On January 1, 2020 this library will no longer support Python 2 on the latest released version. Previously released library versions will continue to be available. For more information please visit Python 2 support on Google Cloud.

Using BigQuery with Pandas

Retrieve BigQuery data as a Pandas DataFrame

As of version 0.29.0, you can use the to_dataframe() function to retrieve query results or table rows as a pandas.DataFrame.

First, ensure that the pandas library is installed by running:

pip install --upgrade pandas

Alternatively, you can install the BigQuery python client library with pandas by running:

pip install --upgrade google-cloud-bigquery[pandas]

To retrieve query results as a pandas.DataFrame:

# from google.cloud import bigquery
# client = bigquery.Client()

sql = """
    SELECT name, SUM(number) as count
    FROM `bigquery-public-data.usa_names.usa_1910_current`
    GROUP BY name
    ORDER BY count DESC
    LIMIT 10
"""

df = client.query(sql).to_dataframe()

To retrieve table rows as a pandas.DataFrame:

# from google.cloud import bigquery
# client = bigquery.Client()
project = "bigquery-public-data"
dataset_id = "samples"

dataset_ref = bigquery.DatasetReference(project, dataset_id)
table_ref = dataset_ref.table("shakespeare")
table = client.get_table(table_ref)

df = client.list_rows(table).to_dataframe()

Load a Pandas DataFrame to a BigQuery Table

As of version 1.3.0, you can use the load_table_from_dataframe() function to load data from a pandas.DataFrame to a Table. To use this function, in addition to pandas, you will need to install the pyarrow library. You can install the BigQuery python client library with pandas and pyarrow by running:

pip install --upgrade google-cloud-bigquery[pandas,pyarrow]

The following example demonstrates how to create a pandas.DataFrame and load it into a new table:

import datetime

from google.cloud import bigquery
import pandas
import pytz

# Construct a BigQuery client object.
client = bigquery.Client()

# TODO(developer): Set table_id to the ID of the table to create.
# table_id = "your-project.your_dataset.your_table_name"

records = [
    {
        "title": u"The Meaning of Life",
        "release_year": 1983,
        "length_minutes": 112.5,
        "release_date": pytz.timezone("Europe/Paris")
        .localize(datetime.datetime(1983, 5, 9, 13, 0, 0))
        .astimezone(pytz.utc),
        # Assume UTC timezone when a datetime object contains no timezone.
        "dvd_release": datetime.datetime(2002, 1, 22, 7, 0, 0),
    },
    {
        "title": u"Monty Python and the Holy Grail",
        "release_year": 1975,
        "length_minutes": 91.5,
        "release_date": pytz.timezone("Europe/London")
        .localize(datetime.datetime(1975, 4, 9, 23, 59, 2))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2002, 7, 16, 9, 0, 0),
    },
    {
        "title": u"Life of Brian",
        "release_year": 1979,
        "length_minutes": 94.25,
        "release_date": pytz.timezone("America/New_York")
        .localize(datetime.datetime(1979, 8, 17, 23, 59, 5))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2008, 1, 14, 8, 0, 0),
    },
    {
        "title": u"And Now for Something Completely Different",
        "release_year": 1971,
        "length_minutes": 88.0,
        "release_date": pytz.timezone("Europe/London")
        .localize(datetime.datetime(1971, 9, 28, 23, 59, 7))
        .astimezone(pytz.utc),
        "dvd_release": datetime.datetime(2003, 10, 22, 10, 0, 0),
    },
]
dataframe = pandas.DataFrame(
    records,
    # In the loaded table, the column order reflects the order of the
    # columns in the DataFrame.
    columns=[
        "title",
        "release_year",
        "length_minutes",
        "release_date",
        "dvd_release",
    ],
    # Optionally, set a named index, which can also be written to the
    # BigQuery table.
    index=pandas.Index(
        [u"Q24980", u"Q25043", u"Q24953", u"Q16403"], name="wikidata_id"
    ),
)
job_config = bigquery.LoadJobConfig(
    # Specify a (partial) schema. All columns are always written to the
    # table. The schema is used to assist in data type definitions.
    schema=[
        # Specify the type of columns whose type cannot be auto-detected. For
        # example the "title" column uses pandas dtype "object", so its
        # data type is ambiguous.
        bigquery.SchemaField("title", bigquery.enums.SqlTypeNames.STRING),
        # Indexes are written if included in the schema by name.
        bigquery.SchemaField("wikidata_id", bigquery.enums.SqlTypeNames.STRING),
    ],
    # Optionally, set the write disposition. BigQuery appends loaded rows
    # to an existing table by default, but with WRITE_TRUNCATE write
    # disposition it replaces the table with the loaded data.
    write_disposition="WRITE_TRUNCATE",
)

job = client.load_table_from_dataframe(
    dataframe, table_id, job_config=job_config
)  # Make an API request.
job.result()  # Wait for the job to complete.

table = client.get_table(table_id)  # Make an API request.
print(
    "Loaded {} rows and {} columns to {}".format(
        table.num_rows, len(table.schema), table_id
    )
)