# Release of PyExasol 1.2.0

**URL:** <https://community.exasol.com/t/release-of-pyexasol-1-2-0/227>\
**Category:** General\
**Created:** [September 29, 2025, 6:56am UTC](https://community.exasol.com/t/release-of-pyexasol-1-2-0/227 "2025-09-29T06:56:47Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Ariel\_Schulz](https://sea2.discourse-cdn.com/flex002/user_avatar/community.exasol.com/ariel_schulz/32/61_2.png) [@Ariel\_Schulz](https://community.exasol.com/u/Ariel_Schulz)\
**Post date:** [September 29, 2025, 6:56am UTC](https://community.exasol.com/t/release-of-pyexasol-1-2-0/227/1 "2025-09-29T06:56:47Z")

</div>

We are happy to announce the release of [pyexasol 1.2.0](https://github.com/exasol/pyexasol/releases/tag/1.2.0 "https://github.com/exasol/pyexasol/releases/tag/1.2.0") with support to import from & export to local parquet file(s).

```python
# As parquet usage is linked to an optional import, make sure to use the equivalent 
# of `pip install pyexasol[pyarrow]` in your dependency management tool beforehand.

import pyexasol
from pathlib import Path

# create the connection with compression activated
C = pyexasol.connect(dsn='<host:port>', user='<user>', password='<password>', compression=True)

# list[Path]: list of specific local parquet files to load
C.import_from_parquet(source=[Path("local_path/test.parquet")], table="users")

# Path: can be either a local file or directory. If it's a local directory,
# all files matching this pattern `*.parquet` will be processed.
C.import_from_parquet(source=Path("local_path/test.parquet"), table="users")

# string: representing a local filepath which already contains a glob pattern
C.import_from_parquet(source="local_path/*.parquet", table="users")

# Unless modified via the callback_params argument, the default requirement is that the
# dst directory must be empty (consequence: will not execute further & raises exception) 
# and that all data is saved into one file.

# read from table using a SQL statement & write to a parquet file which will be added to dst
C.export_to_parquet(dst="other_local_path", query_or_table="SELECT * FROM users")

# read from table by giving it as a string & write to a parquet file which will be added to dst
C.export_to_parquet(dst="other_local_path", query_or_table="users")

```