title: Convert Bigquery results to Pandas Data Frame
body: <p>Below is the code to convert BigQuery results into Pandas data frame.
  Im learning Python&amp;Pandas and wonder if i can get suggestion/ideas about any kind of improvements to the code?</p>
  
  <pre><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')
  </code></pre>
  
  <p>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.</p>