pandas-gbq

import os import pandas as pd import pandas_gbq # Set your Google Cloud Project ID # It's recommended to set this as an environment variable or via credentials project_id = os.environ.get('GOOGLE_CLOUD_PROJECT', 'your-gcp-project-id') # --- Reading data from BigQuery --- # Example query from a public dataset sql_query = """ SELECT country_name, alpha_2_code FROM `bigquery-public-data.utility_us.country_code_iso` WHERE alpha_2_code LIKE 'U%' LIMIT 5 """ try: df_read = pandas_gbq.read_gbq(sql_query, project_id=project_id) print("\n--- Data read from BigQuery ---") print(df_read) except Exception as e: print(f"Error reading from BigQuery: {e}") print("Please ensure GOOGLE_CLOUD_PROJECT is set and you have authenticated (e.g., `gcloud auth application-default login`).") # --- Writing data to BigQuery --- # Create a sample DataFrame to upload data = { 'col1': [1, 2, 3], 'col2': ['A', 'B', 'C'], 'timestamp_col': pd.to_datetime(['2026-01-01', '2026-01-02', '2026-01-03']) } df_write = pd.DataFrame(data) # Define destination table (dataset.tablename) destination_table = 'my_test_dataset.my_test_table' # To avoid errors, you might want to replace the table if it exists for testing # In production, consider 'append' or 'fail' with proper checks try: pandas_gbq.to_gbq( df_write, destination_table, project_id=project_id, if_exists='replace' # Options: 'fail', 'replace', 'append' ) print(f"\n--- DataFrame successfully written to {destination_table} in project {project_id} ---") except Exception as e: print(f"Error writing to BigQuery: {e}") print("Ensure 'my_test_dataset' exists in BigQuery or remove 'my_test_dataset.' from 'destination_table' to allow automatic dataset creation if permitted.")