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 ) )
