Python-based SQL queries - Exercise 2

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# Step 1
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from google.cloud import bigquery

# Create a "Client" object
client = bigquery.Client()

# Construct a reference to the "openaq" dataset
dataset_ref = client.dataset("openaq", project="bigquery-public-data")

# API request - fetch the dataset
dataset = client.get_dataset(dataset_ref)

# Construct a reference to the "global_air_quality" table
table_ref = dataset_ref.table("global_air_quality")

# API request - fetch the table
table = client.get_table(table_ref)

# Preview the first five lines of the "global_air_quality" table
client.list_rows(table, max_results=5).to_dataframe()



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# Step 2
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# Query to select countries with units of "ppm"
first_query = """
                SELECT DISTINCT country
                from `bigquery-public-data.openaq.global_air_quality`
                WHERE unit = 'ppm'
            """ # Your code goes here

# Set up the query (cancel the query if it would use too much of
# your quota, with the limit set to 10 GB)
safe_config = bigquery.QueryJobConfig(maximum_bytes_billed=10**10)
first_query_job = client.query(first_query, job_config=safe_config)

# API request - run the query, and return a pandas DataFrame
first_results = first_query_job.to_dataframe()
# View top few rows of results
print(first_results.head())