lint fixes

Author: joe-clickhouse

docs/integrations/language-clients/python/advanced-inserting.md

@@ -250,5 +250,3 @@ insert_file(client, 'example_table', 'my_data.csv',
             settings={'input_format_allow_errors_ratio': .2,
                       'input_format_allow_errors_num': 5})
 ```
-
-

docs/integrations/language-clients/python/advanced-querying.md

@@ -424,7 +424,6 @@ ClickHouse queries can accept external data in any ClickHouse format. This binar
 | structure | str or seq of str | A list of column name + data type in the data (see examples). Either `structure` or `types` is required                                       |
 | mime_type | str               | Optional MIME type of the file data. Currently ClickHouse ignores this HTTP subheader                                                         |
 
-
 To send a query with an external CSV file containing "movie" data, and combine that data with an `directors` table already present on the ClickHouse server:
 
 ```python
@@ -453,4 +452,3 @@ When using time zone aware data types in queries - in particular the Python `dat
 5. Finally, if the client `apply_server_timezone` parameter has been set to True (the default), the ClickHouse server timezone is applied.
 
 Note that if the applied timezone based on these rules is UTC, `clickhouse-connect` will _always_ return a time zone naive Python `datetime.datetime` object. Additional timezone information can then be added to this timezone naive object by the application code if desired.
-

docs/integrations/language-clients/python/index.md

@@ -24,7 +24,6 @@ ClickHouse Connect is a core database driver providing interoperability with a w
 - There is a [SQLAlchemy](https://www.sqlalchemy.org/) dialect in the package `clickhouse_connect.cc_sqlalchemy` which is built off of the `datatypes` and `dbi` packages. This implementation supports SQLAlchemy Core functionality including `SELECT` queries with `JOIN`s (`INNER`, `LEFT OUTER`, `FULL OUTER`, `CROSS`), `WHERE` clauses, `ORDER BY`, `LIMIT`/`OFFSET`, `DISTINCT` operations, lightweight `DELETE` statements with `WHERE` conditions, table reflection, and basic DDL operations (`CREATE TABLE`, `CREATE`/`DROP DATABASE`). While it does not support advanced ORM features or advanced DDL features, it provides robust query capabilities suitable for most analytical workloads against ClickHouse's OLAP-oriented database.
 - The core driver and [ClickHouse Connect SQLAlchemy](sqlalchemy.md) implementation are the preferred method for connecting ClickHouse to Apache Superset. Use the `ClickHouse Connect` database connection, or `clickhousedb` SQLAlchemy dialect connection string.
 
-
 This documentation is current as of the clickhouse-connect release 0.9.2.
 
 :::note
@@ -43,7 +42,6 @@ The official ClickHouse Connect Python driver uses the HTTP protocol for communi
 |       3.13.x | ✅ |                 |   | 25.7.x (Stable) | ✅  |             |   |           3.0.x | ✅ |         |   |        |   |
 |              |   |                 |   |    25.8.x (LTS) | ✅  |             |   |                 |   |         |   |        |   |
 
-
 ¹ClickHouse Connect has been explicitly tested against the listed platforms. In addition, untested binary wheels (with C optimization) are built for all architectures supported by the excellent [`cibuildwheel`](https://cibuildwheel.readthedocs.io/en/stable/) project. Finally, because ClickHouse Connect can also run as pure Python, the source installation should work on any recent Python installation.
 
 ²SQLAlchemy support is limited to Core functionality (queries, basic DDL). ORM features are not supported. See [SQLAlchemy Integration Support](sqlalchemy.md) docs for details.