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# Copyright 2019 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.
import typing
if typing.TYPE_CHECKING:
from google.cloud import bigquery
def load_table_dataframe(table_id: str) -> "bigquery.Table":
# [START bigquery_load_table_dataframe]
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": "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": "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": "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": "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(
["Q24980", "Q25043", "Q24953", "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
)
)
# [END bigquery_load_table_dataframe]
return table