> ## Documentation Index
> Fetch the complete documentation index at: https://knowledge.cloudquant.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Selecting Specific Columns

> How to use the fields parameter to restrict which columns are returned in CloudQuant Data Liberator query results.

# 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:

```python theme={null}
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']
))
```

<Note>
  CloudQuant Data Liberator places your selected columns at the front of the DataFrame, followed by the default columns.
</Note>

## Discovering available columns

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

### Using the schema function

```python theme={null}
liberator.datasets(schema=True)['nameOfDataset']
```

### Inspecting a sample query result

```python theme={null}
df.columns
list(df.columns)
print(list(df.columns))
```

Example output for `daily_bars`:

```python theme={null}
['_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']
```