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https://github.com/practical-data-science/gapandas4
GAPandas4 is a Python package for querying the Google Analytics Data API for GA4 and displaying the results in a Pandas dataframe.
https://github.com/practical-data-science/gapandas4
Last synced: about 2 months ago
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GAPandas4 is a Python package for querying the Google Analytics Data API for GA4 and displaying the results in a Pandas dataframe.
- Host: GitHub
- URL: https://github.com/practical-data-science/gapandas4
- Owner: practical-data-science
- License: mit
- Created: 2022-06-22T08:43:23.000Z (about 2 years ago)
- Default Branch: master
- Last Pushed: 2022-07-06T07:55:20.000Z (about 2 years ago)
- Last Synced: 2024-05-14T00:13:16.074Z (4 months ago)
- Language: Python
- Homepage:
- Size: 29.3 KB
- Stars: 32
- Watchers: 2
- Forks: 5
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# 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](https://practicaldatascience.co.uk/data-science/how-to-access-google-analytics-data-in-pandas-using-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](https://practicaldatascience.co.uk/data-engineering/how-to-create-a-google-service-account-client-secrets-json-key) 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
You can install GAPandas4 in two ways: via GitHub or via PyPi using the Pip Python package management system.
```commandline
pip3 install git+https://github.com/practical-data-science/gapandas4.git
pip3 install gapandas4
```
### Usage examples
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.
```python
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.
```python
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.
```python
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.
```python
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.
```python
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.