Package pandas (0.15.0)

Version 0.15.0

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API documentation for pandas package.

Classes

NamedAgg

NamedAgg(column, aggfunc)

option_context

Context manager to temporarily set options in the with statement context.

You need to invoke as option_context(pat, val, [(pat, val), ...]).

.. rubric:: Examples

import bigframes with bigframes.option_context('display.max_rows', 10, 'display.max_columns', 5): ... pass

Packages Functions

concat

Concatenate BigQuery DataFrames objects along a particular axis.

Allows optional set logic along the other axes.

Can also add a layer of hierarchical indexing on the concatenation axis, which may be useful if the labels are the same (or overlapping) on the passed axis number.

Parameters
Name	Description
axis	The axis to concatenate along.
join	How to handle indexes on other axis (or axes).
ignore_index	If True, do not use the index values along the concatenation axis. The resulting axis will be labeled 0, ..., n - 1. This is useful if you are concatenating objects where the concatenation axis does not have meaningful indexing information. Note the index values on the other axes are still respected in the join.

cut

cut(
    x: bigframes.series.Series, bins: int, *, labels: typing.Optional[bool] = None
) -> bigframes.series.Series

Bin values into discrete intervals.

Use cut when you need to segment and sort data values into bins. This function is also useful for going from a continuous variable to a categorical variable. For example, cut could convert ages to groups of age ranges. Supports binning into an equal number of bins, or a pre-specified array of bins.

labels=False implies you just want the bins back.

Examples:

import bigframes.pandas as pd

pd.options.display.progress_bar = None
s = pd.Series([0, 1, 1, 2])
pd.cut(s, bins=4, labels=False)

0    0
1    1
2    1
3    3
dtype: Int64

Parameters
Name	Description
x	The input Series to be binned. Must be 1-dimensional.
bins	The criteria to bin by. int : Defines the number of equal-width bins in the range of x. The range of x is extended by .1% on each side to include the minimum and maximum values of x.
labels	Specifies the labels for the returned bins. Must be the same length as the resulting bins. If False, returns only integer indicators of the bins. This affects the type of the output container (see below). If True, raises an error. When ordered=False, labels must be provided.

get_dummies

get_dummies(
    data: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    prefix: typing.Optional[typing.Union[typing.List, dict, str]] = None,
    prefix_sep: typing.Optional[typing.Union[typing.List, dict, str]] = "_",
    dummy_na: bool = False,
    columns: typing.Optional[typing.List] = None,
    drop_first: bool = False,
    dtype: typing.Optional[typing.Any] = None,
) -> bigframes.dataframe.DataFrame

Convert categorical variable into dummy/indicator variables.

Each variable is converted in as many 0/1 variables as there are different values. Columns in the output are each named after a value; if the input is a DataFrame, the name of the original variable is prepended to the value.

Examples:

import bigframes.pandas as pd pd.options.display.progress_bar = None s = pd.Series(list('abca')) pd.get_dummies(s) a b c 0 True False False 1 False True False 2 False False True 3 True False False

s1 = pd.Series(['a', 'b', None]) pd.get_dummies(s1) a b 0 True False 1 False True 2 False False

pd.get_dummies(s1, dummy_na=True) a b

df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'], 'C': [1, 2, 3]}) pd.get_dummies(df, prefix=['col1', 'col2']) C col1_a col1_b col2_a col2_b col2_c 0 1 True False False True False 1 2 False True True False False 2 3 True False False False True

pd.get_dummies(pd.Series(list('abcaa'))) a b c 0 True False False 1 False True False 2 False False True 3 True False False 4 True False False

pd.get_dummies(pd.Series(list('abcaa')), drop_first=True) b c 0 False False 1 True False 2 False True 3 False False 4 False False

Parameters
Name	Description
data	Data of which to get dummy indicators.
prefix	String to append DataFrame column names. Pass a list with length equal to the number of columns when calling get_dummies on a DataFrame. Alternatively, prefix can be a dictionary mapping column names to prefixes.
prefix_sep	Separator/delimiter to use, appended to prefix. Or pass a list or dictionary as with prefix.
dummy_na	Add a column to indicate NaNs, if False NaNs are ignored.
columns	Column names in the DataFrame to be encoded. If columns is None then only the columns with string dtype will be converted.
drop_first	Whether to get k-1 dummies out of k categorical levels by removing the first level.
dtype	Data type for new columns. Only a single dtype is allowed.

merge

merge(
    left: bigframes.dataframe.DataFrame,
    right: bigframes.dataframe.DataFrame,
    how: typing.Literal["inner", "left", "outer", "right", "cross"] = "inner",
    on: typing.Optional[str] = None,
    *,
    left_on: typing.Optional[str] = None,
    right_on: typing.Optional[str] = None,
    sort: bool = False,
    suffixes: tuple[str, str] = ("_x", "_y")
) -> bigframes.dataframe.DataFrame

Merge DataFrame objects with a database-style join.

The join is done on columns or indexes. If joining columns on columns, the DataFrame indexes will be ignored. Otherwise if joining indexes on indexes or indexes on a column or columns, the index will be passed on. When performing a cross merge, no column specifications to merge on are allowed.

Note: A named Series object is treated as a DataFrame with a single named column.

Warning: If both key columns contain rows where the key is a null value, those rows will be matched against each other. This is different from usual SQL join behaviour and can lead to unexpected results.

Parameters
Name	Description
on	Columns to join on. It must be found in both DataFrames. Either on or left_on + right_on must be passed in.
left_on	Columns to join on in the left DataFrame. Either on or left_on + right_on must be passed in.
right_on	Columns to join on in the right DataFrame. Either on or left_on + right_on must be passed in.

qcut

qcut(
    x: bigframes.series.Series,
    q: int,
    *,
    labels: typing.Optional[bool] = None,
    duplicates: typing.Literal["drop", "error"] = "error"
) -> bigframes.series.Series

Quantile-based discretization function.

Discretize variable into equal-sized buckets based on rank or based on sample quantiles. For example 1000 values for 10 quantiles would produce a Categorical object indicating quantile membership for each data point.

Parameters
Name	Description
x	The input Series to be binned. Must be 1-dimensional.
q	Number of quantiles. 10 for deciles, 4 for quartiles, etc. Alternately array of quantiles, e.g. [0, .25, .5, .75, 1.] for quartiles.
labels	Used as labels for the resulting bins. Must be of the same length as the resulting bins. If False, return only integer indicators of the bins. If True, raises an error.
duplicates	If bin edges are not unique, raise ValueError or drop non-uniques.

read_csv

read_csv(
    filepath_or_buffer: typing.Union[str, typing.IO[bytes]],
    *,
    sep: typing.Optional[str] = ",",
    header: typing.Optional[int] = 0,
    names: typing.Optional[
        typing.Union[
            typing.MutableSequence[typing.Any],
            numpy.ndarray[typing.Any, typing.Any],
            typing.Tuple[typing.Any, ...],
            range,
        ]
    ] = None,
    index_col: typing.Optional[
        typing.Union[
            int, str, typing.Sequence[typing.Union[str, int]], typing.Literal[False]
        ]
    ] = None,
    usecols: typing.Optional[
        typing.Union[
            typing.MutableSequence[str],
            typing.Tuple[str, ...],
            typing.Sequence[int],
            pandas.core.series.Series,
            pandas.core.indexes.base.Index,
            numpy.ndarray[typing.Any, typing.Any],
            typing.Callable[[typing.Any], bool],
        ]
    ] = None,
    dtype: typing.Optional[typing.Dict] = None,
    engine: typing.Optional[
        typing.Literal["c", "python", "pyarrow", "python-fwf", "bigquery"]
    ] = None,
    encoding: typing.Optional[str] = None,
    **kwargs
) -> bigframes.dataframe.DataFrame

Loads DataFrame from comma-separated values (csv) file locally or from Cloud Storage.

The CSV file data will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed.

Note: using engine="bigquery" will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.csv"
>>> df = bpd.read_csv(filepath_or_buffer=gcs_path)
>>> df.head(2)
      name post_abbr
0  Alabama        AL
1   Alaska        AK
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
filepath_or_buffer	A local or Google Cloud Storage (gs://) path with engine="bigquery" otherwise passed to pandas.read_csv.
sep	the separator for fields in a CSV file. For the BigQuery engine, the separator can be any ISO-8859-1 single-byte character. To use a character in the range 128-255, you must encode the character as UTF-8. Both engines support sep=" " to specify tab character as separator. Default engine supports having any number of spaces as separator by specifying sep="\s+". Separators longer than 1 character are interpreted as regular expressions by the default engine. BigQuery engine only supports single character separators.
header	row number to use as the column names. - None: Instructs autodetect that there are no headers and data should be read starting from the first row. - 0: If using engine="bigquery", Autodetect tries to detect headers in the first row. If they are not detected, the row is read as data. Otherwise data is read starting from the second row. When using default engine, pandas assumes the first row contains column names unless the names argument is specified. If names is provided, then the first row is ignored, second row is read as data, and column names are inferred from names. - N > 0: If using engine="bigquery", Autodetect skips N rows and tries to detect headers in row N+1. If headers are not detected, row N+1 is just skipped. Otherwise row N+1 is used to extract column names for the detected schema. When using default engine, pandas will skip N rows and assumes row N+1 contains column names unless the names argument is specified. If names is provided, row N+1 will be ignored, row N+2 will be read as data, and column names are inferred from names.
names	a list of column names to use. If the file contains a header row and you want to pass this parameter, then header=0 should be passed as well so the first (header) row is ignored. Only to be used with default engine.
index_col	column(s) to use as the row labels of the DataFrame, either given as string name or column index. index_col=False can be used with the default engine only to enforce that the first column is not used as the index. Using column index instead of column name is only supported with the default engine. The BigQuery engine only supports having a single column name as the index_col. Neither engine supports having a multi-column index.
usecols	List of column names to use): The BigQuery engine only supports having a list of string column names. Column indices and callable functions are only supported with the default engine. Using the default engine, the column names in usecols can be defined to correspond to column names provided with the names parameter (ignoring the document's header row of column names). The order of the column indices/names in usecols is ignored with the default engine. The order of the column names provided with the BigQuery engine will be consistent in the resulting dataframe. If using a callable function with the default engine, only column names that evaluate to True by the callable function will be in the resulting dataframe.
dtype	Data type for data or columns. Only to be used with default engine.
engine	Type of engine to use. If engine="bigquery" is specified, then BigQuery's load API will be used. Otherwise, the engine will be passed to pandas.read_csv.
encoding	encoding the character encoding of the data. The default encoding is UTF-8 for both engines. The default engine acceps a wide range of encodings. Refer to Python documentation for a comprehensive list, https://docs.python.org/3/library/codecs.html#standard-encodings The BigQuery engine only supports UTF-8 and ISO-8859-1.

read_gbq

read_gbq(
    query_or_table: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    col_order: typing.Iterable[str] = (),
    max_results: typing.Optional[int] = None,
    use_cache: bool = True
) -> bigframes.dataframe.DataFrame

Loads a DataFrame from BigQuery.

BigQuery tables are an unordered, unindexed data source. By default, the DataFrame will have an arbitrary index and ordering.

Set the index_col argument to one or more columns to choose an index. The resulting DataFrame is sorted by the index columns. For the best performance, ensure the index columns don't contain duplicate values.

Note: By default, even SQL query inputs with an ORDER BY clause create a DataFrame with an arbitrary ordering. Use row_number() OVER (ORDER BY ...) AS rowindex in your SQL query and set index_col='rowindex' to preserve the desired ordering.

If your query doesn't have an ordering, select GENERATE_UUID() AS rowindex in your SQL and set index_col='rowindex' for the best performance.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

If the input is a table ID:

>>> df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")

Preserve ordering in a query input.

>>> df = bpd.read_gbq('''
...    SELECT
...       -- Instead of an ORDER BY clause on the query, use
...       -- ROW_NUMBER() to create an ordered DataFrame.
...       ROW_NUMBER() OVER (ORDER BY AVG(pitchSpeed) DESC)
...         AS rowindex,
...
...       pitcherFirstName,
...       pitcherLastName,
...       AVG(pitchSpeed) AS averagePitchSpeed
...     FROM `bigquery-public-data.baseball.games_wide`
...     WHERE year = 2016
...     GROUP BY pitcherFirstName, pitcherLastName
... ''', index_col="rowindex")
>>> df.head(2)
         pitcherFirstName pitcherLastName  averagePitchSpeed
rowindex
1                Albertin         Chapman          96.514113
2                 Zachary         Britton          94.591039
<BLANKLINE>
[2 rows x 3 columns]

Parameters
Name	Description
query_or_table	A SQL string to be executed or a BigQuery table to be read. The table must be specified in the format of project.dataset.tablename or dataset.tablename.
index_col	Name of result column(s) to use for index in results DataFrame.
col_order	List of BigQuery column names in the desired order for results DataFrame.
max_results	If set, limit the maximum number of rows to fetch from the query results.
use_cache	Whether to cache the query inputs. Default to True.

read_gbq_function

read_gbq_function(function_name: str)

Loads a BigQuery function from BigQuery.

Then it can be applied to a DataFrame or Series.

Note: The return type of the function must be explicitly specified in the function's original definition even if not otherwise required.

BigQuery Utils provides many public functions under the bqutil project on Google Cloud Platform project (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs#using-the-udfs). You can checkout Community UDFs to use community-contributed functions. (See: https://github.com/GoogleCloudPlatform/bigquery-utils/tree/master/udfs/community#community-udfs).

Examples:

Use the cw_lower_case_ascii_only function from Community UDFs. (https://github.com/GoogleCloudPlatform/bigquery-utils/blob/master/udfs/community/cw_lower_case_ascii_only.sqlx)

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> df = bpd.DataFrame({'id': [1, 2, 3], 'name': ['AURÉLIE', 'CÉLESTINE', 'DAPHNÉ']})
>>> df
   id       name
0   1    AURÉLIE
1   2  CÉLESTINE
2   3     DAPHNÉ
<BLANKLINE>
[3 rows x 2 columns]

>>> func = bpd.read_gbq_function("bqutil.fn.cw_lower_case_ascii_only")
>>> df1 = df.assign(new_name=df['name'].apply(func))
>>> df1
   id       name   new_name
0   1    AURÉLIE    aurÉlie
1   2  CÉLESTINE  cÉlestine
2   3     DAPHNÉ     daphnÉ
<BLANKLINE>
[3 rows x 3 columns]

Parameter
Name	Description
function_name	the function's name in BigQuery in the format project_id.dataset_id.function_name, or dataset_id.function_name to load from the default project, or function_name to load from the default project and the dataset associated with the current session.

read_gbq_model

read_gbq_model(model_name: str)

Loads a BigQuery ML model from BigQuery.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Read an existing BigQuery ML model.

>>> model_name = "bigframes-dev.bqml_tutorial.penguins_model"
>>> model = bpd.read_gbq_model(model_name)

Parameter
Name	Description
model_name	the model's name in BigQuery in the format project_id.dataset_id.model_id, or just dataset_id.model_id to load from the default project.

read_gbq_query

read_gbq_query(
    query: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    col_order: typing.Iterable[str] = (),
    max_results: typing.Optional[int] = None,
    use_cache: bool = True
) -> bigframes.dataframe.DataFrame

Turn a SQL query into a DataFrame.

Note: Because the results are written to a temporary table, ordering by ORDER BY is not preserved. A unique index_col is recommended. Use row_number() over () if there is no natural unique index or you want to preserve ordering.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Simple query input:

>>> df = bpd.read_gbq_query('''
...    SELECT
...       pitcherFirstName,
...       pitcherLastName,
...       pitchSpeed,
...    FROM `bigquery-public-data.baseball.games_wide`
... ''')

Preserve ordering in a query input.

>>> df = bpd.read_gbq_query('''
...    SELECT
...       -- Instead of an ORDER BY clause on the query, use
...       -- ROW_NUMBER() to create an ordered DataFrame.
...       ROW_NUMBER() OVER (ORDER BY AVG(pitchSpeed) DESC)
...         AS rowindex,
...
...       pitcherFirstName,
...       pitcherLastName,
...       AVG(pitchSpeed) AS averagePitchSpeed
...     FROM `bigquery-public-data.baseball.games_wide`
...     WHERE year = 2016
...     GROUP BY pitcherFirstName, pitcherLastName
... ''', index_col="rowindex")
>>> df.head(2)
         pitcherFirstName pitcherLastName  averagePitchSpeed
rowindex
1                Albertin         Chapman          96.514113
2                 Zachary         Britton          94.591039
<BLANKLINE>
[2 rows x 3 columns]

See also: Session.read_gbq.

read_gbq_table

read_gbq_table(
    query: str,
    *,
    index_col: typing.Union[typing.Iterable[str], str] = (),
    col_order: typing.Iterable[str] = (),
    max_results: typing.Optional[int] = None,
    use_cache: bool = True
) -> bigframes.dataframe.DataFrame

Turn a BigQuery table into a DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

Read a whole table, with arbitrary ordering or ordering corresponding to the primary key(s).

>>> df = bpd.read_gbq_table("bigquery-public-data.ml_datasets.penguins")

See also: Session.read_gbq.

read_json

read_json(
    path_or_buf: typing.Union[str, typing.IO[bytes]],
    *,
    orient: typing.Literal[
        "split", "records", "index", "columns", "values", "table"
    ] = "columns",
    dtype: typing.Optional[typing.Dict] = None,
    encoding: typing.Optional[str] = None,
    lines: bool = False,
    engine: typing.Literal["ujson", "pyarrow", "bigquery"] = "ujson",
    **kwargs
) -> bigframes.dataframe.DataFrame

Convert a JSON string to DataFrame object.

Note: using engine="bigquery" will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://bigframes-dev-testing/sample1.json"
>>> df = bpd.read_json(path_or_buf=gcs_path, lines=True, orient="records")
>>> df.head(2)
   id   name
0   1  Alice
1   2    Bob
<BLANKLINE>
[2 rows x 2 columns]

Parameters
Name	Description
path_or_buf	A local or Google Cloud Storage (gs://) path with engine="bigquery" otherwise passed to pandas.read_json.
orient	If engine="bigquery" orient only supports "records". Indication of expected JSON string format. Compatible JSON strings can be produced by to_json() with a corresponding orient value. The set of possible orients is: - 'split' : dict like {{index -> [index], columns -> [columns], data -> [values]}} - 'records' : list like [{{column -> value}}, ... , {{column -> value}}] - 'index' : dict like {{index -> {{column -> value}}}} - 'columns' : dict like {{column -> {{index -> value}}}} - 'values' : just the values array
dtype	If True, infer dtypes; if a dict of column to dtype, then use those; if False, then don't infer dtypes at all, applies only to the data. For all orient values except 'table', default is True.
encoding	The encoding to use to decode py3 bytes.
lines	Read the file as a json object per line. If using engine="bigquery" lines only supports True.
engine	Type of engine to use. If engine="bigquery" is specified, then BigQuery's load API will be used. Otherwise, the engine will be passed to pandas.read_json.

read_pandas

read_pandas(
    pandas_dataframe: pandas.core.frame.DataFrame,
) -> bigframes.dataframe.DataFrame

Loads DataFrame from a pandas DataFrame.

The pandas DataFrame will be persisted as a temporary BigQuery table, which can be automatically recycled after the Session is closed.

Examples:

>>> import bigframes.pandas as bpd
>>> import pandas as pd
>>> bpd.options.display.progress_bar = None

>>> d = {'col1': [1, 2], 'col2': [3, 4]}
>>> pandas_df = pd.DataFrame(data=d)
>>> df = bpd.read_pandas(pandas_df)
>>> df
   col1  col2
0     1     3
1     2     4
<BLANKLINE>
[2 rows x 2 columns]

Parameter
Name	Description
pandas_dataframe	a pandas DataFrame object to be loaded.

read_parquet

read_parquet(
    path: typing.Union[str, typing.IO[bytes]]
) -> bigframes.dataframe.DataFrame

Load a Parquet object from the file path (local or Cloud Storage), returning a DataFrame.

Note: This method will not guarantee the same ordering as the file. Instead, set a serialized index column as the index and sort by that in the resulting DataFrame.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://cloud-samples-data/bigquery/us-states/us-states.parquet"
>>> df = bpd.read_parquet(path=gcs_path)

Parameter
Name	Description
path	Local or Cloud Storage path to Parquet file.

read_pickle

read_pickle(
    filepath_or_buffer: FilePath | ReadPickleBuffer,
    compression: CompressionOptions = "infer",
    storage_options: StorageOptions = None,
)

Load pickled BigFrames object (or any object) from file.

Note: If the content of the pickle file is a Series and its name attribute is None, the name will be set to '0' by default.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None

>>> gcs_path = "gs://bigframes-dev-testing/test_pickle.pkl"
>>> df = bpd.read_pickle(filepath_or_buffer=gcs_path)

Parameters
Name	Description
filepath_or_buffer	String, path object (implementing os.PathLike[str]), or file-like object implementing a binary readlines() function. Also accepts URL. URL is not limited to S3 and GCS.
compression	For on-the-fly decompression of on-disk data. If 'infer' and 'filepath_or_buffer' is path-like, then detect compression from the following extensions: '.gz', '.bz2', '.zip', '.xz', '.zst', '.tar', '.tar.gz', '.tar.xz' or '.tar.bz2' (otherwise no compression). If using 'zip' or 'tar', the ZIP file must contain only one data file to be read in. Set to None for no decompression. Can also be a dict with key 'method' set to one of {'zip', 'gzip', 'bz2', 'zstd', 'tar'} and other key-value pairs are forwarded to zipfile.ZipFile, gzip.GzipFile, bz2.BZ2File, zstandard.ZstdDecompressor or tarfile.TarFile, respectively. As an example, the following could be passed for Zstandard decompression using a custom compression dictionary compression={'method': 'zstd', 'dict_data': my_compression_dict}.
storage_options	Extra options that make sense for a particular storage connection, e.g. host, port, username, password, etc. For HTTP(S) URLs the key-value pairs are forwarded to urllib.request.Request as header options. For other URLs (e.g. starting with “s3://”, and “gcs://”) the key-value pairs are forwarded to fsspec.open. Please see fsspec and urllib for more details, and for more examples on storage options refer here.

remote_function

remote_function(
    input_types: typing.List[type],
    output_type: type,
    dataset: typing.Optional[str] = None,
    bigquery_connection: typing.Optional[str] = None,
    reuse: bool = True,
    name: typing.Optional[str] = None,
    packages: typing.Optional[typing.Sequence[str]] = None,
)

Decorator to turn a user defined function into a BigQuery remote function. Check out the code samples at: https://cloud.google.com/bigquery/docs/remote-functions#bigquery-dataframes.

Note: Please make sure following is setup before using this API:

1. Have the below APIs enabled for your project:

   • BigQuery Connection API
   • Cloud Functions API
   • Cloud Run API
   • Cloud Build API
   • Artifact Registry API
   • Cloud Resource Manager API

   This can be done from the cloud console (change PROJECT_ID to yours): https://console.cloud.google.com/apis/enableflow?apiid=bigqueryconnection.googleapis.com,cloudfunctions.googleapis.com,run.googleapis.com,cloudbuild.googleapis.com,artifactregistry.googleapis.com,cloudresourcemanager.googleapis.com&project=PROJECT_ID

   Or from the gcloud CLI:

   $ gcloud services enable bigqueryconnection.googleapis.com cloudfunctions.googleapis.com run.googleapis.com cloudbuild.googleapis.com artifactregistry.googleapis.com cloudresourcemanager.googleapis.com

2. Have following IAM roles enabled for you:

   • BigQuery Data Editor (roles/bigquery.dataEditor)
   • BigQuery Connection Admin (roles/bigquery.connectionAdmin)
   • Cloud Functions Developer (roles/cloudfunctions.developer)
   • Service Account User (roles/iam.serviceAccountUser) on the service account [e-mail]
   • Storage Object Viewer (roles/storage.objectViewer)
   • Project IAM Admin (roles/resourcemanager.projectIamAdmin) (Only required if the bigquery connection being used is not pre-created and is created dynamically with user credentials.)

3. Either the user has setIamPolicy privilege on the project, or a BigQuery connection is pre-created with necessary IAM role set:

   1. To create a connection, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#create_a_connection

   2. To set up IAM, follow https://cloud.google.com/bigquery/docs/reference/standard-sql/remote-functions#grant_permission_on_function

      Alternatively, the IAM could also be setup via the gcloud CLI:

      $ gcloud projects add-iam-policy-binding PROJECT_ID --member="serviceAccount:CONNECTION_SERVICE_ACCOUNT_ID" --role="roles/run.invoker".

Parameters
Name	Description
input_types	List of input data types in the user defined function.
output_type	Data type of the output in the user defined function.
dataset	Dataset in which to create a BigQuery remote function. It should be in <project_id>.<dataset_name> or <dataset_name> format. If this parameter is not provided then session dataset id is used.
bigquery_connection	Name of the BigQuery connection. You should either have the connection already created in the location you have chosen, or you should have the Project IAM Admin role to enable the service to create the connection for you if you need it. If this parameter is not provided then the BigQuery connection from the session is used.
reuse	Reuse the remote function if already exists. True by default, which will result in reusing an existing remote function and corresponding cloud function (if any) that was previously created for the same udf. Setting it to False would force creating a unique remote function. If the required remote function does not exist then it would be created irrespective of this param.
name	Explicit name of the persisted BigQuery remote function. Use it with caution, because two users working in the same project and dataset could overwrite each other's remote functions if they use the same persistent name.
packages	Explicit name of the external package dependencies. Each dependency is added to the requirements.txt as is, and can be of the form supported in https://pip.pypa.io/en/stable/reference/requirements-file-format/.