Transformation_of_Data.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
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        "id": "Mj7HVhYHaGGg",
        "outputId": "723a3b99-8629-42fb-b0f3-cc5da157fada"
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          "text": [
            "--2023-01-14 17:51:39--  https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/datasource.zip\n",
            "Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.63.118.104\n",
            "Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4249 (4.1K) [application/zip]\n",
            "Saving to: ‘datasource.zip’\n",
            "\n",
            "datasource.zip      100%[===================>]   4.15K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-01-14 17:51:39 (383 MB/s) - ‘datasource.zip’ saved [4249/4249]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0221EN-SkillsNetwork/labs/module%206/Lab%20-%20Extract%20Transform%20Load/data/datasource.zip"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "import glob,os\n",
        "import xml.etree.ElementTree as ET"
      ],
      "metadata": {
        "id": "fKUKOfgHdw1Q"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#UNZIP THE FILE"
      ],
      "metadata": {
        "id": "GK2MxO0IauKA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip datasource.zip -d dealership_data\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vGI-cj0aaG3v",
        "outputId": "4edfbb99-352b-412a-d74a-cced21629f57"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  datasource.zip\n",
            "  inflating: dealership_data/used_car_prices1.csv  \n",
            "  inflating: dealership_data/used_car_prices2.csv  \n",
            "  inflating: dealership_data/used_car_prices3.csv  \n",
            "  inflating: dealership_data/used_car_prices1.json  \n",
            "  inflating: dealership_data/used_car_prices2.json  \n",
            "  inflating: dealership_data/used_car_prices3.json  \n",
            "  inflating: dealership_data/used_car_prices1.xml  \n",
            "  inflating: dealership_data/used_car_prices2.xml  \n",
            "  inflating: dealership_data/used_car_prices3.xml  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#SET PATHS"
      ],
      "metadata": {
        "id": "-j6CDFjgbI1X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tmpfile    = \"temp.tmp\"               # file used to store all extracted data\n",
        "logfile    = \"logfile.txt\"            # all event logs will be stored in this file\n",
        "targetfile = \"transformed_data.csv\"   # file where transformed data is stored"
      ],
      "metadata": {
        "id": "LtBRLsoAaUq5"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#CSV EXTRACT FUNCTION"
      ],
      "metadata": {
        "id": "73PILsdqbbKM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_from_csv(file_to_process):\n",
        "    dataframe = pd.read_csv(file_to_process)\n",
        "    return dataframe"
      ],
      "metadata": {
        "id": "jP23M6pGbQL0"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### JSON Extract Function"
      ],
      "metadata": {
        "id": "JCUIwz4Nbo2h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_from_json(file_to_process):\n",
        "    dataframe = pd.read_json(file_to_process,lines=True)\n",
        "    return dataframe"
      ],
      "metadata": {
        "id": "aIth0593bnGf"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# XML EXTRACT FUNCTION"
      ],
      "metadata": {
        "id": "O_ABlAQlby5f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_from_xml(file_to_process):\n",
        "  #https://www.analyticsvidhya.com/blog/2021/06/implementing-python-to-learn-data-engineering-etl-process/\n",
        "\n",
        "    dataframe = pd.DataFrame(columns=['car_model','year_of_manufacture','price', 'fuel'])\n",
        "\n",
        "    tree = ET.parse(file_to_process) \n",
        "\n",
        "    root = tree.getroot() \n",
        "\n",
        "    for person in root: \n",
        "\n",
        "        car_model = person.find(\"car_model\").text \n",
        "\n",
        "        year_of_manufacture = int(person.find(\"year_of_manufacture\").text)\n",
        "\n",
        "        price = float(person.find(\"price\").text) \n",
        "\n",
        "        fuel = person.find(\"fuel\").text \n",
        "\n",
        "        dataframe = dataframe.append({\"car_model\":car_model, \"year_of_manufacture\":year_of_manufacture, \"price\":price, \"fuel\":fuel}, ignore_index=True) \n",
        "\n",
        "        return dataframe"
      ],
      "metadata": {
        "id": "sSLaNLhZbxts"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract():\n",
        "    extracted_data = pd.DataFrame(columns=['car_model','year_of_manufacture','price', 'fuel']) # create an empty data frame to hold extracted data\n",
        "    \n",
        "    #process all csv files\n",
        "    for csvfile in glob.glob(\"dealership_data/*.csv\"):\n",
        "        extracted_data = extracted_data.append(extract_from_csv(csvfile), ignore_index=True)\n",
        "        \n",
        "    #process all json files\n",
        "    for jsonfile in glob.glob(\"dealership_data/*.json\"):\n",
        "        extracted_data = extracted_data.append(extract_from_json(jsonfile), ignore_index=True)\n",
        "    \n",
        "    #process all xml files\n",
        "    for xmlfile in glob.glob(\"dealership_data/*.xml\"):\n",
        "        extracted_data = extracted_data.append(extract_from_xml(xmlfile), ignore_index=True)\n",
        "        \n",
        "    return extracted_data"
      ],
      "metadata": {
        "id": "Ai_GF6UYcEZs"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#TRANSFORM"
      ],
      "metadata": {
        "id": "bVRJ1zbVcoBd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def transform(data): \n",
        "        data['price'] = round(data.price,2)\n",
        "        return data"
      ],
      "metadata": {
        "id": "ryNE23B1cjbv"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LOADING "
      ],
      "metadata": {
        "id": "PmZi4mladLfy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load(targetfile,data_to_load):\n",
        "    data_to_load.to_csv(targetfile)  "
      ],
      "metadata": {
        "id": "Lsj9u6ordGy1"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# LOGGING"
      ],
      "metadata": {
        "id": "nunb-q-gdYmK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def log(message):\n",
        "    timestamp_format = '%Y-%h-%d-%H:%M:%S' # Year-Monthname-Day-Hour-Minute-Second\n",
        "    now = datetime.now() # get current timestamp\n",
        "    timestamp = now.strftime(timestamp_format)\n",
        "    with open(\"logfile.txt\",\"a\") as f:\n",
        "        f.write(timestamp + ',' + message + '\\n')"
      ],
      "metadata": {
        "id": "zxrHjLHvdX12"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#RUNNING ETL JOBS"
      ],
      "metadata": {
        "id": "vh3UF4e4d4f9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "log(\"ETL Job Started\")"
      ],
      "metadata": {
        "id": "7FalaTXgdtri"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "log(\"Extract phase Started\")\n",
        "extracted_data = extract()\n",
        "log(\"Extract phase Ended\")\n",
        "extracted_data"
      ],
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        "colab": {
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        "id": "cZxTDlsxeA_f",
        "outputId": "5c000341-852f-425f-e2d7-129ebc4bb8cc"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        car_model year_of_manufacture         price    fuel\n",
              "0        alto k10                2016   4253.731343  Petrol\n",
              "1           ignis                2017   7313.432836  Petrol\n",
              "2             sx4                2011   6567.164179  Petrol\n",
              "3        alto k10                2014   3731.343284  Petrol\n",
              "4         wagon r                2013   4328.358209  Petrol\n",
              "..            ...                 ...           ...     ...\n",
              "58        etios g                2014   6119.402985  Petrol\n",
              "59       fortuner                2014  29835.820896  Diesel\n",
              "60  corolla altis                2016  21985.074627  Diesel\n",
              "61  corolla altis                2013  10373.134328  Petrol\n",
              "62     etios liva                2014   5895.522388  Diesel\n",
              "\n",
              "[63 rows x 4 columns]"
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alto k10</td>\n",
              "      <td>2016</td>\n",
              "      <td>4253.731343</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ignis</td>\n",
              "      <td>2017</td>\n",
              "      <td>7313.432836</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>alto k10</td>\n",
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              "      <td>3731.343284</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>wagon r</td>\n",
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              "      <td>4328.358209</td>\n",
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              "      <th>58</th>\n",
              "      <td>etios g</td>\n",
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              "      <td>6119.402985</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>fortuner</td>\n",
              "      <td>2014</td>\n",
              "      <td>29835.820896</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>corolla altis</td>\n",
              "      <td>2016</td>\n",
              "      <td>21985.074627</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>corolla altis</td>\n",
              "      <td>2013</td>\n",
              "      <td>10373.134328</td>\n",
              "      <td>Petrol</td>\n",
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              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>etios liva</td>\n",
              "      <td>2014</td>\n",
              "      <td>5895.522388</td>\n",
              "      <td>Diesel</td>\n",
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              "  </tbody>\n",
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              "<p>63 rows × 4 columns</p>\n",
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          "metadata": {},
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      "source": [
        "log(\"Transform phase Started\")\n",
        "transformed_data = transform(extracted_data)\n",
        "log(\"Transform phase Ended\")\n",
        "transformed_data "
      ],
      "metadata": {
        "colab": {
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          "height": 424
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        "id": "KDrR1ejjfPGo",
        "outputId": "a5a881c7-f86f-4aef-9386-822f6bb7f216"
      },
      "execution_count": 45,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        car_model year_of_manufacture     price    fuel\n",
              "0        alto k10                2016   4253.73  Petrol\n",
              "1           ignis                2017   7313.43  Petrol\n",
              "2             sx4                2011   6567.16  Petrol\n",
              "3        alto k10                2014   3731.34  Petrol\n",
              "4         wagon r                2013   4328.36  Petrol\n",
              "..            ...                 ...       ...     ...\n",
              "58        etios g                2014   6119.40  Petrol\n",
              "59       fortuner                2014  29835.82  Diesel\n",
              "60  corolla altis                2016  21985.07  Diesel\n",
              "61  corolla altis                2013  10373.13  Petrol\n",
              "62     etios liva                2014   5895.52  Diesel\n",
              "\n",
              "[63 rows x 4 columns]"
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>alto k10</td>\n",
              "      <td>2016</td>\n",
              "      <td>4253.73</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ignis</td>\n",
              "      <td>2017</td>\n",
              "      <td>7313.43</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>sx4</td>\n",
              "      <td>2011</td>\n",
              "      <td>6567.16</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>alto k10</td>\n",
              "      <td>2014</td>\n",
              "      <td>3731.34</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>wagon r</td>\n",
              "      <td>2013</td>\n",
              "      <td>4328.36</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>etios g</td>\n",
              "      <td>2014</td>\n",
              "      <td>6119.40</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>fortuner</td>\n",
              "      <td>2014</td>\n",
              "      <td>29835.82</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>corolla altis</td>\n",
              "      <td>2016</td>\n",
              "      <td>21985.07</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>corolla altis</td>\n",
              "      <td>2013</td>\n",
              "      <td>10373.13</td>\n",
              "      <td>Petrol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>etios liva</td>\n",
              "      <td>2014</td>\n",
              "      <td>5895.52</td>\n",
              "      <td>Diesel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d22d8c79-a5fe-4783-846c-654ff96e6282')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
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              "\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",
              "    [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",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-d22d8c79-a5fe-4783-846c-654ff96e6282 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-d22d8c79-a5fe-4783-846c-654ff96e6282');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log(\"Load phase Started\")\n",
        "load(targetfile,transformed_data)\n",
        "log(\"Load phase Ended\")"
      ],
      "metadata": {
        "id": "qKiWuiB_eXrV"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "BsA4VbmGffDr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}