Skip to main content

​
Selecting Specific Columns

CloudQuant Data Liberator automatically returns a minimum default set of columns for each dataset, which varies but typically includes _seq, muts, timestamp, and symbol. Since some datasets contain hundreds of columns, you may want to limit results to specific fields, especially on slower connections.

​
Filtering Columns with the Fields Parameter

To reduce returned columns, pass the fields parameter with your desired column list: 
df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-24',
    back_to = '2024-07-22',
    symbols = ['AAPL', 'GOOGL'],
    fields = ['Open', 'Close']
))
CloudQuant Data Liberator places your selected columns at the front of the DataFrame, followed by the default columns.

​
Discovering Available Columns

To identify column names in a dataset, use one of these approaches:

​
Using the schema function

liberator.datasets(schema=True)['nameOfDataset']

​
Inspecting a sample query result

df.columns
list(df.columns)
print(list(df.columns))
Example output for daily_bars: 
['_seq', '_dsname', 'timestamp', 'msg_len', 'msg', 'muts', 'symbol',
'length', 'open', 'high', 'low', 'close', 'volume', 'vwap', 'bvwap',
'spread', 'bidvol', 'askvol', 'count', 'avgdelta', 'Date', 'Time',
'Hour', 'DateTime', 'DateHour']