> ## 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.

# Pulling Data into a Pandas DataFrame

> How to extract data from CloudQuant Data Liberator datasets into Python pandas DataFrames with practical examples.

# Pulling data into a Pandas DataFrame

This guide demonstrates how to extract data from CloudQuant Data Liberator datasets into Python pandas DataFrames. The examples use `daily_bars`, a dataset containing US Equity daily OHLCV (Open, High, Low, Close, Volume) data with additional fields.

## Example: pulling daily bars for AAPL

### Multi-line approach

```python theme={null}
today = '2024-06-20'
oneMonthAgo = '2024-05-20'
dataset = 'daily_bars'
mySymbols = 'AAPL'
query = liberator.query(name=dataset, symbols=mySymbols, as_of=today, back_to=oneMonthAgo)
df = liberator.get_dataframe(query)
```

### Single-line approach

```python theme={null}
df = liberator.get_dataframe(liberator.query(name='daily_bars', symbols='AAPL', as_of='2024-06-20', back_to='2024-05-20'))
```

## Key features

* **Flexible symbol input** — accepts single symbols, lists, or `None` (all symbols)
* **Granular time selection** — supports daily, minute, second, and nanosecond resolution
* **Simple syntax** — collapsible into single-line queries for efficiency

The query returns a properly formatted pandas DataFrame containing the requested historical data.