p2a-field-decomp.ipynb

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        "# Field Decomposition"
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        "In this notebook, we decompose the fields from the raw tables that contain more than one property in their value. Specifically, we have two description fields, one in `air_carriers` and another in `bird_airports` that need to be split up into their individual components.\n"
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        "%%bigquery\n",
        "select * from airline_raw.air_carriers limit 5"
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              "    code                      description                        load_time\n",
              "0  19031   Mackey International Inc.: MAC 2024-01-26 22:23:22.051288+00:00\n",
              "1  19032  Munz Northern Airlines Inc.: XY 2024-01-26 22:23:22.051288+00:00\n",
              "2  19033       Cochise Airlines Inc.: COC 2024-01-26 22:23:22.051288+00:00\n",
              "3  19034   Golden Gate Airlines Inc.: GSA 2024-01-26 22:23:22.051288+00:00\n",
              "4  19035               Aeromech Inc.: RZZ 2024-01-26 22:23:22.051288+00:00"
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        "%%bigquery\n",
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              "  code         description                        load_time\n",
              "0  CBK   Colby, KS: Murray 2024-01-22 00:47:19.324417+00:00\n",
              "1  ANP  Annapolis, MD: Lee 2024-01-22 00:47:19.324417+00:00\n",
              "2  KCA  Kuqa, China: Kuche 2024-01-22 00:47:19.324417+00:00\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-978d9e71-7e7d-4b96-89ad-6be78bf353ef')\"\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\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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              "  </style>\n",
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              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-978d9e71-7e7d-4b96-89ad-6be78bf353ef 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-978d9e71-7e7d-4b96-89ad-6be78bf353ef');\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",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    width: 32px;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "    40% {\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-f1d6afa6-d134-49bb-b540-0480d78c1186 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Air_Carrier"
      ],
      "metadata": {
        "id": "WDqcrc7Bo3_f"
      },
      "id": "WDqcrc7Bo3_f"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split up the description field from the raw `air_carriers` table and create a staging table from the results:"
      ],
      "metadata": {
        "id": "JkRSWgOxgGQ9"
      },
      "id": "JkRSWgOxgGQ9"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "select description, description_array[0] as airline_name, description_array[1] as airline_code\n",
        "from\n",
        "(select description, split(description, ':') as description_array\n",
        "from airline_raw.air_carriers\n",
        "limit 5)"
      ],
      "metadata": {
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      "id": "q1AQQLRe_B-W",
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      "outputs": [
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          "data": {
            "text/plain": [
              "Query is running:   0%|          |"
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        {
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          "data": {
            "text/plain": [
              "                       description                 airline_name airline_code\n",
              "0   Mackey International Inc.: MAC    Mackey International Inc.          MAC\n",
              "1  Munz Northern Airlines Inc.: XY  Munz Northern Airlines Inc.           XY\n",
              "2       Cochise Airlines Inc.: COC        Cochise Airlines Inc.          COC\n",
              "3   Golden Gate Airlines Inc.: GSA    Golden Gate Airlines Inc.          GSA\n",
              "4               Aeromech Inc.: RZZ                Aeromech Inc.          RZZ"
            ],
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              "      <th>2</th>\n",
              "      <td>Cochise Airlines Inc.: COC</td>\n",
              "      <td>Cochise Airlines Inc.</td>\n",
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              "    </tr>\n",
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              "      <th>3</th>\n",
              "      <td>Golden Gate Airlines Inc.: GSA</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "create or replace table airline_stg.Air_Carrier as\n",
        "  select airline_id, description_array[0] as airline_name, description_array[1] as airline_code, 'bird' as data_source, load_time\n",
        "  from\n",
        "  (select code as airline_id, split(description, ':') as description_array, load_time\n",
        "  from airline_raw.air_carriers)"
      ],
      "metadata": {
        "colab": {
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            "4ae68438f4cd40a6895eabf5c6bf5f82"
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      "id": "pSBegAMYXmIB",
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              "\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",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verify that the raw and staging tables both have the same record counts (i.e. we don't want to lose any records from raw to staging):"
      ],
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        "id": "wSOTivzegzUw"
      },
      "id": "wSOTivzegzUw"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "select (select count(*) from airline_raw.air_carriers) as raw_count,\n",
        "(select count(*) from airline_stg.Air_Carrier) as staging_count"
      ],
      "metadata": {
        "colab": {
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        {
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              "Query is running:   0%|          |"
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        {
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          "data": {
            "text/plain": [
              "   raw_count  staging_count\n",
              "0       1656           1656"
            ],
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              "\n",
              "  <div id=\"df-ea67ca6a-9775-4fb0-93c2-816087b3d9d7\" class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ea67ca6a-9775-4fb0-93c2-816087b3d9d7 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-ea67ca6a-9775-4fb0-93c2-816087b3d9d7');\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",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# bird_airport"
      ],
      "metadata": {
        "id": "BzZJ64uEpU5G"
      },
      "id": "BzZJ64uEpU5G"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split up the description from the `bird_airports` table. The description contains either a city, state and airport or a city, country, and airport:\n",
        "\n",
        "  Example: Austin, TX: Austin - Bergstrom International"
      ],
      "metadata": {
        "id": "IpP65q--_hcP"
      },
      "id": "IpP65q--_hcP"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "select code, city_state_array[0] as city, city_state_array[1] as state, airport_name\n",
        "from\n",
        "(select code, split(description_array[0], ',') as city_state_array, description_array[1] as airport_name\n",
        "from\n",
        "(select code, description, split(description, ':') as description_array\n",
        "from airline_raw.bird_airports\n",
        "limit 5))"
      ],
      "metadata": {
        "colab": {
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              "  code       city    state airport_name\n",
              "0  CBK      Colby       KS       Murray\n",
              "1  ANP  Annapolis       MD          Lee\n",
              "2  KCA       Kuqa    China        Kuche\n",
              "3  MOL      Molde   Norway          Aro\n",
              "4  OIC    Norwich       NY        Eaton"
            ],
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              "      <td>CBK</td>\n",
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              "      <td>KCA</td>\n",
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unfortunately, this query doesn't get us very far. It fails with `400 Array index 1 is out of bounds`. That's because the description doesn't always contain two elements:"
      ],
      "metadata": {
        "id": "WQlSl0yrhIaF"
      },
      "id": "WQlSl0yrhIaF"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "create or replace table airline_stg.bird_airport as\n",
        "  select code, airport_name, city_state_array[0] as city, city_state_array[1] as state, 'bird' as data_source, load_time\n",
        "  from\n",
        "    (select code, split(description_array[0], ',') as city_state_array, description_array[1] as airport_name, load_time\n",
        "    from\n",
        "      (select code, split(description, ':') as description_array, load_time\n",
        "      from airline_raw.bird_airports))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkYxBHw5fShJ",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1706887926068,
          "user_tz": 360,
          "elapsed": 1381,
          "user": {
            "displayName": "",
            "userId": ""
          }
        },
        "outputId": "7620123e-2a50-474f-a43a-f662b5106554"
      },
      "id": "DkYxBHw5fShJ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executing query with job ID: 10d93347-72fb-4b3d-9649-749962f3b9b1\n",
            "\rQuery executing: 0.60s"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "ERROR:\n",
            " 400 Array index 1 is out of bounds (overflow)\n",
            "\n",
            "Location: US\n",
            "Job ID: 10d93347-72fb-4b3d-9649-749962f3b9b1\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instead of transforming the data with SQL, we'll switch to Python so that we end up with simpler and more maintainable code. We'll output the results into json and load them into a staging table.\n"
      ],
      "metadata": {
        "id": "3FaB_SdrcdZf"
      },
      "id": "3FaB_SdrcdZf"
    },
    {
      "cell_type": "code",
      "source": [
        "import json, datetime\n",
        "from google.cloud import bigquery\n",
        "\n",
        "project_id = \"cs329e-sp2024\"\n",
        "raw_dataset_name = \"airline_raw\"\n",
        "raw_table_name = \"bird_airports\"\n",
        "stg_dataset_name = \"airline_stg\"\n",
        "stg_table_name = \"bird_airports\" # lowercase the name because it's an intermediate table\n",
        "\n",
        "bird_airports = []\n",
        "target_table_id = \"{}.{}.{}\".format(project_id, stg_dataset_name, stg_table_name)\n",
        "\n",
        "def serialize_datetime(obj):\n",
        "    if isinstance(obj, datetime.datetime):\n",
        "        return obj.isoformat()\n",
        "    raise TypeError(\"Type not serializable\")\n",
        "\n",
        "# removes the entries from the dictionary whose values are None\n",
        "# this filter is needed for loading JSON into BQ\n",
        "def remove_none_values(record):\n",
        "    filtered_record = {}\n",
        "    for field in record.keys():\n",
        "        if record[field] != None:\n",
        "            filtered_record[field] = record[field]\n",
        "    return filtered_record\n",
        "\n",
        "\n",
        "schema = [bigquery.SchemaField(\"code\", \"STRING\", mode=\"REQUIRED\"),\n",
        "          bigquery.SchemaField(\"name\", \"STRING\", mode=\"NULLABLE\"),\n",
        "          bigquery.SchemaField(\"city\", \"STRING\", mode=\"NULLABLE\"),\n",
        "          bigquery.SchemaField(\"state\", \"STRING\", mode=\"NULLABLE\"),\n",
        "          bigquery.SchemaField(\"country\", \"STRING\", mode=\"NULLABLE\"),\n",
        "          bigquery.SchemaField(\"data_source\", \"STRING\", mode=\"REQUIRED\"),\n",
        "          bigquery.SchemaField(\"load_time\", \"TIMESTAMP\", mode=\"REQUIRED\")]\n",
        "\n",
        "bq_client = bigquery.Client()\n",
        "sql = \"select code, description, load_time from {}.{}\".format(raw_dataset_name, raw_table_name)\n",
        "query_job = bq_client.query(sql)\n",
        "\n",
        "index = 0\n",
        "\n",
        "for row in query_job:\n",
        "    code = row[\"code\"]\n",
        "    description = row[\"description\"]\n",
        "    load_time = json.dumps(row[\"load_time\"], default=serialize_datetime).replace('\"', '')\n",
        "    city = description.split(\",\")[0].strip()\n",
        "\n",
        "    if len(description.split(\",\")) > 1:\n",
        "        state_country = description.split(\",\")[1].split(\":\")[0].strip()\n",
        "\n",
        "        if state_country.isupper() and len(state_country) == 2:\n",
        "            state = state_country\n",
        "            country = 'US'\n",
        "        else:\n",
        "            state = None\n",
        "            country = state_country\n",
        "\n",
        "    else:\n",
        "        state_country = None\n",
        "        print('state_country is null: ' + description)\n",
        "\n",
        "    if len(description.split(\":\")) > 1:\n",
        "        name = description.split(\":\")[1].strip()\n",
        "    else:\n",
        "        name = None\n",
        "        print('airport name is null: ' + description)\n",
        "\n",
        "    record = {\"code\": code, \"name\": name, \"city\": city, \"state\": state, \"country\": country, \"data_source\": \"bird\", \"load_time\": load_time}\n",
        "    filtered_record = remove_none_values(record)\n",
        "\n",
        "    if index == 0:\n",
        "        print('printing first record for debugging', filtered_record)\n",
        "\n",
        "    bird_airports.append(filtered_record)\n",
        "\n",
        "    index += 1\n",
        "\n",
        "# load the records into staging table\n",
        "job_config = bigquery.LoadJobConfig(schema=schema, source_format=bigquery.SourceFormat.NEWLINE_DELIMITED_JSON, write_disposition='WRITE_TRUNCATE')\n",
        "table_ref = bigquery.table.TableReference.from_string(target_table_id)\n",
        "\n",
        "try:\n",
        "    load_job = bq_client.load_table_from_json(bird_airports, table_ref, job_config=job_config)\n",
        "    load_job.result()\n",
        "    destination_table = bq_client.get_table(target_table_id)\n",
        "    print('Wrote', destination_table.num_rows, 'records into', target_table_id)\n",
        "\n",
        "    if load_job.errors:\n",
        "      print('job errors:', load_job.errors)\n",
        "\n",
        "except Exception as e:\n",
        "    print(\"Error inserting into BQ: {}\".format(e))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pFHDTg8Wh_xE",
        "executionInfo": {
          "status": "ok",
          "timestamp": 1706981393250,
          "user_tz": 360,
          "elapsed": 4376,
          "user": {
            "displayName": "",
            "userId": ""
          }
        },
        "outputId": "391e31e9-9612-4847-dc69-bbb0f0243d22"
      },
      "id": "pFHDTg8Wh_xE",
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "printing first record for debugging {'code': 'CBK', 'name': 'Murray', 'city': 'Colby', 'state': 'KS', 'country': 'US', 'data_source': 'bird', 'load_time': '2024-01-26T22:23:29.069725+00:00'}\n",
            "state_country is null: Unknown Point in Alaska\n",
            "airport name is null: Unknown Point in Alaska\n",
            "Wrote 6510 records into cs329e-sp2024.airline_stg.bird_airports\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verify that we ended up with the same record count in the staging table as in the raw table:"
      ],
      "metadata": {
        "id": "S77Jwai4ivhO"
      },
      "id": "S77Jwai4ivhO"
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery\n",
        "select (select count(*) from airline_raw.bird_airports) as raw_count,\n",
        "  (select count(*) from airline_stg.bird_airports) as intermediate_stg_count"
      ],
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              "   raw_count  intermediate_stg_count\n",
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        "# Primary Key"
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        "%%bigquery\n",
        "select airline_id, count(*) duplicate_records\n",
        "from airline_stg.Air_Carrier\n",
        "group by airline_id\n",
        "having count(*) > 1\n",
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