GAPandas4

GAPandas4 is a Python package for querying the Google Analytics Data API for GA4 and displaying the results in a Pandas dataframe. It is the successor to the GAPandas package, which did the same thing for GA3 or Universal Analytics. GAPandas4 is a wrapper around the official Google Analytics Data API package and simplifies imports and queries, requiring far less code.

Before you start

In order to use GAPandas4 you will first need to create a Google Service Account with access to the Google Analytics Data API and export a client secrets JSON keyfile to use for authentication. You'll also need to add the service account email address as a user on the Google Analytics 4 property you wish to access, and you'll need to note the property ID to use in your queries.

Installation

As this is currently in alpha, there's currently no Pip package, however, you can install the code into your Python environment directly from GitHub using the command below. It will run fine in a Jupyter notebook, a Python IDE, or a Python script.

pip3 install git+https://github.com/practical-data-science/gapandas4.git

Usage

GAPandas4 has been written to allow you to use as little code as possible. Unlike the previous version of GAPandas for Universal Analytics, which used a payload based on a Python dictionary, GAPandas4 now uses a Protobuf (Protocol Buffer) payload as used in the API itself.

Report

The query() function is used to send a protobug API payload to the API. The function supports various report types via the report_type argument. Standard reports are handled using report_type="report", but this is also the default. Data are returned as a Pandas dataframe.

import gapandas4 as gp

service_account = 'client_secrets.json'
property_id = 'xxxxxxxxx'

report_request = gp.RunReportRequest(
    property=f"properties/{property_id}",
    dimensions=[
        gp.Dimension(name="country"),
        gp.Dimension(name="city")
    ],
    metrics=[
        gp.Metric(name="activeUsers")
    ],
    date_ranges=[gp.DateRange(start_date="2022-06-01", end_date="2022-06-01")],
)

df = gp.query(service_account, report_request, report_type="report")
print(df.head())

Batch report

If you construct a protobuf payload using BatchRunReportsRequest() you can pass up to five requests at once. These are returned as a list of Pandas dataframes, so will need to access them using their index.

import gapandas4 as gp

service_account = 'client_secrets.json'
property_id = 'xxxxxxxxx'


batch_report_request = gp.BatchRunReportsRequest(
    property=f"properties/{property_id}",
    requests=[
        gp.RunReportRequest(
            dimensions=[
                gp.Dimension(name="country"),
                gp.Dimension(name="city")
            ],
            metrics=[
                gp.Metric(name="activeUsers")
            ],
            date_ranges=[gp.DateRange(start_date="2022-06-01", end_date="2022-06-01")]
        ),
        gp.RunReportRequest(
            dimensions=[
                gp.Dimension(name="country"),
                gp.Dimension(name="city")
            ],
            metrics=[
                gp.Metric(name="activeUsers")
            ],
            date_ranges=[gp.DateRange(start_date="2022-06-02", end_date="2022-06-02")]
        )
    ]
)

df = gp.query(service_account, batch_report_request, report_type="batch_report")
print(df[0].head())
print(df[1].head())

Pivot report

Constructing a report using RunPivotReportRequest() will return pivoted data in a single Pandas dataframe.

import gapandas4 as gp

service_account = 'client_secrets.json'
property_id = 'xxxxxxxxx'

pivot_request = gp.RunPivotReportRequest(
    property=f"properties/{property_id}",
    dimensions=[gp.Dimension(name="country"),
                gp.Dimension(name="browser")],
    metrics=[gp.Metric(name="sessions")],
    date_ranges=[gp.DateRange(start_date="2022-05-30", end_date="today")],
    pivots=[
        gp.Pivot(
            field_names=["country"],
            limit=5,
            order_bys=[
                gp.OrderBy(
                    dimension=gp.OrderBy.DimensionOrderBy(dimension_name="country")
                )
            ],
        ),
        gp.Pivot(
            field_names=["browser"],
            offset=0,
            limit=5,
            order_bys=[
                gp.OrderBy(
                    metric=gp.OrderBy.MetricOrderBy(metric_name="sessions"), desc=True
                )
            ],
        ),
    ],
)

df = gp.query(service_account, pivot_request, report_type="pivot")
print(df.head())

Batch pivot report

Constructing a payload using BatchRunPivotReportsRequest() will allow you to run up to five pivot reports. These are returned as a list of Pandas dataframes.

import gapandas4 as gp

service_account = 'client_secrets.json'
property_id = 'xxxxxxxxx'

batch_pivot_request = gp.BatchRunPivotReportsRequest(
    property=f"properties/{property_id}",
    requests=[
        gp.RunPivotReportRequest(
            dimensions=[gp.Dimension(name="country"),
                        gp.Dimension(name="browser")],
                metrics=[gp.Metric(name="sessions")],
                date_ranges=[gp.DateRange(start_date="2022-05-30", end_date="today")],
                pivots=[
                    gp.Pivot(
                        field_names=["country"],
                        limit=5,
                        order_bys=[
                            gp.OrderBy(
                                dimension=gp.OrderBy.DimensionOrderBy(dimension_name="country")
                            )
                        ],
                    ),
                    gp.Pivot(
                        field_names=["browser"],
                        offset=0,
                        limit=5,
                        order_bys=[
                            gp.OrderBy(
                                metric=gp.OrderBy.MetricOrderBy(metric_name="sessions"), desc=True
                            )
                        ],
                    ),
                ],
        ),
        gp.RunPivotReportRequest(
            dimensions=[gp.Dimension(name="country"),
                        gp.Dimension(name="browser")],
                metrics=[gp.Metric(name="sessions")],
                date_ranges=[gp.DateRange(start_date="2022-05-30", end_date="today")],
                pivots=[
                    gp.Pivot(
                        field_names=["country"],
                        limit=5,
                        order_bys=[
                            gp.OrderBy(
                                dimension=gp.OrderBy.DimensionOrderBy(dimension_name="country")
                            )
                        ],
                    ),
                    gp.Pivot(
                        field_names=["browser"],
                        offset=0,
                        limit=5,
                        order_bys=[
                            gp.OrderBy(
                                metric=gp.OrderBy.MetricOrderBy(metric_name="sessions"), desc=True
                            )
                        ],
                    ),
                ],
        )
    ]
)

df = gp.query(service_account, batch_pivot_request, report_type="batch_pivot")
print(df[0].head())
print(df[1].head())

Metadata

The get_metadata() function will return all metadata on dimensions and metrics within the Google Analytics 4 property.

metadata = gp.get_metadata(service_account, property_id)
print(metadata)

Current features

• Support for all current API functionality including RunReportRequest, BatchRunReportsRequest, RunPivotReportRequest, BatchRunPivotReportsRequest, RunRealtimeReportRequest, and GetMetadataRequest.
• Returns data in a Pandas dataframe, or a list of Pandas dataframes.