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June 12, 2024 21:44
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Carregando Dataframes no BigQuery
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| from google.colab import userdata | |
| from google.oauth2 import service_account | |
| from google.cloud import bigquery | |
| import json | |
| import pandas as pd | |
| import pyarrow as pa | |
| # Configura o client do BigQuery | |
| # precisa ter o secret configurado no Colab, chave 'svc_account' | |
| credentials = service_account.Credentials.from_service_account_info( | |
| info=json.loads(userdata.get('svc_account'), strict=False), scopes=["https://www.googleapis.com/auth/cloud-platform"] | |
| ) | |
| client = bigquery.Client(credentials=credentials) | |
| # Substitui valores None ou NaN por valores padrão | |
| def replace_none_with_default(df): | |
| for col in df.columns: | |
| if df[col].dtype == 'int64': | |
| df[col] = df[col].fillna(0) | |
| elif df[col].dtype == 'float64': | |
| df[col] = df[col].fillna(0.0) | |
| elif df[col].dtype == 'object': | |
| df[col] = df[col].fillna('') | |
| return df | |
| # Converte colunas de data para o formato datetime | |
| def convert_date_columns(df, date_columns): | |
| for date_col in date_columns: | |
| if date_col in df.columns: | |
| df[date_col] = pd.to_datetime(df[date_col], errors='coerce') | |
| return df | |
| def get_date_columns(bq_schema): | |
| return [field.name for field in bq_schema if field.field_type in ['DATE', 'DATETIME', 'TIME', 'TIMESTAMP']] | |
| # Function to convert BigQuery SchemaField to pyarrow field | |
| def bq_to_pyarrow_field(bq_field): | |
| field_type = bq_field.field_type.lower() | |
| if field_type == 'string': | |
| arrow_type = pa.string() | |
| elif field_type == 'int64': | |
| arrow_type = pa.int64() | |
| elif field_type == 'integer': | |
| arrow_type = pa.int64() | |
| elif field_type == 'float': | |
| arrow_type = pa.float64() | |
| elif field_type == 'float64': | |
| arrow_type = pa.float64() | |
| elif field_type == 'bool': | |
| arrow_type = pa.bool_() | |
| elif field_type == 'boolean': | |
| arrow_type = pa.bool_() | |
| elif field_type == 'timestamp': | |
| arrow_type = pa.timestamp('us') | |
| elif field_type == 'date': | |
| arrow_type = pa.date32() | |
| elif field_type == 'datetime': | |
| arrow_type = pa.timestamp('s') | |
| elif field_type == 'time': | |
| arrow_type = pa.time32('us') | |
| elif field_type == 'bytes': | |
| arrow_type = pa.binary() | |
| elif field_type == 'numeric': | |
| arrow_type = pa.decimal128(38, 9) | |
| else: | |
| raise ValueError(f"Unsupported field type: {field_type}") | |
| return pa.field(bq_field.name, arrow_type, nullable=bq_field.is_nullable) | |
| # Function to convert BigQuery schema to pyarrow schema | |
| def bq_schema_to_pyarrow_schema(bq_schema): | |
| pyarrow_fields = [bq_to_pyarrow_field(field) for field in bq_schema] | |
| return pa.schema(pyarrow_fields) | |
| def prepare_df(df, bq_schema): | |
| pa_schema = bq_schema_to_pyarrow_schema(bq_schema) | |
| df = convert_date_columns(df, get_date_columns(bq_schema)) | |
| return pa.Table.from_pandas(df, preserve_index=False).cast(pa_schema).to_pandas() | |
| def load_bq(df, schema, table_name, write_disposition="WRITE_EMPTY"): | |
| job_config = bigquery.LoadJobConfig( | |
| schema=schema, | |
| write_disposition=write_disposition | |
| ) | |
| job = client.load_table_from_dataframe( | |
| df, table_name, job_config=job_config | |
| ) # Make an API request. | |
| loaded_rows = job.result().output_rows | |
| table_info = client.get_table(table_name) | |
| print( | |
| "Loaded {} rows and {} columns to {}, table now has {}".format( | |
| loaded_rows, len(table_info.schema), table_name, table_info.num_rows | |
| ) | |
| ) |
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| # Exemplo de uso | |
| some_df = seu_dataframe_ja_modelado | |
| # Configurar o schema | |
| # Referência: https://cloud.google.com/bigquery/docs/reference/rest/v2/tables#TableFieldSchema.FIELDS.type | |
| schema = [ | |
| bigquery.SchemaField("ANO_MES", bigquery.enums.SqlTypeNames.STRING), | |
| bigquery.SchemaField("ID_ASSISTENCIA", bigquery.enums.SqlTypeNames.STRING) | |
| ] | |
| # df_load é o dataframe que será carregado | |
| df_load = prepare_df(some_df, schema_acionamentos) | |
| # montar table_name no formato dataset.tabela (dataset precisa já existir no BigQuery) | |
| table_name = "dataset.tabela" | |
| # para carregar a tabela sem sobrescrever (erro se existir a tabela) | |
| load_bq(df_load, schema, table_name) | |
| # para carregar a tabela apenas acrescentando registros caso a tabela já exista | |
| load_bq(df_load, schema, table_name, "WRITE_APPEND") | |
| # para carregar a tabela sobrescrevendo tudo | |
| load_bq(df_load, schema, table_name, "WRITE_TRUNCATE") |
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Output: