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

# DownloadFormat

> File formats for downloading and exporting explanation data.

File formats for downloading and exporting explanation data.

This enum defines the supported file formats for downloading explanation
results from Fiddler. Different formats offer different advantages in
terms of performance, compatibility, and data structure preservation.

## PARQUET

Apache Parquet format for efficient columnar storage

## CSV

Comma-separated values format for broad compatibility

### Examples

Downloading explanations in different formats:

```python theme={null}
# Download as Parquet (recommended for large datasets)
parquet_data = model.download_explanations(
    format=fdl.DownloadFormat.PARQUET,
    chunk_size=1000
)

# Download as CSV (better compatibility)
csv_data = model.download_explanations(
    format=fdl.DownloadFormat.CSV,
    chunk_size=500

    ## )
```

<Info>
  Choose format based on your analysis tools and data size requirements.
  Parquet is recommended for large datasets due to compression and performance.
</Info>

## PARQUET *= 'PARQUET'*

Apache Parquet format for efficient columnar data storage.

Parquet is a columnar storage format that provides excellent compression
and query performance. It preserves data types and schema information,
making it ideal for analytical workloads and large datasets.

Advantages:

* Excellent compression ratios
* Fast query performance
* Preserves data types and schema
* Efficient for analytical operations

Best for:

* Large explanation datasets
* Analytical workflows
* Integration with data science tools
* Long-term data storage

## CSV *= 'CSV'*

Comma-separated values format for broad tool compatibility.

CSV is a simple, widely-supported text format that can be opened by
virtually any data analysis tool, spreadsheet application, or programming
language. While less efficient than Parquet, it offers maximum compatibility.

Advantages:

* Universal compatibility
* Human-readable format
* Simple structure
* Supported by all tools

Best for:

* Small to medium datasets
* Sharing with non-technical users
* Quick data inspection
* Integration with legacy systems