",
        "\n",
        "    # Calculate slopes and lengths\n",
        "    slopes = []\n",
        "    lengths= []\n",
        "    trends = []\n",
        "\n",
        "\n",
        "    for i in range(len(df)):\n",
        "\n",
        "\n",
        "\n",
        "        if i ==  len(df) -1 :\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[0], df['Sum_Last_Half'].iloc[0]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "        else:\n",
        "          x1, y1 = df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i]\n",
        "          x2, y2 = df['Sum_First_Half'].iloc[i + 1], df['Sum_Last_Half'].iloc[i + 1]\n",
        "          slope = calculate_slope(x1, y1, x2, y2)\n",
        "          length  = calculate_length(x1, y1, x2, y2)\n",
        "          slopes.append(slope)\n",
        "          lengths.append(length)\n",
        "\n",
        "        if slope< 1:\n",
        "          trends.append(1)\n",
        "        elif slope ==0:\n",
        "          trends.append(0)\n",
        "        else:\n",
        "          trends.append(-1)\n",
        "\n",
        "    df['Trend'] = trends\n",
        "\n",
        "    # Add None for the first slope (no previous point)\n",
        "    #slopes = [None] + slopes\n",
        "    slopes = slopes\n",
        "\n",
        "    # Add slopes to the DataFrame\n",
        "    df['Slopes'] = slopes\n",
        "    df['Length'] = lengths\n",
        "\n",
        "\n",
        "\n",
        "    df_dataset = pd.DataFrame(df, columns=['Month', 'Trend'])\n",
        "\n",
        "    # Transpose the DataFrame\n",
        "    df_transposed = df_dataset.set_index('Month').transpose()\n",
        "\n",
        "    #df_transposed.head()\n",
        "    #print(df_transposed)\n",
        "    return df_dataset\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "    # Plot IPTA\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    plt.plot(df['Sum_First_Half'], df['Sum_Last_Half'], marker='o', linestyle='--', color='b', label='Rainfall Data')\n",
        "\n",
        "    # Plot month names beside each point\n",
        "    for i in range(len(df)):\n",
        "        plt.text(df['Sum_First_Half'].iloc[i], df['Sum_Last_Half'].iloc[i],\n",
        "                 f\"{df['Month'].iloc[i]}\", fontsize=9, ha='right')\n",
        "\n",
        "    # Plot slopes\n",
        "    #for i in range(len(df) - 1):\n",
        "    #    plt.text((df['Sum_First_Half'].iloc[i] + df['Sum_First_Half'].iloc[i + 1]) / 2,\n",
        "    #              (df['Sum_Last_Half'].iloc[i] + df['Sum_Last_Half'].iloc[i + 1]) / 2,\n",
        "     #             f\"Slope: {round(slopes[i + 1], 2)}\", fontsize=9, color='green')\n",
        "\n",
        "    # Plot 45-degree reference line\n",
        "\n",
        "    plt.plot([df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             [df['Sum_First_Half'].min(), df['Sum_First_Half'].max()],\n",
        "             'r--', label='45-degree line')\n",
        "\n",
        "    plt.title('IPTA Polygon Plot')\n",
        "    plt.xlabel('First Half  ')\n",
        "    plt.ylabel('Last Half  ')\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "    \"\"\"\n",
        "\n",
        "\n",
        "\n"
      ],
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              "'\\n    # Plot IPTA\\n    plt.figure(figsize=(10, 6))\\n    plt.plot(df[\\'Sum_First_Half\\'], df[\\'Sum_Last_Half\\'], marker=\\'o\\', linestyle=\\'--\\', color=\\'b\\', label=\\'Rainfall Data\\')\\n\\n    # Plot month names beside each point\\n    for i in range(len(df)):\\n        plt.text(df[\\'Sum_First_Half\\'].iloc[i], df[\\'Sum_Last_Half\\'].iloc[i],\\n                 f\"{df[\\'Month\\'].iloc[i]}\", fontsize=9, ha=\\'right\\')\\n\\n    # Plot slopes\\n    #for i in range(len(df) - 1):\\n    #    plt.text((df[\\'Sum_First_Half\\'].iloc[i] + df[\\'Sum_First_Half\\'].iloc[i + 1]) / 2,\\n    #              (df[\\'Sum_Last_Half\\'].iloc[i] + df[\\'Sum_Last_Half\\'].iloc[i + 1]) / 2,\\n     #             f\"Slope: {round(slopes[i + 1], 2)}\", fontsize=9, color=\\'green\\')\\n\\n    # Plot 45-degree reference line\\n\\n    plt.plot([df[\\'Sum_First_Half\\'].min(), df[\\'Sum_First_Half\\'].max()],\\n             [df[\\'Sum_First_Half\\'].min(), df[\\'Sum_First_Half\\'].max()],\\n             \\'r--\\', label=\\'45-degree line\\')\\n\\n    plt.title(\\'IPTA Polygon Plot\\')\\n    plt.xlabel(\\'First Half  \\')\\n    plt.ylabel(\\'Last Half  \\')\\n    plt.legend()\\n    plt.grid(True)\\n    plt.show()\\n    '"
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      "source": [
        "**Making Function CTWISE**"
      ],
      "metadata": {
        "id": "_CsrWN21SosY"
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      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
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      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "df = pd.read_excel('/content/drive/MyDrive/research/Rainfall_1981-2022.xlsx', header=0,na_values=[''])"
      ],
      "metadata": {
        "id": "RfzseepUSrn6"
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      "outputs": []
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        "df.head()"
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              "          Stati  Year Month   1    2  3  4   5  6  7  ...  22 23  24  25  26  \\\n",
              "0  Dhaka         1981     1   5  NaN  4  5   5  0  0  ...   0  0   0   0   0   \n",
              "1  Dhaka         1981     2   9    2  0  0   0  0  0  ...   0  0   0   0   0   \n",
              "2  Dhaka         1981     3   0    0  0  0   0  0  0  ...  10  0  10   0  13   \n",
              "3  Dhaka         1981     4  41    2  0  0   4  0  0  ...   0  2   0   0   0   \n",
              "4  Dhaka         1981     5   0    0  0  0  20  8  0  ...  20  0   0  13   1   \n",
              "\n",
              "  27 28   29   30   31  \n",
              "0  0  0    0    0    6  \n",
              "1  0  0  NaN  NaN  NaN  \n",
              "2  0  8    0    2    0  \n",
              "3  0  0    0    3  NaN  \n",
              "4  1  1   43   45    0  \n",
              "\n",
              "[5 rows x 34 columns]"
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              "      <th>Stati</th>\n",
              "      <th>Year</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
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              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dhaka</td>\n",
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              "      <td>41</td>\n",
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              "        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",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ffab29b6-5a81-485c-84fd-b3c77cdc3934\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ffab29b6-5a81-485c-84fd-b3c77cdc3934')\"\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-ffab29b6-5a81-485c-84fd-b3c77cdc3934 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Count occurrences of \"Trace\" or \"Xx\" in the DataFrame\n",
        "#trace_count = df[df.applymap(lambda x: isinstance(x, str) and x.lower() == 'trace').any(axis=1)].shape[0]\n",
        "#xx_count = df[df.applymap(lambda x: isinstance(x, str) and x.lower() == 'xx').any(axis=1)].shape[0]\n",
        "#xx_count = df[df.applymap(lambda x: isinstance(x, str) and x.lower() == '***').any(axis=1)].shape[0]\n",
        "#print(f\"Number of rows with 'Trace': {trace_count}\")\n",
        "#print(f\"Number of rows with 'Xx': {xx_count}\")"
      ],
      "metadata": {
        "id": "s3iCnCfRjv6q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.replace(to_replace=['Trace', 'Xx','***'], value=0, inplace=True)"
      ],
      "metadata": {
        "id": "rky71EFmj1MY"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df.loc[:, 1:31] = df.loc[:, 1:31].apply(pd.to_numeric, errors='coerce')\n",
        "for col in range(1, 32):  # loop through column names 1 to 31\n",
        "    df[col] = pd.to_numeric(df[col], errors='coerce')  # convert to numeric and coerce errors\n",
        "\n",
        "###new\n",
        "df.loc[:, 1:31] = df.loc[:, 1:31].interpolate(method='linear', axis=1)\n",
        "\n",
        "df['sum_1_to_31'] = df.loc[:, 1:31].sum(axis=1)\n",
        "df.head()\n"
      ],
      "metadata": {
        "id": "nqFNfaWBTUwM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "5042a41b-7726-4d89-ab83-f7b728a65478"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Stati  Year Month     1    2    3    4     5    6    7  ...   23  \\\n",
              "0  Dhaka         1981     1   5.0  4.5  4.0  5.0   5.0  0.0  0.0  ...  0.0   \n",
              "1  Dhaka         1981     2   9.0  2.0  0.0  0.0   0.0  0.0  0.0  ...  0.0   \n",
              "2  Dhaka         1981     3   0.0  0.0  0.0  0.0   0.0  0.0  0.0  ...  0.0   \n",
              "3  Dhaka         1981     4  41.0  2.0  0.0  0.0   4.0  0.0  0.0  ...  2.0   \n",
              "4  Dhaka         1981     5   0.0  0.0  0.0  0.0  20.0  8.0  0.0  ...  0.0   \n",
              "\n",
              "     24    25    26   27   28    29    30   31  sum_1_to_31  \n",
              "0   0.0   0.0   0.0  0.0  0.0   0.0   0.0  6.0         33.5  \n",
              "1   0.0   0.0   0.0  0.0  0.0   0.0   0.0  0.0         42.0  \n",
              "2  10.0   0.0  13.0  0.0  8.0   0.0   2.0  0.0        109.0  \n",
              "3   0.0   0.0   0.0  0.0  0.0   0.0   3.0  3.0        277.0  \n",
              "4   0.0  13.0   1.0  1.0  1.0  43.0  45.0  0.0        272.0  \n",
              "\n",
              "[5 rows x 35 columns]"
            ],
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              "\n",
              "  <div id=\"df-b68f0a65-c2d5-4843-9bb9-38a7864acd77\" 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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\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>Stati</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>...</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>sum_1_to_31</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
              "      <td>1</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.5</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>33.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
              "      <td>2</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>42.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>109.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
              "      <td>4</td>\n",
              "      <td>41.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Dhaka</td>\n",
              "      <td>1981</td>\n",
              "      <td>5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>43.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>272.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 35 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b68f0a65-c2d5-4843-9bb9-38a7864acd77')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [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",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b68f0a65-c2d5-4843-9bb9-38a7864acd77 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-b68f0a65-c2d5-4843-9bb9-38a7864acd77');\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",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            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",
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              "    </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",
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              "    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",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    border-bottom-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",
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              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    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-0027cd6a-a5b4-44e8-91b6-8324117085c7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[['Stati','Year', 'Month', 'sum_1_to_31']]\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "w4udUwgzlXV8",
        "outputId": "96b3b972-ee62-431d-d8d6-379ac79312ca"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Stati  Year Month  sum_1_to_31\n",
              "0  Dhaka         1981     1         33.5\n",
              "1  Dhaka         1981     2         42.0\n",
              "2  Dhaka         1981     3        109.0\n",
              "3  Dhaka         1981     4        277.0\n",
              "4  Dhaka         1981     5        272.0"
            ],
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              "  }\n",
              "\n",
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              "    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",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        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",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 17341,\n  \"fields\": [\n    {\n      \"column\": \"Stati\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 37,\n        \"samples\": [\n          \"Khulna      \",\n          \"Rangpur     \",\n          \"Mymensingh  \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 43,\n        \"samples\": [\n          2018,\n          2005,\n          2006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          12,\n          10,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sum_1_to_31\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 250.78197066820184,\n        \"min\": 0.0,\n        \"max\": 3024.0,\n        \"num_unique_values\": 1186,\n        \"samples\": [\n          156.0,\n          1288.0,\n          1472.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_city = (df['Stati'].unique())\n",
        "print(unique_city)"
      ],
      "metadata": {
        "id": "ZrecJ4NTS2HK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "outputId": "038254fe-a1c6-445c-8b6b-522238292586"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Dhaka       ' nan\n",
            " '          Daily  total  Rainfall  data  in  Millimeter  ' 'Stati'\n",
            " 'Tangail     ' 'Mymensingh  ' 'Faridpur' 'Madaripur   ' 'Srimangal   '\n",
            " 'Sylhet      ' 'Bogra       ' 'Dinajpur    ' 'Ishurdi     '\n",
            " 'Rajshahi    ' 'Rangpur     ' 'sydpur      ' 'chuadanga   '\n",
            " 'Jessore     ' 'Khulna      ' 'Mongla      ' 'Satkhira    '\n",
            " 'Barisal     ' 'Bhola       ' 'Khepupara   ' 'Patuakhali  '\n",
            " 'Chandpur    ' 'Teknaf      ' 'Chittagong  ' 'Comilla     '\n",
            " \"Cox'sbazar  \" 'Feni        ' 'Hatiya      ' 'Kutubdia    '\n",
            " 'M.court     ' 'Rangamati   ' 'Sandwip     ' 'Ambagan(Ctg)'\n",
            " 'Sitakunda   ']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p = 0\n",
        "for city in unique_city:\n",
        "    #print index and city name\n",
        "    print(city,p)\n",
        "    p= p+1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fNu97oBpko7T",
        "outputId": "abde8e2f-51b1-485d-f2c7-70cf0eb9fab2"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dhaka        0\n",
            "nan 1\n",
            "          Daily  total  Rainfall  data  in  Millimeter   2\n",
            "Stati 3\n",
            "Tangail      4\n",
            "Mymensingh   5\n",
            "Faridpur 6\n",
            "Madaripur    7\n",
            "Srimangal    8\n",
            "Sylhet       9\n",
            "Bogra        10\n",
            "Dinajpur     11\n",
            "Ishurdi      12\n",
            "Rajshahi     13\n",
            "Rangpur      14\n",
            "sydpur       15\n",
            "chuadanga    16\n",
            "Jessore      17\n",
            "Khulna       18\n",
            "Mongla       19\n",
            "Satkhira     20\n",
            "Barisal      21\n",
            "Bhola        22\n",
            "Khepupara    23\n",
            "Patuakhali   24\n",
            "Chandpur     25\n",
            "Teknaf       26\n",
            "Chittagong   27\n",
            "Comilla      28\n",
            "Cox'sbazar   29\n",
            "Feni         30\n",
            "Hatiya       31\n",
            "Kutubdia     32\n",
            "M.court      33\n",
            "Rangamati    34\n",
            "Sandwip      35\n",
            "Ambagan(Ctg) 36\n",
            "Sitakunda    37\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Corrected indices for each item in the requested list:\n",
        "preferred_index= [\n",
        "    16,  # Chuadanga\n",
        "    17,  # Jessore\n",
        "    18,  # Khulna\n",
        "    20,  # Satkhira\n",
        "    27,  # Chittagong\n",
        "    36,  # Ambagan(Ctg)\n",
        "    29,  # Cox'sbazar\n",
        "    30,  # Feni\n",
        "    34,  # Rangamati\n",
        "    37,  # Sitakunda\n",
        "    26,  # Teknaf\n",
        "    10,   # Bogra\n",
        "    11,  # Dinajpur\n",
        "    12,  # Ishurdi\n",
        "    13,  # Rajshahi\n",
        "    14,  # Rangpur\n",
        "    15,  # Syedpur\n",
        "    8,   # Srimangal\n",
        "    9,   # Sylhet\n",
        "    21,  # Barisal\n",
        "    22,  # Bhola\n",
        "    31,  # Hatiya\n",
        "    23,  # Khepupara\n",
        "    32,  # Kutubdia\n",
        "    33,  # M.court\n",
        "    19,  # Mongla\n",
        "    24,  # Patuakhali\n",
        "    35,  # Sandwip\n",
        "    25,  # Chandpur\n",
        "    28,  # Comilla\n",
        "    0,   # Dhaka\n",
        "    6,   # Faridpur\n",
        "    7,   # Madaripur\n",
        "    5,   # Mymensingh\n",
        "    4    # Tangail\n",
        "]\n",
        "\n"
      ],
      "metadata": {
        "id": "GfoRSH9JSUNj"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "final_results = pd.DataFrame()\n",
        "\n",
        "#for city in unique_city:\n",
        "for ind in preferred_index:\n",
        "\n",
        "    city  = unique_city[ind]\n",
        "    if city in ['          Daily  total  Rainfall  data  in  Millimeter  ', 'Stati', np.nan]:\n",
        "        continue\n",
        "    print (i,city)\n",
        "\n",
        "    df_city = df[df['Stati'] == city]\n",
        "    df_city = df_city[['Year', 'Month', 'sum_1_to_31']]\n",
        "\n",
        "\n",
        "\n",
        "    df_dataset = convert_to_first_and_last_half(df_city)\n",
        "\n",
        "    n_df_dataset = plot_ipta(df_dataset['Month'],df_dataset['Sum_First_Half'],df_dataset['Sum_Last_Half'])\n",
        "    df_trends = n_df_dataset[['Month', 'Trend']]\n",
        "    df_trends.set_index('Month', inplace=True)\n",
        "    df_trends.columns = [city]  # Set city name as column name\n",
        "\n",
        "    # Append to the final results DataFrame\n",
        "    if final_results.empty:\n",
        "        final_results = df_trends\n",
        "    else:\n",
        "        final_results = pd.concat([final_results, df_trends], axis=1)\n",
        "\n",
        "\n",
        "\n",
        "    #print( \"Plot Rainfall std\")\n",
        "    #plot_ipta(df_dataset['Month'],df_dataset['std_First_Half'],df_dataset['std_Last_Half'])\n",
        "\n",
        "    print(\"========================\")\n",
        "    i= i+1\n",
        "#print(final_results)\n",
        "# Transpose the DataFrame\n",
        "df_transposed = final_results.transpose()\n",
        "\n",
        "#df_transposed.head()\n",
        "print(df_transposed)\n",
        "\n",
        "file_path = \"/content/drive/MyDrive/research/IPTA_11Nov.csv\" ;\n",
        "# now wanna export df_transport in csv\n",
        "df_transposed.reset_index(inplace=True)\n",
        "df_transposed.to_csv(file_path, index=False)\n",
        "\n",
        "import os\n",
        "if os.path.exists(file_path):\n",
        "    print(f\"File successfully saved at: {file_path}\")\n",
        "else:\n",
        "    print(\"Error: File not saved.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vpvwdp44S9h_",
        "outputId": "3839f5d8-39fe-4f72-dcf5-7a7bf5efed18"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 chuadanga   \n",
            "========================\n",
            "1 Jessore     \n",
            "========================\n",
            "2 Khulna      \n",
            "========================\n",
            "3 Satkhira    \n",
            "========================\n",
            "4 Chittagong  \n",
            "========================\n",
            "5 Ambagan(Ctg)\n",
            "========================\n",
            "6 Cox'sbazar  \n",
            "========================\n",
            "7 Feni        \n",
            "========================\n",
            "8 Rangamati   \n",
            "========================\n",
            "9 Sitakunda   \n",
            "========================\n",
            "10 Teknaf      \n",
            "========================\n",
            "11 Bogra       \n",
            "========================\n",
            "12 Dinajpur    \n",
            "========================\n",
            "13 Ishurdi     \n",
            "========================\n",
            "14 Rajshahi    \n",
            "========================\n",
            "15 Rangpur     \n",
            "========================\n",
            "16 sydpur      \n",
            "========================\n",
            "17 Srimangal   \n",
            "========================\n",
            "18 Sylhet      \n",
            "========================\n",
            "19 Barisal     \n",
            "========================\n",
            "20 Bhola       \n",
            "========================\n",
            "21 Hatiya      \n",
            "========================\n",
            "22 Khepupara   \n",
            "========================\n",
            "23 Kutubdia    \n",
            "========================\n",
            "24 M.court     \n",
            "========================\n",
            "25 Mongla      \n",
            "========================\n",
            "26 Patuakhali  \n",
            "========================\n",
            "27 Sandwip     \n",
            "========================\n",
            "28 Chandpur    \n",
            "========================\n",
            "29 Comilla     \n",
            "========================\n",
            "30 Dhaka       \n",
            "========================\n",
            "31 Faridpur\n",
            "========================\n",
            "32 Madaripur   \n",
            "========================\n",
            "33 Mymensingh  \n",
            "========================\n",
            "34 Tangail     \n",
            "========================\n",
            "Month         1   2   3   4   5   6   7   8   9   10  11  12\n",
            "chuadanga     -1   1  -1  -1   1  -1   1   1   1   1   1   1\n",
            "Jessore       -1   1  -1  -1   1   1  -1   1   1  -1   1   1\n",
            "Khulna         1   1   1  -1   1   1   1   1  -1  -1   1   1\n",
            "Satkhira      -1   1   1  -1   1  -1  -1  -1   1  -1   1   1\n",
            "Chittagong     1   1   1  -1  -1   1   1   1   1  -1   1   1\n",
            "Ambagan(Ctg)   1  -1  -1   1  -1   1  -1   1  -1  -1   1   1\n",
            "Cox'sbazar     1  -1   1  -1   1  -1   1   1   1  -1   1   1\n",
            "Feni           1   1   1  -1  -1   1  -1   1   1  -1   1  -1\n",
            "Rangamati      1   1  -1   1  -1   1  -1   1  -1  -1   1   1\n",
            "Sitakunda      1   1  -1  -1  -1   1  -1   1  -1  -1   1   1\n",
            "Teknaf         1   1   1  -1   1   1  -1   1  -1  -1   1   1\n",
            "Bogra         -1  -1  -1   1  -1   1   1   1   1  -1   1   1\n",
            "Dinajpur       1  -1  -1   1  -1   1  -1   1   1  -1   1  -1\n",
            "Ishurdi        1  -1   1   1  -1   1   1   1   1  -1   1   1\n",
            "Rajshahi       1  -1   1  -1   1   1   1   1   1  -1   1   1\n",
            "Rangpur       -1  -1  -1   1   1   1   1   1   1  -1   1  -1\n",
            "sydpur         1   1  -1   1   1   1   1   1   1   1   1  -1\n",
            "Srimangal      1   1  -1  -1   1  -1   1  -1   1  -1   1  -1\n",
            "Sylhet         1   1  -1   1  -1   1   1  -1   1  -1   1  -1\n",
            "Barisal        1   1   1  -1   1   1  -1   1  -1  -1   1   1\n",
            "Bhola          1   1   1  -1   1   1  -1   1   1  -1   1   1\n",
            "Hatiya         1   1   1  -1  -1   1  -1   1   1  -1   1   1\n",
            "Khepupara      1  -1   1  -1  -1  -1  -1   1   1  -1   1   1\n",
            "Kutubdia       1   1  -1  -1   1  -1  -1   1  -1  -1   1  -1\n",
            "M.court        1   1   1  -1  -1   1  -1   1   1  -1   1   1\n",
            "Mongla         1   1  -1   1   1   1  -1   1   1  -1   1   1\n",
            "Patuakhali     1  -1   1  -1   1   1  -1   1   1  -1   1   1\n",
            "Sandwip        1   1   1  -1  -1  -1  -1   1  -1  -1   1   1\n",
            "Chandpur      -1   1   1  -1  -1   1  -1   1   1  -1   1  -1\n",
            "Comilla        1   1   1   1  -1   1  -1   1  -1  -1   1  -1\n",
            "Dhaka         -1   1  -1   1  -1  -1  -1   1   1  -1   1  -1\n",
            "Faridpur      -1   1   1   1  -1   1   1  -1   1   1   1  -1\n",
            "Madaripur     -1   1   1   1   1   1  -1   1   1  -1   1   1\n",
            "Mymensingh     1   1  -1   1  -1   1   1   1   1   1   1   1\n",
            "Tangail        1   1   1   1  -1   1   1   1   1  -1   1   1\n",
            "File successfully saved at: /content/drive/MyDrive/research/IPTA_11Nov.csv\n"
          ]
        }
      ]
    }
  ]
}