diff --git a/AAK_Tele_Science_data_engineer_challenge (1).ipynb b/AAK_Tele_Science_data_engineer_challenge (1).ipynb
new file mode 100644
index 0000000..dd94b96
--- /dev/null
+++ b/AAK_Tele_Science_data_engineer_challenge (1).ipynb	
@@ -0,0 +1,1318 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "id": "6bGpgnzGzUoS"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import requests"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df_data=pd.read_csv(\"/content/API_TUN_DS2_en_csv_v2_1037756.csv\",skiprows=4)\n",
+        "df_country=pd.read_csv(\"/content/Metadata_Country_API_TUN_DS2_en_csv_v2_1037756.csv\")\n",
+        "df_indicators=pd.read_csv(\"/content/Metadata_Indicator_API_TUN_DS2_en_csv_v2_1037756.csv\",)\n",
+        "\n",
+        "#df_data\n",
+        "indicator_code=df_data[\"Indicator Code\"].unique().tolist()\n",
+        "#indicator_code\n"
+      ],
+      "metadata": {
+        "id": "AoslGqA7LRgy"
+      },
+      "execution_count": 128,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "# Web Scraping\n",
+        "country_code='TN'\n",
+        "start_year=1960\n",
+        "end_year=2024\n",
+        "all_data=[]\n",
+        "for ind in indicator_code:\n",
+        "  url=(f\"https://api.worldbank.org/country/{country_code}/indicator/{ind}\"\n",
+        "  f\"?date={start_year}:{end_year}&format=json&per_page=1000\")\n",
+        "response=requests.get(url)\n",
+        "if response.status_code==200 :\n",
+        "  data= response.json()\n",
+        "  if len(data) >1 :\n",
+        "    for i in data[1]:\n",
+        "      all_data.append({\n",
+        "          \"country_code\":i.get(\"countryiso3code\"),\n",
+        "          \"indicator_code\":i.get(\"indicator\").get(\"id\"),\n",
+        "          \"indicator_name\":i.get(\"indicator\").get(\"value\"),\n",
+        "          \"year\":i.get(\"date\"),\n",
+        "          \"value\":i.get(\"value\")})\n",
+        "\n",
+        "df_scraped=pd.DataFrame(all_data)\n",
+        "df_scraped"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "rrhK1MNBA8gF",
+        "outputId": "4d148605-2dbb-4ed3-e91b-d9272ea0ec50"
+      },
+      "execution_count": 140,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   country_code     indicator_code  \\\n",
+              "0           TUN  EN.ATM.PM25.MC.ZS   \n",
+              "1           TUN  EN.ATM.PM25.MC.ZS   \n",
+              "2           TUN  EN.ATM.PM25.MC.ZS   \n",
+              "3           TUN  EN.ATM.PM25.MC.ZS   \n",
+              "4           TUN  EN.ATM.PM25.MC.ZS   \n",
+              "..          ...                ...   \n",
+              "60          TUN  EN.ATM.PM25.MC.ZS   \n",
+              "61          TUN  EN.ATM.PM25.MC.ZS   \n",
+              "62          TUN  EN.ATM.PM25.MC.ZS   \n",
+              "63          TUN  EN.ATM.PM25.MC.ZS   \n",
+              "64          TUN  EN.ATM.PM25.MC.ZS   \n",
+              "\n",
+              "                                       indicator_name  year  value  \n",
+              "0   PM2.5 air pollution, population exposed to lev...  2024    NaN  \n",
+              "1   PM2.5 air pollution, population exposed to lev...  2023    NaN  \n",
+              "2   PM2.5 air pollution, population exposed to lev...  2022    NaN  \n",
+              "3   PM2.5 air pollution, population exposed to lev...  2021    NaN  \n",
+              "4   PM2.5 air pollution, population exposed to lev...  2020    NaN  \n",
+              "..                                                ...   ...    ...  \n",
+              "60  PM2.5 air pollution, population exposed to lev...  1964    NaN  \n",
+              "61  PM2.5 air pollution, population exposed to lev...  1963    NaN  \n",
+              "62  PM2.5 air pollution, population exposed to lev...  1962    NaN  \n",
+              "63  PM2.5 air pollution, population exposed to lev...  1961    NaN  \n",
+              "64  PM2.5 air pollution, population exposed to lev...  1960    NaN  \n",
+              "\n",
+              "[65 rows x 5 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b42123f3-4ebb-40ce-8d13-ca8ec1cf2ac9\" 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>country_code</th>\n",
+              "      <th>indicator_code</th>\n",
+              "      <th>indicator_name</th>\n",
+              "      <th>year</th>\n",
+              "      <th>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>2023</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>2022</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>2021</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>2020</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>60</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>1964</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>1963</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>62</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>1962</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>63</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>1961</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>64</th>\n",
+              "      <td>TUN</td>\n",
+              "      <td>EN.ATM.PM25.MC.ZS</td>\n",
+              "      <td>PM2.5 air pollution, population exposed to lev...</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>65 rows × 5 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-b42123f3-4ebb-40ce-8d13-ca8ec1cf2ac9')\"\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-b42123f3-4ebb-40ce-8d13-ca8ec1cf2ac9 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-b42123f3-4ebb-40ce-8d13-ca8ec1cf2ac9');\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",
+              "    <div id=\"df-d0dfeed9-4d4b-40fb-9059-f3fc5c1f228f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d0dfeed9-4d4b-40fb-9059-f3fc5c1f228f')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\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",
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+              "      --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",
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+              "    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",
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+              "    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-left-color: var(--fill-color);\n",
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+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
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+              "      border-right-color: var(--fill-color);\n",
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+              "    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-d0dfeed9-4d4b-40fb-9059-f3fc5c1f228f button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_40b8d7a2-9f8f-4f32-9bcf-8492d19f022c\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
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+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate: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",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_scraped')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_scraped');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_scraped",
+              "summary": "{\n  \"name\": \"df_scraped\",\n  \"rows\": 65,\n  \"fields\": [\n    {\n      \"column\": \"country_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"TUN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indicator_code\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"EN.ATM.PM25.MC.ZS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"indicator_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (% of total)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 65,\n        \"samples\": [\n          \"1971\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 100.0,\n        \"max\": 100.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          100.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 140
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# ETL\n",
+        "df_data.drop(df_data.tail(1).index,inplace=True)\n",
+        "years=[col for col in df_data.columns if col.isdigit()]\n",
+        "id_vars=[\"Country Name\",\"Country Code\",\"Indicator Name\",\"Indicator Code\"]\n",
+        "df_data_long=pd.melt(df_data,id_vars=id_vars,value_vars=years,var_name=\"year\",value_name=\"value\")\n",
+        "df_data_long[\"year\"]=pd.to_numeric(df_data_long[\"year\"],errors=\"coerce\")\n",
+        "df_data_long[\"value\"]=pd.to_numeric(df_data_long[\"value\"],errors=\"coerce\")\n",
+        "\n",
+        "df_scraped[\"year\"]=pd.to_numeric(df_scraped[\"year\"],errors=\"coerce\")\n",
+        "df_scraped[\"value\"]=pd.to_numeric(df_scraped[\"value\"],errors=\"coerce\")\n",
+        "df_merged=pd.merge(df_data_long,df_country,on =[\"Country Code\"],how='left')\n",
+        "df_indicators.rename(columns={'INDICATOR_CODE':'Indicator_Code'},inplace=True)\n",
+        "\n",
+        "df_data_long.rename(columns={'Indicator Code':'Indicator_Code'},inplace=True)\n",
+        "df_merged=pd.merge(df_data_long,df_indicators, on=[\"Indicator_Code\"],how='left')\n",
+        "\n",
+        "df_merged.rename(columns={'Country Code':'country_code'},inplace=True)\n",
+        "df_merged.rename(columns={'Indicator_Code':'indicator_code'},inplace=True)\n",
+        "df_data_long.rename(columns={'Year':'year'},inplace=True)\n",
+        "\n",
+        "df_data_all=pd.merge(df_merged,df_scraped , on =[\"country_code\",\"indicator_code\",\"year\"],how ='left')\n",
+        "df_data_all"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 981
+        },
+        "id": "t5vIqBZpPi_7",
+        "outputId": "67a74872-368a-4bf1-b84d-b98a4188e1b3"
+      },
+      "execution_count": 165,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      Country Name country_code  \\\n",
+              "0          Tunisia          TUN   \n",
+              "1          Tunisia          TUN   \n",
+              "2          Tunisia          TUN   \n",
+              "3          Tunisia          TUN   \n",
+              "4          Tunisia          TUN   \n",
+              "...            ...          ...   \n",
+              "97105      Tunisia          TUN   \n",
+              "97106      Tunisia          TUN   \n",
+              "97107      Tunisia          TUN   \n",
+              "97108      Tunisia          TUN   \n",
+              "97109      Tunisia          TUN   \n",
+              "\n",
+              "                                          Indicator Name     indicator_code  \\\n",
+              "0         Intentional homicides, male (per 100,000 male)  VC.IHR.PSRC.MA.P5   \n",
+              "1               Battle-related deaths (number of people)        VC.BTL.DETH   \n",
+              "2              Voice and Accountability: Percentile Rank         VA.PER.RNK   \n",
+              "3      Transport services (% of commercial service ex...  TX.VAL.TRAN.ZS.WT   \n",
+              "4      Computer, communications and other services (%...  TX.VAL.OTHR.ZS.WT   \n",
+              "...                                                  ...                ...   \n",
+              "97105         Claims on central government, etc. (% GDP)  FS.AST.CGOV.GD.ZS   \n",
+              "97106                          Lending interest rate (%)        FR.INR.LEND   \n",
+              "97107                  Consumer price index (2010 = 100)        FP.CPI.TOTL   \n",
+              "97108                          Broad money (current LCU)     FM.LBL.BMNY.CN   \n",
+              "97109                  Net domestic credit (current LCU)     FM.AST.DOMS.CN   \n",
+              "\n",
+              "       year       value_x                                        SOURCE_NOTE  \\\n",
+              "0      1960           NaN  Intentional homicides, male are estimates of u...   \n",
+              "1      1960           NaN  Battle-related deaths are deaths in battle-rel...   \n",
+              "2      1960           NaN  The Worldwide Governance Indicators (WGI) are ...   \n",
+              "3      1960           NaN  Transport is the process of carriage of people...   \n",
+              "4      1960           NaN  Computer, communications and other services in...   \n",
+              "...     ...           ...                                                ...   \n",
+              "97105  2024  2.022259e+01  Claims on central government include loans to ...   \n",
+              "97106  2024           NaN  Lending rate is the bank rate that usually mee...   \n",
+              "97107  2024  2.201705e+02  Index of the prices of consumption goods and s...   \n",
+              "97108  2024  1.329393e+11  Broad money is the sum of all liquid financial...   \n",
+              "97109  2024  1.527961e+11  Net domestic credit is the sum of net claims o...   \n",
+              "\n",
+              "                                     SOURCE_ORGANIZATION  Unnamed: 4  \\\n",
+              "0      International Homicide Statistics database, UN...         NaN   \n",
+              "1      Uppsala Conflict Data Program (UCDP), uri: htt...         NaN   \n",
+              "2      Worldwide Governance Indicators, World Bank (W...         NaN   \n",
+              "3      Balance of Payments Statistics Yearbook and da...         NaN   \n",
+              "4      Balance of Payments Statistics Yearbook and da...         NaN   \n",
+              "...                                                  ...         ...   \n",
+              "97105  International Financial Statistics database, I...         NaN   \n",
+              "97106  International Financial Statistics database, I...         NaN   \n",
+              "97107  International Financial Statistics database, I...         NaN   \n",
+              "97108  International Financial Statistics database, I...         NaN   \n",
+              "97109  International Financial Statistics database, I...         NaN   \n",
+              "\n",
+              "      indicator_name  value_y  \n",
+              "0                NaN      NaN  \n",
+              "1                NaN      NaN  \n",
+              "2                NaN      NaN  \n",
+              "3                NaN      NaN  \n",
+              "4                NaN      NaN  \n",
+              "...              ...      ...  \n",
+              "97105            NaN      NaN  \n",
+              "97106            NaN      NaN  \n",
+              "97107            NaN      NaN  \n",
+              "97108            NaN      NaN  \n",
+              "97109            NaN      NaN  \n",
+              "\n",
+              "[97110 rows x 11 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-44e71921-ba9c-4448-81e4-5b811adb95f4\" 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>Country Name</th>\n",
+              "      <th>country_code</th>\n",
+              "      <th>Indicator Name</th>\n",
+              "      <th>indicator_code</th>\n",
+              "      <th>year</th>\n",
+              "      <th>value_x</th>\n",
+              "      <th>SOURCE_NOTE</th>\n",
+              "      <th>SOURCE_ORGANIZATION</th>\n",
+              "      <th>Unnamed: 4</th>\n",
+              "      <th>indicator_name</th>\n",
+              "      <th>value_y</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Intentional homicides, male (per 100,000 male)</td>\n",
+              "      <td>VC.IHR.PSRC.MA.P5</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Intentional homicides, male are estimates of u...</td>\n",
+              "      <td>International Homicide Statistics database, UN...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Battle-related deaths (number of people)</td>\n",
+              "      <td>VC.BTL.DETH</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Battle-related deaths are deaths in battle-rel...</td>\n",
+              "      <td>Uppsala Conflict Data Program (UCDP), uri: htt...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Voice and Accountability: Percentile Rank</td>\n",
+              "      <td>VA.PER.RNK</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>The Worldwide Governance Indicators (WGI) are ...</td>\n",
+              "      <td>Worldwide Governance Indicators, World Bank (W...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Transport services (% of commercial service ex...</td>\n",
+              "      <td>TX.VAL.TRAN.ZS.WT</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Transport is the process of carriage of people...</td>\n",
+              "      <td>Balance of Payments Statistics Yearbook and da...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Computer, communications and other services (%...</td>\n",
+              "      <td>TX.VAL.OTHR.ZS.WT</td>\n",
+              "      <td>1960</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Computer, communications and other services in...</td>\n",
+              "      <td>Balance of Payments Statistics Yearbook and da...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97105</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Claims on central government, etc. (% GDP)</td>\n",
+              "      <td>FS.AST.CGOV.GD.ZS</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>2.022259e+01</td>\n",
+              "      <td>Claims on central government include loans to ...</td>\n",
+              "      <td>International Financial Statistics database, I...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97106</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Lending interest rate (%)</td>\n",
+              "      <td>FR.INR.LEND</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Lending rate is the bank rate that usually mee...</td>\n",
+              "      <td>International Financial Statistics database, I...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97107</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Consumer price index (2010 = 100)</td>\n",
+              "      <td>FP.CPI.TOTL</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>2.201705e+02</td>\n",
+              "      <td>Index of the prices of consumption goods and s...</td>\n",
+              "      <td>International Financial Statistics database, I...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97108</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Broad money (current LCU)</td>\n",
+              "      <td>FM.LBL.BMNY.CN</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>1.329393e+11</td>\n",
+              "      <td>Broad money is the sum of all liquid financial...</td>\n",
+              "      <td>International Financial Statistics database, I...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>97109</th>\n",
+              "      <td>Tunisia</td>\n",
+              "      <td>TUN</td>\n",
+              "      <td>Net domestic credit (current LCU)</td>\n",
+              "      <td>FM.AST.DOMS.CN</td>\n",
+              "      <td>2024</td>\n",
+              "      <td>1.527961e+11</td>\n",
+              "      <td>Net domestic credit is the sum of net claims o...</td>\n",
+              "      <td>International Financial Statistics database, I...</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>97110 rows × 11 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-44e71921-ba9c-4448-81e4-5b811adb95f4')\"\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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+              "  <style>\n",
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+              "\n",
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+              "\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
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+              "\n",
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+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
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+              "          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",
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+              "                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-b7e3f736-7665-4312-9a66-522ad4615c28 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "  <div id=\"id_c77e85ef-94f2-4db0-b94c-4bdca1b875bf\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\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-generate: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",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate: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",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_data_all')\"\n",
+              "            title=\"Generate code using this dataframe.\"\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",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_c77e85ef-94f2-4db0-b94c-4bdca1b875bf button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_data_all');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df_data_all",
+              "repr_error": "Out of range float values are not JSON compliant: nan"
+            }
+          },
+          "metadata": {},
+          "execution_count": 165
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# EDA\n",
+        "df_data_all.info()\n",
+        "df_data_all.isnull().sum()\n",
+        "df_data_all[\"value_x\"].describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 647
+        },
+        "id": "-pETwejOkuPU",
+        "outputId": "587fc930-b6a9-4cbc-8af2-4065f4d80dbc"
+      },
+      "execution_count": 166,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 97110 entries, 0 to 97109\n",
+            "Data columns (total 11 columns):\n",
+            " #   Column               Non-Null Count  Dtype  \n",
+            "---  ------               --------------  -----  \n",
+            " 0   Country Name         97110 non-null  object \n",
+            " 1   country_code         97110 non-null  object \n",
+            " 2   Indicator Name       97110 non-null  object \n",
+            " 3   indicator_code       97110 non-null  object \n",
+            " 4   year                 97110 non-null  int64  \n",
+            " 5   value_x              44812 non-null  float64\n",
+            " 6   SOURCE_NOTE          97110 non-null  object \n",
+            " 7   SOURCE_ORGANIZATION  97110 non-null  object \n",
+            " 8   Unnamed: 4           0 non-null      float64\n",
+            " 9   indicator_name       0 non-null      object \n",
+            " 10  value_y              0 non-null      float64\n",
+            "dtypes: float64(3), int64(1), object(7)\n",
+            "memory usage: 8.1+ MB\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    4.481200e+04\n",
+              "mean     2.435825e+09\n",
+              "std      1.161396e+10\n",
+              "min     -1.934900e+10\n",
+              "25%      6.073641e+00\n",
+              "50%      4.317533e+01\n",
+              "75%      1.387495e+06\n",
+              "max      1.797240e+11\n",
+              "Name: value_x, dtype: float64"
+            ],
+            "text/html": [
+              "<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>value_x</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>4.481200e+04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.435825e+09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.161396e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-1.934900e+10</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>6.073641e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>4.317533e+01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.387495e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.797240e+11</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> float64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 166
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Visualisation\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "gdp=df_data_all[df_data_all['Indicator Name']==\"GDP (current US$)\"]\n",
+        "gdp=gdp.sort_values(\"year\")\n",
+        "plt.figure(figsize=(8,5))\n",
+        "plt.plot(gdp['year'],gdp['value_x'])\n",
+        "plt.title('GDP Over Time')\n",
+        "plt.xlabel('year')\n",
+        "plt.ylabel('GDP (current US$)')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "id": "RQmsp2C9lnvQ",
+        "outputId": "0b26f67e-cb98-44a6-b28e-5b82828d4538"
+      },
+      "execution_count": 172,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "for name in[\"GDP (current US$)\",\"Population, total\"]:\n",
+        "  compare=df_data_all[df_data_all['Indicator Name']==name]\n",
+        "  plt.plot(compare[\"year\"],compare['value_x'],label=name)\n",
+        "\n",
+        "plt.title('GDP (current US$) vs Population, total')\n",
+        "plt.xlabel('year')\n",
+        "plt.ylabel('value')\n",
+        "plt.grid(True)\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "skh0rmBgqF5T",
+        "outputId": "20e836a3-c7bc-482d-88d3-60645d97d53d"
+      },
+      "execution_count": 178,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "indicators=[\"GDP (current US$)\",\"Population, total\",\"Forest area (% of land area)\"]\n",
+        "data_corr =df_data_all[df_data_all['Indicator Name'].isin (indicators)]\n",
+        "data_corr=data_corr.pivot_table(index=\"year\",columns=\"Indicator Name\",values=\"value_x\")\n",
+        "data_corr=data_corr.dropna()\n",
+        "sns.heatmap(data_corr.corr())\n",
+        "plt.title('Correlation between indicators')\n",
+        "\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 636
+        },
+        "id": "dJeCJ2KgsDR_",
+        "outputId": "95af2a1f-7c42-4bb7-98bf-a242a771f2ff"
+      },
+      "execution_count": 194,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/aak_tele_science_data_engineer_challenge (1).py b/aak_tele_science_data_engineer_challenge (1).py
new file mode 100644
index 0000000..d9bd6e2
--- /dev/null
+++ b/aak_tele_science_data_engineer_challenge (1).py	
@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+"""AAK-Tele-Science/data_engineer_challenge.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/120kjcyfgdBBv4HdRy_BVtiVEMwLgKAxZ
+"""
+
+import numpy as np
+import pandas as pd
+import requests
+
+df_data=pd.read_csv("/content/API_TUN_DS2_en_csv_v2_1037756.csv",skiprows=4)
+df_country=pd.read_csv("/content/Metadata_Country_API_TUN_DS2_en_csv_v2_1037756.csv")
+df_indicators=pd.read_csv("/content/Metadata_Indicator_API_TUN_DS2_en_csv_v2_1037756.csv",)
+
+#df_data
+indicator_code=df_data["Indicator Code"].unique().tolist()
+#indicator_code
+
+# Web Scraping
+country_code='TN'
+start_year=1960
+end_year=2024
+all_data=[]
+for ind in indicator_code:
+  url=(f"https://api.worldbank.org/country/{country_code}/indicator/{ind}"
+  f"?date={start_year}:{end_year}&format=json&per_page=1000")
+response=requests.get(url)
+if response.status_code==200 :
+  data= response.json()
+  if len(data) >1 :
+    for i in data[1]:
+      all_data.append({
+          "country_code":i.get("countryiso3code"),
+          "indicator_code":i.get("indicator").get("id"),
+          "indicator_name":i.get("indicator").get("value"),
+          "year":i.get("date"),
+          "value":i.get("value")})
+
+df_scraped=pd.DataFrame(all_data)
+df_scraped
+
+# ETL
+df_data.drop(df_data.tail(1).index,inplace=True)
+years=[col for col in df_data.columns if col.isdigit()]
+id_vars=["Country Name","Country Code","Indicator Name","Indicator Code"]
+df_data_long=pd.melt(df_data,id_vars=id_vars,value_vars=years,var_name="year",value_name="value")
+df_data_long["year"]=pd.to_numeric(df_data_long["year"],errors="coerce")
+df_data_long["value"]=pd.to_numeric(df_data_long["value"],errors="coerce")
+
+df_scraped["year"]=pd.to_numeric(df_scraped["year"],errors="coerce")
+df_scraped["value"]=pd.to_numeric(df_scraped["value"],errors="coerce")
+df_merged=pd.merge(df_data_long,df_country,on =["Country Code"],how='left')
+df_indicators.rename(columns={'INDICATOR_CODE':'Indicator_Code'},inplace=True)
+
+df_data_long.rename(columns={'Indicator Code':'Indicator_Code'},inplace=True)
+df_merged=pd.merge(df_data_long,df_indicators, on=["Indicator_Code"],how='left')
+
+df_merged.rename(columns={'Country Code':'country_code'},inplace=True)
+df_merged.rename(columns={'Indicator_Code':'indicator_code'},inplace=True)
+df_data_long.rename(columns={'Year':'year'},inplace=True)
+
+df_data_all=pd.merge(df_merged,df_scraped , on =["country_code","indicator_code","year"],how ='left')
+df_data_all
+
+# EDA
+df_data_all.info()
+df_data_all.isnull().sum()
+df_data_all["value_x"].describe()
+
+#Visualisation
+import matplotlib.pyplot as plt
+import seaborn as sns
+gdp=df_data_all[df_data_all['Indicator Name']=="GDP (current US$)"]
+gdp=gdp.sort_values("year")
+plt.figure(figsize=(8,5))
+plt.plot(gdp['year'],gdp['value_x'])
+plt.title('GDP Over Time')
+plt.xlabel('year')
+plt.ylabel('GDP (current US$)')
+plt.grid(True)
+plt.show()
+
+for name in["GDP (current US$)","Population, total"]:
+  compare=df_data_all[df_data_all['Indicator Name']==name]
+  plt.plot(compare["year"],compare['value_x'],label=name)
+
+plt.title('GDP (current US$) vs Population, total')
+plt.xlabel('year')
+plt.ylabel('value')
+plt.grid(True)
+plt.show()
+
+indicators=["GDP (current US$)","Population, total","Forest area (% of land area)"]
+data_corr =df_data_all[df_data_all['Indicator Name'].isin (indicators)]
+data_corr=data_corr.pivot_table(index="year",columns="Indicator Name",values="value_x")
+data_corr=data_corr.dropna()
+sns.heatmap(data_corr.corr())
+plt.title('Correlation between indicators')
+
+plt.show()
\ No newline at end of file