# Parquet

Loading or writing [`Parquet` files](https://parquet.apache.org/) is lightning fast as the layout of
data in a Polars `DataFrame` in memory mirrors the layout of a Parquet file on disk in many
respects.

Unlike CSV, Parquet is a columnar format. This means that the data is stored in columns rather than
rows. This is a more efficient way of storing data as it allows for better compression and faster
access to data.

## Read

We can read a `Parquet` file into a `DataFrame` using the `read_parquet` function:

{{code_block('user-guide/io/parquet','read',['read_parquet'])}}

## Write

{{code_block('user-guide/io/parquet','write',['write_parquet'])}}

## Scan

Polars allows you to _scan_ a `Parquet` input. Scanning delays the actual parsing of the file and
instead returns a lazy computation holder called a `LazyFrame`.

{{code_block('user-guide/io/parquet','scan',['scan_parquet'])}}

If you want to know why this is desirable, you can read more about those Polars optimizations
[here](../concepts/lazy-api.md).

When we scan a `Parquet` file stored in the cloud, we can also apply predicate and projection
pushdowns. This can significantly reduce the amount of data that needs to be downloaded. For
scanning a Parquet file in the cloud, see
[Cloud storage](cloud-storage.md#scanning-from-cloud-storage-with-query-optimisation).
