> ## 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.

# Usage of the as_of Parameter

> How to use the as_of parameter to retrieve data as it was known at a particular point in time.

# Usage of the as\_of parameter

The `as_of` parameter allows you to query historical data from a specific moment. You can specify it as a date alone (e.g., `'2024-07-08'`) which CloudQuant Data Liberator interprets as midnight that date, or with specific times down to nanosecond precision.

<Info>
  All CloudQuant Data Liberator times are New York Time (US ET).
</Info>

## Default behavior

When you omit the `as_of` parameter, CloudQuant Data Liberator assumes the current date and time.

## Example query

```python theme={null}
df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-10',
    back_to = '2024-07-10',
    symbols = ['GOOG', 'AAPL', 'MSFT']))
```

This returns data for the specified date range.

## Without back\_to parameter

If you specify `as_of` but omit or set `back_to` to `None`, the query returns data through yesterday's completed trading bar:

```python theme={null}
df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-18',
    symbols = ['GOOG', 'AAPL', 'MSFT']))
```

## Jupyter notebook configuration

To display more rows in Jupyter:

```python theme={null}
import pandas as pd
pd.set_option('display.max_rows', 400)
```

<Tip>
  Consider your data volume and test with smaller queries initially. See [Query Considerations](/python-guide/query-considerations) for more details.
</Tip>