pandas.read_gbq

pandas.read_gbq(query, project_id=None, index_col=None, col_order=None, reauth=False, verbose=True, private_key=None, dialect='legacy', **kwargs)[source]

Load data from Google BigQuery.

The main method a user calls to execute a Query in Google BigQuery and read results into a pandas DataFrame.

This function requires the pandas-gbq package.

Authentication to the Google BigQuery service is via OAuth 2.0.

  • If “private_key” is not provided:

    By default “application default credentials” are used.

    If default application credentials are not found or are restrictive, user account credentials are used. In this case, you will be asked to grant permissions for product name ‘pandas GBQ’.

  • If “private_key” is provided:

    Service account credentials will be used to authenticate.

Parameters:

query : str

SQL-Like Query to return data values

project_id : str

Google BigQuery Account project ID.

index_col : str (optional)

Name of result column to use for index in results DataFrame

col_order : list(str) (optional)

List of BigQuery column names in the desired order for results DataFrame

reauth : boolean (default False)

Force Google BigQuery to reauthenticate the user. This is useful if multiple accounts are used.

verbose : boolean (default True)

Verbose output

private_key : str (optional)

Service account private key in JSON format. Can be file path or string contents. This is useful for remote server authentication (eg. jupyter iPython notebook on remote host)

dialect : {‘legacy’, ‘standard’}, default ‘legacy’

‘legacy’ : Use BigQuery’s legacy SQL dialect. ‘standard’ : Use BigQuery’s standard SQL, which is compliant with the SQL 2011 standard. For more information see BigQuery SQL Reference

`**kwargs` : Arbitrary keyword arguments

configuration (dict): query config parameters for job processing. For example:

configuration = {‘query’: {‘useQueryCache’: False}}

For more information see BigQuery SQL Reference

Returns:

df: DataFrame

DataFrame representing results of query

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