Getting Started with BigQuery in Colab.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TirendazAcademy/Google-BigQuery-Tutorial/blob/main/Getting_started_with_BigQuery_in_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Provide your credentials to the runtime"
      ],
      "metadata": {
        "id": "eP-o7MP7zpix"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        },
        "id": "M9lk4WF6zeqp",
        "outputId": "8512eae3-cce2-4525-f26e-096d4f4c85e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Authenticated\n"
          ]
        }
      ],
      "source": [
        "from google.colab import auth\n",
        "auth.authenticate_user()\n",
        "print('Authenticated')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Use BigQuery via magics"
      ],
      "metadata": {
        "id": "40rJS1M90FAK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery --project tirendaz-ml-tutorial\n",
        "SELECT \n",
        "  COUNT(*) as total_rows\n",
        "FROM `bigquery-public-data.samples.natality`"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "azMnurh9zvqJ",
        "outputId": "02cbdd90-e04f-47c9-ca5d-370dcbc4c756"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_rows\n",
              "0   137826763"
            ],
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      "source": [
        "You can assign the output to a variable, df."
      ],
      "metadata": {
        "id": "xDaYbLmM0aUT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%bigquery --project tirendaz-ml-tutorial df\n",
        "SELECT \n",
        "  COUNT(*) as total_rows\n",
        "FROM `bigquery-public-data.samples.natality`"
      ],
      "metadata": {
        "id": "3lw9ZrQy0JOJ"
      },
      "execution_count": 3,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "p9fzidCS0pjg",
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      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   total_rows\n",
              "0   137826763"
            ],
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Working with BigQuery through google-cloud-bigquery"
      ],
      "metadata": {
        "id": "wzyRRT-601Lp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Declare the Cloud project ID\n",
        "project_id = 'tirendaz-ml-tutorial'"
      ],
      "metadata": {
        "id": "qOXuVNJB0rtp"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.cloud import bigquery\n",
        "client = bigquery.Client(project=project_id)\n",
        "\n",
        "# Assign data to df \n",
        "df = client.query('''\n",
        "  SELECT\n",
        "    source_year AS year,\n",
        "    COUNT(is_male) AS birth_count\n",
        "  FROM `bigquery-public-data.samples.natality`\n",
        "  GROUP BY year\n",
        "  ORDER BY year DESC\n",
        "  LIMIT 15 ''').to_dataframe()"
      ],
      "metadata": {
        "id": "BMeSn7YZ1hz-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "19kL9jfw13U-",
        "outputId": "4a03d1b6-ba17-4680-9bed-6bb265268e9f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   year  birth_count\n",
              "0  2008      4255156\n",
              "1  2007      4324008\n",
              "2  2006      4273225\n",
              "3  2005      4145619\n",
              "4  2004      4118907"
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              "            + ' 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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y160rUPw3Am4",
        "outputId": "7a636e6b-ecc2-4ec0-bd03-ca67e57edaf1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, you can use df as pandas dataframe. Let's show a graph for this df."
      ],
      "metadata": {
        "id": "RZMDeYVe2z-x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.plot(kind=\"bar\", x=\"year\", y=\"birth_count\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "UNTh8lLh1Kgc",
        "outputId": "b62ad29d-9139-4807-91d4-599b41d9434b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc4bc7f3fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Follow me [Twitter](https://twitter.com/TirendazAcademy), [Instagram](https://www.instagram.com/tirendazacademy), [Medium](https://tirendazacademy.medium.com),  [YouTube-Tr](https://youtube.com/c/tirendazakademi), [YouTube-Eng](https://www.youtube.com/channel/UCFU9Go20p01kC64w-tmFORw), [Tiktok](https://www.tiktok.com/@tirendazacademy), [GitHub](https://github.com/TirendazAcademy), [LinkedIn](https://www.linkedin.com/in/tirendaz-academy), [Kaggle](https://www.kaggle.com/tirendazacademy)"
      ],
      "metadata": {
        "id": "RuHx7eEt3aNp"
      }
    }
  ]
}