Convert Bigquery results to Pandas Data Frame

Question

Below is the code to convert BigQuery results into Pandas data frame. Im learning Python&Pandas and wonder if i can get suggestion/ideas about any kind of improvements to the code?

#...code to run query, that returns 3 columns: 'date' DATE, 'currency' STRING,'rate' FLOAT...

rows, total_count, token = query.fetch_data()
currency = []
rate = []
dates = []
for row in rows:
    dates.append(row[0])
    currency.append(row[1])
    rate.append(row[2])


dict = {
'currency' : currency,
'date' : dates,
'rate' : rate
}

df2 = pd.DataFrame(dict)

df2['date'] = pd.to_datetime(df2['date'])
df2 = df2.set_index('date')

The above works. But looks chunky. Is there any way to do the same thing more efficiently than the above? I tried libraries such as sqlalchemy but they do not support BigQuery. And generally my question is about code and syntax above.

Solution 1

You should use read_gbq() instead: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_gbq.html

Solution 2

Per the Using BigQuery with Pandas page in the Google Cloud Client Library for Python:

As of version 0.29.0, you can use the to_dataframe() function to retrieve query results or table rows as a pandas.DataFrame.

Aside: See Migrating from pandas-gbq for the difference between the google-cloud-bigquery BQ Python client library and pandas-gbq.