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| 1 | +# Copyright 2026 Google LLC |
| 2 | +# |
| 3 | +# Licensed under the Apache License, Version 2.0 (the "License"); |
| 4 | +# you may not use this file except in compliance with the License. |
| 5 | +# You may obtain a copy of the License at |
| 6 | +# |
| 7 | +# http://www.apache.org/licenses/LICENSE-2.0 |
| 8 | +# |
| 9 | +# Unless required by applicable law or agreed to in writing, software |
| 10 | +# distributed under the License is distributed on an "AS IS" BASIS, |
| 11 | +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 12 | +# See the License for the specific language governing permissions and |
| 13 | +# limitations under the License. |
| 14 | + |
| 15 | +# [START bigquery_load_table_dataframe] |
| 16 | +import datetime |
| 17 | + |
| 18 | +import pandas as pd |
| 19 | +import pytz |
| 20 | + |
| 21 | +import bigframes.pandas as bpd |
| 22 | + |
| 23 | +# Set partial ordering mode as the default configuration for BigQuery |
| 24 | +# DataFrames. |
| 25 | +bpd.options.bigquery.ordering_mode = "partial" |
| 26 | + |
| 27 | + |
| 28 | +def load_table_dataframe( |
| 29 | + table_id: str = "your-project.your_dataset.your_table_name", |
| 30 | +) -> bpd.DataFrame: |
| 31 | + # TODO(developer): Set table_id to the ID of the table to create. |
| 32 | + # table_id = "your-project.your_dataset.your_table_name" |
| 33 | + |
| 34 | + records = [ |
| 35 | + { |
| 36 | + "title": "The Meaning of Life", |
| 37 | + "release_year": 1983, |
| 38 | + "length_minutes": 112.5, |
| 39 | + "release_date": pytz.timezone("Europe/Paris") |
| 40 | + .localize(datetime.datetime(1983, 5, 9, 13, 0, 0)) |
| 41 | + .astimezone(pytz.utc), |
| 42 | + # Assume UTC timezone when a datetime object contains no timezone. |
| 43 | + "dvd_release": datetime.datetime(2002, 1, 22, 7, 0, 0), |
| 44 | + }, |
| 45 | + { |
| 46 | + "title": "Monty Python and the Holy Grail", |
| 47 | + "release_year": 1975, |
| 48 | + "length_minutes": 91.5, |
| 49 | + "release_date": pytz.timezone("Europe/London") |
| 50 | + .localize(datetime.datetime(1975, 4, 9, 23, 59, 2)) |
| 51 | + .astimezone(pytz.utc), |
| 52 | + "dvd_release": datetime.datetime(2002, 7, 16, 9, 0, 0), |
| 53 | + }, |
| 54 | + { |
| 55 | + "title": "Life of Brian", |
| 56 | + "release_year": 1979, |
| 57 | + "length_minutes": 94.25, |
| 58 | + "release_date": pytz.timezone("America/New_York") |
| 59 | + .localize(datetime.datetime(1979, 8, 17, 23, 59, 5)) |
| 60 | + .astimezone(pytz.utc), |
| 61 | + "dvd_release": datetime.datetime(2008, 1, 14, 8, 0, 0), |
| 62 | + }, |
| 63 | + { |
| 64 | + "title": "And Now for Something Completely Different", |
| 65 | + "release_year": 1971, |
| 66 | + "length_minutes": 88.0, |
| 67 | + "release_date": pytz.timezone("Europe/London") |
| 68 | + .localize(datetime.datetime(1971, 9, 28, 23, 59, 7)) |
| 69 | + .astimezone(pytz.utc), |
| 70 | + "dvd_release": datetime.datetime(2003, 10, 22, 10, 0, 0), |
| 71 | + }, |
| 72 | + ] |
| 73 | + dataframe = pd.DataFrame( |
| 74 | + records, |
| 75 | + # In the loaded table, the column order reflects the order of the |
| 76 | + # columns in the DataFrame. |
| 77 | + columns=[ |
| 78 | + "title", |
| 79 | + "release_year", |
| 80 | + "length_minutes", |
| 81 | + "release_date", |
| 82 | + "dvd_release", |
| 83 | + ], |
| 84 | + # Optionally, set a named index, which can also be written to the |
| 85 | + # BigQuery table. |
| 86 | + index=pd.Index( |
| 87 | + ["Q24980", "Q25043", "Q24953", "Q16403"], name="wikidata_id" |
| 88 | + ), |
| 89 | + ) |
| 90 | + |
| 91 | + # Convert the local pandas DataFrame into a BigQuery DataFrame. |
| 92 | + bq_df = bpd.read_pandas(dataframe) |
| 93 | + |
| 94 | + # Write the DataFrame to a BigQuery table. |
| 95 | + bq_df.to_gbq(table_id, if_exists="replace", index=True) |
| 96 | + return bq_df |
| 97 | +# [END bigquery_load_table_dataframe] |
| 98 | + |
| 99 | + |
| 100 | +if __name__ == "__main__": |
| 101 | + import os |
| 102 | + |
| 103 | + table_id = os.environ.get( |
| 104 | + "TABLE_ID", "your-project.your_dataset.your_table_name" |
| 105 | + ) |
| 106 | + print(load_table_dataframe(table_id=table_id)) |
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