Prepare 2.13 release

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 2.12.1
+current_version = 2.13.0
 commit = False
 tag = False
 tag_name = {new_version}

CONTRIBUTING_COMMON_ERRORS.md

@@ -13,9 +13,9 @@ Requirement already satisfied: pbr!=2.1.0,>=2.0.0 in ./.venv/lib/python3.7/site-
 Using legacy 'setup.py install' for python-Levenshtein, since package 'wheel' is not installed.
 Installing collected packages: awswrangler, python-Levenshtein
   Attempting uninstall: awswrangler
-    Found existing installation: awswrangler 2.12.1
-    Uninstalling awswrangler-2.12.1:
-      Successfully uninstalled awswrangler-2.12.1
+    Found existing installation: awswrangler 2.13.0
+    Uninstalling awswrangler-2.13.0:
+      Successfully uninstalled awswrangler-2.13.0
   Running setup.py develop for awswrangler
     Running setup.py install for python-Levenshtein ... error
     ERROR: Command errored out with exit status 1:

README.md

@@ -8,7 +8,7 @@ Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, Clo
 
 > An [AWS Professional Service](https://aws.amazon.com/professional-services/) open source initiative | aws-proserve-opensource@amazon.com
 
-[![Release](https://img.shields.io/badge/release-2.12.1-brightgreen.svg)](https://pypi.org/project/awswrangler/)
+[![Release](https://img.shields.io/badge/release-2.13.0-brightgreen.svg)](https://pypi.org/project/awswrangler/)
 [![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-brightgreen.svg)](https://anaconda.org/conda-forge/awswrangler)
 [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
 [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
@@ -23,7 +23,7 @@ Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, Clo
 | **[PyPi](https://pypi.org/project/awswrangler/)**  | [![PyPI Downloads](https://pepy.tech/badge/awswrangler)](https://pypi.org/project/awswrangler/) | `pip install awswrangler` |
 | **[Conda](https://anaconda.org/conda-forge/awswrangler)** | [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/awswrangler.svg)](https://anaconda.org/conda-forge/awswrangler) | `conda install -c conda-forge awswrangler` |
 
-> ⚠️ **For platforms without PyArrow 3 support (e.g. [EMR](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#aws-glue-pyspark-jobs), MWAA):**<br>
+> ⚠️ **For platforms without PyArrow 3 support (e.g. [EMR](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#aws-glue-pyspark-jobs), MWAA):**<br>
 ➡️ `pip install pyarrow==2 awswrangler`
 
 Powered By [<img src="https://arrow.apache.org/img/arrow.png" width="200">](https://arrow.apache.org/powered_by/)
@@ -42,7 +42,7 @@ Powered By [<img src="https://arrow.apache.org/img/arrow.png" width="200">](http
 
 Installation command: `pip install awswrangler`
 
-> ⚠️ **For platforms without PyArrow 3 support (e.g. [EMR](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#aws-glue-pyspark-jobs), MWAA):**<br>
+> ⚠️ **For platforms without PyArrow 3 support (e.g. [EMR](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#aws-glue-pyspark-jobs), MWAA):**<br>
 ➡️`pip install pyarrow==2 awswrangler`
 
 ```py3
@@ -96,17 +96,17 @@ FROM "sampleDB"."sampleTable" ORDER BY time DESC LIMIT 3
 
 ## [Read The Docs](https://aws-data-wrangler.readthedocs.io/)
 
-- [**What is AWS Data Wrangler?**](https://aws-data-wrangler.readthedocs.io/en/2.12.1/what.html)
-- [**Install**](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html)
-  - [PyPi (pip)](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#pypi-pip)
-  - [Conda](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#conda)
-  - [AWS Lambda Layer](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#aws-lambda-layer)
-  - [AWS Glue Python Shell Jobs](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#aws-glue-python-shell-jobs)
-  - [AWS Glue PySpark Jobs](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#aws-glue-pyspark-jobs)
-  - [Amazon SageMaker Notebook](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#amazon-sagemaker-notebook)
-  - [Amazon SageMaker Notebook Lifecycle](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#amazon-sagemaker-notebook-lifecycle)
-  - [EMR](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#emr)
-  - [From source](https://aws-data-wrangler.readthedocs.io/en/2.12.1/install.html#from-source)
+- [**What is AWS Data Wrangler?**](https://aws-data-wrangler.readthedocs.io/en/2.13.0/what.html)
+- [**Install**](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html)
+  - [PyPi (pip)](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#pypi-pip)
+  - [Conda](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#conda)
+  - [AWS Lambda Layer](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#aws-lambda-layer)
+  - [AWS Glue Python Shell Jobs](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#aws-glue-python-shell-jobs)
+  - [AWS Glue PySpark Jobs](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#aws-glue-pyspark-jobs)
+  - [Amazon SageMaker Notebook](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#amazon-sagemaker-notebook)
+  - [Amazon SageMaker Notebook Lifecycle](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#amazon-sagemaker-notebook-lifecycle)
+  - [EMR](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#emr)
+  - [From source](https://aws-data-wrangler.readthedocs.io/en/2.13.0/install.html#from-source)
 - [**Tutorials**](https://github.com/awslabs/aws-data-wrangler/tree/main/tutorials)
   - [001 - Introduction](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/001%20-%20Introduction.ipynb)
   - [002 - Sessions](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/002%20-%20Sessions.ipynb)

awswrangler/__metadata__.py

@@ -7,5 +7,5 @@
 
 __title__: str = "awswrangler"
 __description__: str = "Pandas on AWS."
-__version__: str = "2.12.1"
+__version__: str = "2.13.0"
 __license__: str = "Apache License 2.0"

awswrangler/athena/_read.py

@@ -626,11 +626,11 @@ def read_sql_query(
 
     **Related tutorial:**
 
-    - `Amazon Athena <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Amazon Athena <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/006%20-%20Amazon%20Athena.html>`_
-    - `Athena Cache <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Athena Cache <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/019%20-%20Athena%20Cache.html>`_
-    - `Global Configurations <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Global Configurations <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/021%20-%20Global%20Configurations.html>`_
 
     **There are two approaches to be defined through ctas_approach parameter:**
@@ -678,7 +678,7 @@ def read_sql_query(
     /athena.html#Athena.Client.get_query_execution>`_ .
 
     For a practical example check out the
-    `related tutorial <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    `related tutorial <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
     tutorials/024%20-%20Athena%20Query%20Metadata.html>`_!
 
 
@@ -911,11 +911,11 @@ def read_sql_table(
 
     **Related tutorial:**
 
-    - `Amazon Athena <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Amazon Athena <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/006%20-%20Amazon%20Athena.html>`_
-    - `Athena Cache <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Athena Cache <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/019%20-%20Athena%20Cache.html>`_
-    - `Global Configurations <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    - `Global Configurations <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
       tutorials/021%20-%20Global%20Configurations.html>`_
 
     **There are two approaches to be defined through ctas_approach parameter:**
@@ -960,7 +960,7 @@ def read_sql_table(
     /athena.html#Athena.Client.get_query_execution>`_ .
 
     For a practical example check out the
-    `related tutorial <https://aws-data-wrangler.readthedocs.io/en/2.12.1/
+    `related tutorial <https://aws-data-wrangler.readthedocs.io/en/2.13.0/
     tutorials/024%20-%20Athena%20Query%20Metadata.html>`_!
 
 

awswrangler/s3/_read_parquet.py

@@ -841,7 +841,7 @@ def read_parquet_table(
         This function MUST return a bool, True to read the partition or False to ignore it.
         Ignored if `dataset=False`.
         E.g ``lambda x: True if x["year"] == "2020" and x["month"] == "1" else False``
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
     columns : List[str], optional
         Names of columns to read from the file(s).
     validate_schema:

awswrangler/s3/_read_text.py

@@ -241,7 +241,7 @@ def read_csv(
         This function MUST return a bool, True to read the partition or False to ignore it.
         Ignored if `dataset=False`.
         E.g ``lambda x: True if x["year"] == "2020" and x["month"] == "1" else False``
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
     pandas_kwargs :
         KEYWORD arguments forwarded to pandas.read_csv(). You can NOT pass `pandas_kwargs` explicit, just add valid
         Pandas arguments in the function call and Wrangler will accept it.
@@ -389,7 +389,7 @@ def read_fwf(
         This function MUST return a bool, True to read the partition or False to ignore it.
         Ignored if `dataset=False`.
         E.g ``lambda x: True if x["year"] == "2020" and x["month"] == "1" else False``
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
     pandas_kwargs:
         KEYWORD arguments forwarded to pandas.read_fwf(). You can NOT pass `pandas_kwargs` explicit, just add valid
         Pandas arguments in the function call and Wrangler will accept it.
@@ -541,7 +541,7 @@ def read_json(
         This function MUST return a bool, True to read the partition or False to ignore it.
         Ignored if `dataset=False`.
         E.g ``lambda x: True if x["year"] == "2020" and x["month"] == "1" else False``
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/023%20-%20Flexible%20Partitions%20Filter.html
     pandas_kwargs:
         KEYWORD arguments forwarded to pandas.read_json(). You can NOT pass `pandas_kwargs` explicit, just add valid
         Pandas arguments in the function call and Wrangler will accept it.

awswrangler/s3/_write_parquet.py

@@ -300,18 +300,18 @@ def to_parquet(  # pylint: disable=too-many-arguments,too-many-locals,too-many-b
     concurrent_partitioning: bool
         If True will increase the parallelism level during the partitions writing. It will decrease the
         writing time and increase the memory usage.
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
     mode: str, optional
         ``append`` (Default), ``overwrite``, ``overwrite_partitions``. Only takes effect if dataset=True.
         For details check the related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
     catalog_versioning : bool
         If True and `mode="overwrite"`, creates an archived version of the table catalog before updating it.
     schema_evolution : bool
         If True allows schema evolution (new or missing columns), otherwise a exception will be raised. True by default.
         (Only considered if dataset=True and mode in ("append", "overwrite_partitions"))
         Related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/014%20-%20Schema%20Evolution.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/014%20-%20Schema%20Evolution.html
     database : str, optional
         Glue/Athena catalog: Database name.
     table : str, optional

awswrangler/s3/_write_text.py

@@ -177,18 +177,18 @@ def to_csv(  # pylint: disable=too-many-arguments,too-many-locals,too-many-state
     concurrent_partitioning: bool
         If True will increase the parallelism level during the partitions writing. It will decrease the
         writing time and increase the memory usage.
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
     mode : str, optional
         ``append`` (Default), ``overwrite``, ``overwrite_partitions``. Only takes effect if dataset=True.
         For details check the related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
     catalog_versioning : bool
         If True and `mode="overwrite"`, creates an archived version of the table catalog before updating it.
     schema_evolution : bool
         If True allows schema evolution (new or missing columns), otherwise a exception will be raised.
         (Only considered if dataset=True and mode in ("append", "overwrite_partitions")). False by default.
         Related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/014%20-%20Schema%20Evolution.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/014%20-%20Schema%20Evolution.html
     database : str, optional
         Glue/Athena catalog: Database name.
     table : str, optional
@@ -750,18 +750,18 @@ def to_json(  # pylint: disable=too-many-arguments,too-many-locals,too-many-stat
     concurrent_partitioning: bool
         If True will increase the parallelism level during the partitions writing. It will decrease the
         writing time and increase the memory usage.
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/022%20-%20Writing%20Partitions%20Concurrently.html
     mode : str, optional
         ``append`` (Default), ``overwrite``, ``overwrite_partitions``. Only takes effect if dataset=True.
         For details check the related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/stubs/awswrangler.s3.to_parquet.html#awswrangler.s3.to_parquet
     catalog_versioning : bool
         If True and `mode="overwrite"`, creates an archived version of the table catalog before updating it.
     schema_evolution : bool
         If True allows schema evolution (new or missing columns), otherwise a exception will be raised.
         (Only considered if dataset=True and mode in ("append", "overwrite_partitions"))
         Related tutorial:
-        https://aws-data-wrangler.readthedocs.io/en/2.12.1/tutorials/014%20-%20Schema%20Evolution.html
+        https://aws-data-wrangler.readthedocs.io/en/2.13.0/tutorials/014%20-%20Schema%20Evolution.html
     database : str, optional
         Glue/Athena catalog: Database name.
     table : str, optional

docs/source/install.rst

@@ -120,7 +120,7 @@ Go to your Glue PySpark job and create a new *Job parameters* key/value:
 
 To install a specific version, set the value for above Job parameter as follows:
 
-* Value: ``cython==0.29.21,pg8000==1.21.0,pyarrow==2,pandas==1.3.0,awswrangler==2.12.1``
+* Value: ``cython==0.29.21,pg8000==1.21.0,pyarrow==2,pandas==1.3.0,awswrangler==2.13.0``
 
 .. note:: Pyarrow 3 is not currently supported in Glue PySpark Jobs, which is why a previous installation of pyarrow 2 is required.
 
@@ -139,7 +139,7 @@ Lambda zipped layers and Python wheels are stored in a publicly accessible S3 bu
 
   * Python wheel: ``awswrangler-<version>-py3-none-any.whl``
 
-For example: ``s3://aws-data-wrangler-public-artifacts/releases/2.12.1/awswrangler-layer-2.12.1-py3.8.zip``
+For example: ``s3://aws-data-wrangler-public-artifacts/releases/2.13.0/awswrangler-layer-2.13.0-py3.8.zip``
 
 Amazon SageMaker Notebook
 -------------------------
@@ -231,7 +231,7 @@ complement Big Data pipelines.
         sudo pip install pyarrow==2 awswrangler
 
 .. note:: Make sure to freeze the Wrangler version in the bootstrap for productive
-          environments (e.g. awswrangler==2.12.1)
+          environments (e.g. awswrangler==2.13.0)
 
 .. note:: Pyarrow 3 is not currently supported in the default EMR image, which is why a previous installation of pyarrow 2 is required.
 

docs/source/what.rst

@@ -8,4 +8,4 @@ SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL
 
 Built on top of other open-source projects like `Pandas <https://github.com/pandas-dev/pandas>`_, `Apache Arrow <https://github.com/apache/arrow>`_ and `Boto3 <https://github.com/boto/boto3>`_, it offers abstracted functions to execute usual ETL tasks like load/unload data from **Data Lakes**, **Data Warehouses** and **Databases**.
 
-Check our `tutorials <https://github.com/awslabs/aws-data-wrangler/tree/main/tutorials>`_ or the `list of functionalities <https://aws-data-wrangler.readthedocs.io/en/2.12.1/api.html>`_.
+Check our `tutorials <https://github.com/awslabs/aws-data-wrangler/tree/main/tutorials>`_ or the `list of functionalities <https://aws-data-wrangler.readthedocs.io/en/2.13.0/api.html>`_.

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "awswrangler"
-version = "2.12.1"
+version = "2.13.0"
 description = "Pandas on AWS."
 authors = ["Igor Tavares"]
 license = "Apache License 2.0"

test_infra/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "awswrangler - test infrastructure"
-version = "2.12.1"
+version = "2.13.0"
 description = "CDK test infrastructure for AWS"
 authors = ["Amazon Web Services"]
 license = "Apache License 2.0"

tests/test_metadata.py

@@ -2,7 +2,7 @@
 
 
 def test_metadata():
-    assert wr.__version__ == "2.12.1"
+    assert wr.__version__ == "2.13.0"
     assert wr.__title__ == "awswrangler"
     assert wr.__description__ == "Pandas on AWS."
     assert wr.__license__ == "Apache License 2.0"