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52 changes: 52 additions & 0 deletions samples/snippets/performance_optimizations_test.py
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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 test_performance_optimizations() -> None:
# [START bigquery_bigframes_use_peek_to_preview_data]
import bigframes.pandas as bpd

# Read the "Penguins" table into a dataframe
df = bpd.read_gbq("bigquery-public-data.ml_datasets.penguins")

# Preview 3 random rows
df.peek(3)
# [END bigquery_bigframes_use_peek_to_preview_data]
assert df.peek(3) is not None

import bigframes.pandas as bpd

users = bpd.DataFrame({"user_name": ["John"]})
groups = bpd.DataFrame({"group_id": ["group_1"]})
transactions = bpd.DataFrame({"amount": [3], "completed": [True]})

# [START bigquery_bigframes_use_cache_after_expensive_operations]
# Assume you have 3 large dataframes "users", "group" and "transactions"

# Expensive join operations
final_df = users.join(groups).join(transactions)
final_df.cache()
# Subsequent derived results will reuse the cached join
print(final_df.peek())
print(len(final_df[final_df["completed"]]))
print(final_df.groupby("group_id")["amount"].mean().peek(30))
# [END bigquery_bigframes_use_cache_after_expensive_operations]
assert final_df is not None

# [START bigquery_bigframes_enable_deferred_repr_for_debugging]
import bigframes.pandas as bpd

bpd.options.display.repr_mode = "deferred"
# [END bigquery_bigframes_enable_deferred_repr_for_debugging]
assert bpd.options.display.repr_mode == "deferred"