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# Copyright 2025 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
def run_udf_and_read_gbq_function(
project_id: str, dataset_id: str, routine_id: str
) -> None:
your_gcp_project_id = project_id
your_bq_dataset_id = dataset_id
your_bq_routine_id = routine_id
# [START bigquery_dataframes_udf]
import bigframes.pandas as bpd
# Set BigQuery DataFrames options
bpd.options.bigquery.project = your_gcp_project_id
bpd.options.bigquery.location = "US"
# BigQuery DataFrames gives you the ability to turn your custom functions
# into a BigQuery Python UDF. One can find more details about the usage and
# the requirements via `help` command.
help(bpd.udf)
# Read a table and inspect the column of interest.
df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
df["body_mass_g"].peek(10)
# Define a custom function, and specify the intent to turn it into a
# BigQuery Python UDF. Let's try a `pandas`-like use case in which we want
# to apply a user defined function to every value in a `Series`, more
# specifically bucketize the `body_mass_g` value of the penguins, which is a
# real number, into a category, which is a string.
@bpd.udf(
dataset=your_bq_dataset_id,
name=your_bq_routine_id,
)
def get_bucket(num: float) -> str:
if not num:
return "NA"
boundary = 4000
return "at_or_above_4000" if num >= boundary else "below_4000"
# Then we can apply the udf on the `Series` of interest via
# `apply` API and store the result in a new column in the DataFrame.
df = df.assign(body_mass_bucket=df["body_mass_g"].apply(get_bucket))
# This will add a new column `body_mass_bucket` in the DataFrame. You can
# preview the original value and the bucketized value side by side.
df[["body_mass_g", "body_mass_bucket"]].peek(10)
# The above operation was possible by doing all the computation on the
# cloud through an underlying BigQuery Python UDF that was created to
# support the user's operations in the Python code.
# The BigQuery Python UDF created to support the BigQuery DataFrames
# udf can be located via a property `bigframes_bigquery_function`
# set in the udf object.
print(f"Created BQ Python UDF: {get_bucket.bigframes_bigquery_function}")
# If you have already defined a custom function in BigQuery, either via the
# BigQuery Google Cloud Console or with the `udf` decorator,
# or otherwise, you may use it with BigQuery DataFrames with the
# `read_gbq_function` method. More details are available via the `help`
# command.
help(bpd.read_gbq_function)
existing_get_bucket_bq_udf = get_bucket.bigframes_bigquery_function
# Here is an example of using `read_gbq_function` to load an existing
# BigQuery Python UDF.
df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")
get_bucket_function = bpd.read_gbq_function(existing_get_bucket_bq_udf)
df = df.assign(body_mass_bucket=df["body_mass_g"].apply(get_bucket_function))
df.peek(10)
# Let's continue trying other potential use cases of udf. Let's say we
# consider the `species`, `island` and `sex` of the penguins sensitive
# information and want to redact that by replacing with their hash code
# instead. Let's define another scalar custom function and decorate it
# as a udf. The custom function in this example has external package
# dependency, which can be specified via `packages` parameter.
@bpd.udf(
dataset=your_bq_dataset_id,
name=your_bq_routine_id,
packages=["cryptography"],
)
def get_hash(input: str) -> str:
from cryptography.fernet import Fernet
# handle missing value
if input is None:
input = ""
key = Fernet.generate_key()
f = Fernet(key)
return f.encrypt(input.encode()).decode()
# We can use this udf in another `pandas`-like API `map` that
# can be applied on a DataFrame
df_redacted = df[["species", "island", "sex"]].map(get_hash)
df_redacted.peek(10)
# If the BigQuery routine is no longer needed, we can clean it up
# to free up any cloud quota
session = bpd.get_global_session()
session.bqclient.delete_routine(f"{your_bq_dataset_id}.{your_bq_routine_id}")
# [END bigquery_dataframes_udf]