BigQuery is a powerful, fully-managed data warehouse that supports standard SQL. With the Python client, you can query BigQuery tables, fetch results, and work with the data using native Python or with libraries like Pandas.

In this article, you’ll learn:

• How to run SELECT queries using Python

• How to use query parameters

• How to work with results (basic and Pandas)

• Tips and common pitfalls

✅ Prerequisites

Before running BigQuery queries via Python:

• Enable the BigQuery API in your GCP project

• Create a BigQuery dataset and table

• Install and configure the Python client

Install the BigQuery Client

pip install google-cloud-bigquery

Step 1: Authenticate and Initialize Client

import os
from google.cloud import bigquery

# Set path to your service account key
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your-key.json"

# Initialize the BigQuery client
client = bigquery.Client()

Step 2: Write a Basic SELECT Query

Assume you have a table named my_dataset.customers.

SELECT id, name, email, signup_date
FROM `your-project-id.my_dataset.customers`
WHERE signup_date >= '2023-01-01'
ORDER BY signup_date DESC
LIMIT 10

▶️ Step 3: Run the Query in Python

query = """
    SELECT id, name, email, signup_date
    FROM `your-project-id.my_dataset.customers`
    WHERE signup_date >= '2023-01-01'
    ORDER BY signup_date DESC
    LIMIT 10
"""

# Run the query
query_job = client.query(query)

# Get the results
results = query_job.result()

# Print each row
for row in results:
    print(f"{row.id}, {row.name}, {row.email}, {row.signup_date}")

Step 4: Load Results into a Pandas DataFrame (Optional)

If you want to analyze the data in Python using Pandas:

import pandas as pd

# Run the query and convert to DataFrame
df = client.query(query).to_dataframe()

print(df.head())

Step 5: Use Query Parameters (Safe and Efficient)

Example: Using Named Parameters

query = """
    SELECT id, name, email, signup_date
    FROM `your-project-id.my_dataset.customers`
    WHERE signup_date >= @start_date
"""

job_config = bigquery.QueryJobConfig(
    query_parameters=[
        bigquery.ScalarQueryParameter("start_date", "DATE", "2023-01-01")
    ]
)

query_job = client.query(query, job_config=job_config)
rows = query_job.result()

for row in rows:
    print(row)

Full Example: SELECT Query with Parameters and Pandas

import os
import pandas as pd
from google.cloud import bigquery

# Authenticate
os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = "path/to/your-key.json"
client = bigquery.Client()

# Define query with parameter
query = """
    SELECT id, name, email, signup_date
    FROM `your-project-id.my_dataset.customers`
    WHERE signup_date >= @start_date
    ORDER BY signup_date
"""

# Use parameterized query
job_config = bigquery.QueryJobConfig(
    query_parameters=[
        bigquery.ScalarQueryParameter("start_date", "DATE", "2023-01-01")
    ]
)

# Run the query
query_job = client.query(query, job_config=job_config)

# Fetch results into a DataFrame
df = query_job.to_dataframe()

print(df)

Tips for SELECT Queries in BigQuery

⚠️ Common Pitfalls

Conclusion

Using Python to run SELECT queries in BigQuery is powerful and flexible. You can:

• Query large datasets using SQL

• Automate analytics pipelines

• Filter, sort, and parameterize your queries

• Load results into Pandas for advanced analysis

With the combination of BigQuery’s scalability and Python’s versatility, querying data has never been easier.