> ## 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.

# Symbols and Key Fields

> Understanding the symbol parameter in CloudQuant Data Liberator queries, how to discover available symbols, and how to query for all symbols in a dataset.

# Symbols and key fields

The **symbol** parameter serves as the key for querying datasets in CloudQuant Data Liberator. The platform initially focused on US Equity Symbols, but its datasets have expanded to include diverse data types such as shipping, weather, and market research. These datasets use varied keys like zip codes and geographic locations. CloudQuant Data Liberator keeps the `symbols` parameter name for consistency with existing integrations.

**Default Value:** `None` (returns all symbols/keys, including blanks/NaN values)

<Warning>
  When setting your query parameters, consider the amount of data you may be requesting and start small until you understand data volume patterns for each symbol/date combination.
</Warning>

## Determining unique symbols in a dataset

The most straightforward approach is to query and count the data. Here is an example using the `daily_bars` dataset:

```python theme={null}
df = liberator.get_dataframe(liberator.query(
    name = 'daily_bars',
    as_of = '2024-07-24',
    back_to = '2024-07-22',
    symbols = None))
df
```

This returns over 20,000 rows. To count unique symbols:

```python theme={null}
len(df.symbol.unique())
# Output: 10524
```

## Storing and displaying symbols

To store symbols in a variable:

```python theme={null}
allsym = list(df.symbol.unique())
```

To display the complete list (with caution — lists can be lengthy):

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

## Query methods for all symbols

Either explicitly set `symbols = None` or omit the parameter entirely, as it defaults to `None`.