MyDemo.ipynb

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        "<a href=\"https://colab.research.google.com/github/kiamaikocoders/Pipelines/blob/main/MyDemo.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# **Building a Data Pipeline with Python**"
      ],
      "metadata": {
        "id": "SpKEcaDOi0An"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "> Author [Zachariah Komu](https://github.com/kiamaikocoders/Pipelines)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "# **Introduction**\n",
        "\n",
        "> In this demo, I'll showcase how to build and manage a simple data pipeline using Python. The pipeline will:\n",
        "\n",
        "\n",
        "\n",
        "* Extract data from a CSV file.\n",
        "* Transform the data by cleaning and preprocessing.\n",
        "* Load the data into an SQLite database.\n",
        "* Visualize the data using Matplotlib.\n",
        "\n",
        "\n",
        "> This demonstration highlights my skills in data analysis, Python programming, SQL, and data visualization.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "We8kSXO8kKXX"
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      "source": [
        "# Install any required libraries\n",
        "!pip install sqlalchemy psycopg2 pandas\n",
        "\n",
        "# Import necessary libraries\n",
        "import pandas as pd\n",
        "import requests\n",
        "from sqlalchemy import create_engine\n"
      ],
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            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.10/dist-packages (2.0.35)\n",
            "Requirement already satisfied: psycopg2 in /usr/local/lib/python3.10/dist-packages (2.9.9)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: typing-extensions>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy) (4.12.2)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from sqlalchemy) (3.1.1)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
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    {
      "cell_type": "markdown",
      "source": [
        "#  Data Ingestion: Pull Data from an online CSV"
      ],
      "metadata": {
        "id": "DpPsoQyDy-IL"
      }
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      "cell_type": "code",
      "source": [
        "# Load the dataset (for example, a sample CSV from a URL)\n",
        "data_url = \"https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv\"\n",
        "df = pd.read_csv(data_url)\n",
        "\n",
        "# Display the first few rows of the dataset\n",
        "df.head()\n"
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              "  Month   \"1958\"   \"1959\"   \"1960\"\n",
              "0   JAN      340      360      417\n",
              "1   FEB      318      342      391\n",
              "2   MAR      362      406      419\n",
              "3   APR      348      396      461\n",
              "4   MAY      363      420      472"
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"NOV\",\n          \"OCT\",\n          \"JAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1958\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64,\n        \"min\": 310,\n        \"max\": 505,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          310,\n          359,\n          340\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1959\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69,\n        \"min\": 342,\n        \"max\": 559,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          362,\n          407,\n          360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1960\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 390,\n        \"max\": 622,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          535,\n          417,\n          390\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#  Data Transformation: Clean and Manipulate the Data"
      ],
      "metadata": {
        "id": "FjCSVUd9zPoq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for null values\n",
        "print(\"Null values before cleaning:\")\n",
        "print(df.isnull().sum())\n",
        "\n",
        "# Data Transformation: Example of handling missing values\n",
        "# we drop any rows with missing data\n",
        "df_cleaned = df.dropna()\n",
        "\n",
        "# creating new columns\n",
        "numeric_columns = df_cleaned.select_dtypes(include=['number']).columns\n",
        "df_cleaned['Total'] = df_cleaned[numeric_columns].sum(axis=1)\n",
        "\n",
        "# the cleaned and transformed data\n",
        "df_cleaned.head()\n"
      ],
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        "colab": {
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null values before cleaning:\n",
            "Month      0\n",
            " \"1958\"    0\n",
            " \"1959\"    0\n",
            " \"1960\"    0\n",
            "dtype: int64\n"
          ]
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              "  Month   \"1958\"   \"1959\"   \"1960\"  Total\n",
              "0   JAN      340      360      417   1117\n",
              "1   FEB      318      342      391   1051\n",
              "2   MAR      362      406      419   1187\n",
              "3   APR      348      396      461   1205\n",
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              "      <td>APR</td>\n",
              "      <td>348</td>\n",
              "      <td>396</td>\n",
              "      <td>461</td>\n",
              "      <td>1205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MAY</td>\n",
              "      <td>363</td>\n",
              "      <td>420</td>\n",
              "      <td>472</td>\n",
              "      <td>1255</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-679f055d-8ca7-430b-b1ea-53e9019375fd')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-679f055d-8ca7-430b-b1ea-53e9019375fd 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-679f055d-8ca7-430b-b1ea-53e9019375fd');\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-c183f613-58d1-46a4-8ec6-b5c00d72cde6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c183f613-58d1-46a4-8ec6-b5c00d72cde6')\"\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-c183f613-58d1-46a4-8ec6-b5c00d72cde6 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_cleaned",
              "summary": "{\n  \"name\": \"df_cleaned\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Month\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 12,\n        \"samples\": [\n          \"NOV\",\n          \"OCT\",\n          \"JAN\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1958\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 64,\n        \"min\": 310,\n        \"max\": 505,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          310,\n          359,\n          340\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1959\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69,\n        \"min\": 342,\n        \"max\": 559,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          362,\n          407,\n          360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \" \\\"1960\\\"\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 390,\n        \"max\": 622,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          535,\n          417,\n          390\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Total\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 210,\n        \"min\": 1051,\n        \"max\": 1670,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          1062,\n          1227,\n          1117\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " # Connect to an in-memory SQLite database"
      ],
      "metadata": {
        "id": "1RSaiwwazTDS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "engine = create_engine('sqlite:///:memory:') # Connects to an in-memory SQLite database\n",
        "# engine = create_engine('postgresql://user:password@host:port/database') # For PostgreSQL\n",
        "# engine = create_engine('mysql://user:password@host:port/database') # For MySQL\n",
        "\n",
        "df_cleaned.to_sql('airtravel', engine, if_exists='replace', index=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dY5Jks-Hrq3r",
        "outputId": "834aacaa-9b0d-4306-81b0-ab7ac6844c9b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data visualization"
      ],
      "metadata": {
        "id": "54iLnm-Ezpyx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Basic visualization of the transformed data\n",
        "df_cleaned.plot(kind='bar', x='Month', y='Total')\n",
        "plt.title('Total Air Travel per Month')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "CVZvf672rbTE",
        "outputId": "f8a30805-d032-4d93-bec9-ce692235ef35"
      },
      "execution_count": 8,
      "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": [],
      "metadata": {
        "id": "RdZ-0BMisWmh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}