Metadata-Version: 2.1
Name: awswrangler
Version: 2.10.0
Summary: Pandas on AWS.
Home-page: https://github.com/awslabs/aws-data-wrangler
Author: Igor Tavares
License: Apache License 2.0
Description: # AWS Data Wrangler
        
        *Pandas on AWS*
        
        Easy integration with Athena, Glue, Redshift, Timestream, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
        
        ![AWS Data Wrangler](docs/source/_static/logo2.png?raw=true "AWS Data Wrangler")
        
        > 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-2.10.0-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)
        
        [![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
        [![Coverage](https://img.shields.io/badge/coverage-91%25-brightgreen.svg)](https://pypi.org/project/awswrangler/)
        ![Static Checking](https://github.com/awslabs/aws-data-wrangler/workflows/Static%20Checking/badge.svg?branch=main)
        [![Documentation Status](https://readthedocs.org/projects/aws-data-wrangler/badge/?version=latest)](https://aws-data-wrangler.readthedocs.io/?badge=latest)
        
        | Source | Downloads | Installation Command |
        |--------|-----------|----------------------|
        | **[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/2.10.0/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.10.0/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/)
        
        ## Table of contents
        
        - [Quick Start](#quick-start)
        - [Read The Docs](#read-the-docs)
        - [Getting Help](#getting-help)
        - [Community Resources](#community-resources)
        - [Logging](#logging)
        - [Who uses AWS Data Wrangler?](#who-uses-aws-data-wrangler)
        - [What is Amazon Sagemaker Data Wrangler?](#what-is-amazon-sageMaker-data-wrangler)
        
        ## Quick Start
        
        Installation command: `pip install awswrangler`
        
        > ⚠️ **For platforms without PyArrow 3 support (e.g. [EMR](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#emr-cluster), [Glue PySpark Job](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#aws-glue-pyspark-jobs), MWAA):**<br>
        ➡️`pip install pyarrow==2 awswrangler`
        
        ```py3
        import awswrangler as wr
        import pandas as pd
        from datetime import datetime
        
        df = pd.DataFrame({"id": [1, 2], "value": ["foo", "boo"]})
        
        # Storing data on Data Lake
        wr.s3.to_parquet(
            df=df,
            path="s3://bucket/dataset/",
            dataset=True,
            database="my_db",
            table="my_table"
        )
        
        # Retrieving the data directly from Amazon S3
        df = wr.s3.read_parquet("s3://bucket/dataset/", dataset=True)
        
        # Retrieving the data from Amazon Athena
        df = wr.athena.read_sql_query("SELECT * FROM my_table", database="my_db")
        
        # Get a Redshift connection from Glue Catalog and retrieving data from Redshift Spectrum
        con = wr.redshift.connect("my-glue-connection")
        df = wr.redshift.read_sql_query("SELECT * FROM external_schema.my_table", con=con)
        con.close()
        
        # Amazon Timestream Write
        df = pd.DataFrame({
            "time": [datetime.now(), datetime.now()],   
            "my_dimension": ["foo", "boo"],
            "measure": [1.0, 1.1],
        })
        rejected_records = wr.timestream.write(df,
            database="sampleDB",
            table="sampleTable",
            time_col="time",
            measure_col="measure",
            dimensions_cols=["my_dimension"],
        )
        
        # Amazon Timestream Query
        wr.timestream.query("""
        SELECT time, measure_value::double, my_dimension
        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/2.10.0/what.html)
        - [**Install**](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html)
          - [PyPi (pip)](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#pypi-pip)
          - [Conda](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#conda)
          - [AWS Lambda Layer](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#aws-lambda-layer)
          - [AWS Glue Python Shell Jobs](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#aws-glue-python-shell-jobs)
          - [AWS Glue PySpark Jobs](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#aws-glue-pyspark-jobs)
          - [Amazon SageMaker Notebook](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#amazon-sagemaker-notebook)
          - [Amazon SageMaker Notebook Lifecycle](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#amazon-sagemaker-notebook-lifecycle)
          - [EMR](https://aws-data-wrangler.readthedocs.io/en/2.10.0/install.html#emr)
          - [From source](https://aws-data-wrangler.readthedocs.io/en/2.10.0/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)
          - [003 - Amazon S3](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/003%20-%20Amazon%20S3.ipynb)
          - [004 - Parquet Datasets](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/004%20-%20Parquet%20Datasets.ipynb)
          - [005 - Glue Catalog](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/005%20-%20Glue%20Catalog.ipynb)
          - [006 - Amazon Athena](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/006%20-%20Amazon%20Athena.ipynb)
          - [007 - Databases (Redshift, MySQL, PostgreSQL and SQL Server)](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/007%20-%20Redshift%2C%20MySQL%2C%20PostgreSQL%2C%20SQL%20Server.ipynb)
          - [008 - Redshift - Copy & Unload.ipynb](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/008%20-%20Redshift%20-%20Copy%20%26%20Unload.ipynb)
          - [009 - Redshift - Append, Overwrite and Upsert](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/009%20-%20Redshift%20-%20Append%2C%20Overwrite%2C%20Upsert.ipynb)
          - [010 - Parquet Crawler](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/010%20-%20Parquet%20Crawler.ipynb)
          - [011 - CSV Datasets](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/011%20-%20CSV%20Datasets.ipynb)
          - [012 - CSV Crawler](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/012%20-%20CSV%20Crawler.ipynb)
          - [013 - Merging Datasets on S3](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/013%20-%20Merging%20Datasets%20on%20S3.ipynb)
          - [014 - Schema Evolution](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/014%20-%20Schema%20Evolution.ipynb)
          - [015 - EMR](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/015%20-%20EMR.ipynb)
          - [016 - EMR & Docker](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/016%20-%20EMR%20%26%20Docker.ipynb)
          - [017 - Partition Projection](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/017%20-%20Partition%20Projection.ipynb)
          - [018 - QuickSight](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/018%20-%20QuickSight.ipynb)
          - [019 - Athena Cache](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/019%20-%20Athena%20Cache.ipynb)
          - [020 - Spark Table Interoperability](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/020%20-%20Spark%20Table%20Interoperability.ipynb)
          - [021 - Global Configurations](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/021%20-%20Global%20Configurations.ipynb)
          - [022 - Writing Partitions Concurrently](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/022%20-%20Writing%20Partitions%20Concurrently.ipynb)
          - [023 - Flexible Partitions Filter](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/023%20-%20Flexible%20Partitions%20Filter.ipynb)
          - [024 - Athena Query Metadata](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/024%20-%20Athena%20Query%20Metadata.ipynb)
          - [025 - Redshift - Loading Parquet files with Spectrum](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/025%20-%20Redshift%20-%20Loading%20Parquet%20files%20with%20Spectrum.ipynb)
          - [026 - Amazon Timestream](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/026%20-%20Amazon%20Timestream.ipynb)
          - [027 - Amazon Timestream 2](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/027%20-%20Amazon%20Timestream%202.ipynb)
          - [028 - Amazon DynamoDB](https://github.com/awslabs/aws-data-wrangler/blob/main/tutorials/028%20-%20DynamoDB.ipynb)
        - [**API Reference**](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html)
          - [Amazon S3](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-s3)
          - [AWS Glue Catalog](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#aws-glue-catalog)
          - [Amazon Athena](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-athena)
          - [Amazon Redshift](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-redshift)
          - [PostgreSQL](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#postgresql)
          - [MySQL](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#mysql)
          - [SQL Server](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#sqlserver)
          - [DynamoDB](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#dynamodb)
          - [Amazon Timestream](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-timestream)
          - [Amazon EMR](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-emr)
          - [Amazon CloudWatch Logs](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-cloudwatch-logs)
          - [Amazon Chime](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-chime)
          - [Amazon QuickSight](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#amazon-quicksight)
          - [AWS STS](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#aws-sts)
          - [AWS Secrets Manager](https://aws-data-wrangler.readthedocs.io/en/2.10.0/api.html#aws-secrets-manager)
        - [**License**](https://github.com/awslabs/aws-data-wrangler/blob/main/LICENSE.txt)
        - [**Contributing**](https://github.com/awslabs/aws-data-wrangler/blob/main/CONTRIBUTING.md)
        - [**Legacy Docs** (pre-1.0.0)](https://aws-data-wrangler.readthedocs.io/en/0.3.3/)
        
        ## Getting Help
        
        The best way to interact with our team is through GitHub. You can open an [issue](https://github.com/awslabs/aws-data-wrangler/issues/new/choose) and choose from one of our templates for bug reports, feature requests...
        You may also find help on these community resources:
        * The #aws-data-wrangler Slack [channel](https://join.slack.com/t/aws-data-wrangler/shared_invite/zt-sxdx38sl-E0coRfAds8WdpxXD2Nzfrg)
        * Ask a question on [Stack Overflow](https://stackoverflow.com/questions/tagged/awswrangler)
          and tag it with `awswrangler`
        
        ## Community Resources
        
        Please [send a Pull Request](https://github.com/awslabs/aws-data-wrangler/edit/main/README.md) with your resource reference and @githubhandle.
        
        - [Optimize Python ETL by extending Pandas with AWS Data Wrangler](https://aws.amazon.com/blogs/big-data/optimize-python-etl-by-extending-pandas-with-aws-data-wrangler/) [[@igorborgest](https://github.com/igorborgest)]
        - [Reading Parquet Files With AWS Lambda](https://aprakash.wordpress.com/2020/04/14/reading-parquet-files-with-aws-lambda/) [[@anand086](https://github.com/anand086)]
        - [Transform AWS CloudTrail data using AWS Data Wrangler](https://aprakash.wordpress.com/2020/09/17/transform-aws-cloudtrail-data-using-aws-data-wrangler/) [[@anand086](https://github.com/anand086)]
        - [Rename Glue Tables using AWS Data Wrangler](https://ananddatastories.com/rename-glue-tables-using-aws-data-wrangler/) [[@anand086](https://github.com/anand086)]
        - [Getting started on AWS Data Wrangler and Athena](https://medium.com/@dheerajsharmainampudi/getting-started-on-aws-data-wrangler-and-athena-7b446c834076) [[@dheerajsharma21](https://github.com/dheerajsharma21)]
        - [Simplifying Pandas integration with AWS data related services](https://medium.com/@bv_subhash/aws-data-wrangler-simplifying-pandas-integration-with-aws-data-related-services-2b3325c12188) [[@bvsubhash](https://github.com/bvsubhash)]
        - [Build an ETL pipeline using AWS S3, Glue and Athena](https://www.linkedin.com/pulse/build-etl-pipeline-using-aws-s3-glue-athena-data-wrangler-tom-reid/) [[@taupirho](https://github.com/taupirho)]
        
        ## Logging
        
        Enabling internal logging examples:
        
        ```py3
        import logging
        logging.basicConfig(level=logging.INFO, format="[%(name)s][%(funcName)s] %(message)s")
        logging.getLogger("awswrangler").setLevel(logging.DEBUG)
        logging.getLogger("botocore.credentials").setLevel(logging.CRITICAL)
        ```
        
        Into AWS lambda:
        
        ```py3
        import logging
        logging.getLogger("awswrangler").setLevel(logging.DEBUG)
        ```
        
        ## Who uses AWS Data Wrangler?
        
        Knowing which companies are using this library is important to help prioritize the project internally.
        
        Please [send a Pull Request](https://github.com/awslabs/aws-data-wrangler/edit/main/README.md) with your company name and @githubhandle if you may.
        
        - [Amazon](https://www.amazon.com/)
        - [AWS](https://aws.amazon.com/)
        - [Cepsa](https://cepsa.com) [[@alvaropc](https://github.com/alvaropc)]
        - [Cognitivo](https://www.cognitivo.ai/) [[@msantino](https://github.com/msantino)]
        - [Digio](https://www.digio.com.br/) [[@afonsomy](https://github.com/afonsomy)]
        - [DNX](https://www.dnx.solutions/) [[@DNXLabs](https://github.com/DNXLabs)]
        - [Funcional Health Tech](https://www.funcionalcorp.com.br/) [[@webysther](https://github.com/webysther)]
        - [Informa Markets](https://www.informamarkets.com/en/home.html) [[@mateusmorato]](http://github.com/mateusmorato)
        - [LINE TV](https://www.linetv.tw/) [[@bryanyang0528](https://github.com/bryanyang0528)]
        - [Magnataur](https://magnataur.com) [[@brianmingus2](https://github.com/brianmingus2)]
        - [M4U](https://www.m4u.com.br/) [[@Thiago-Dantas](https://github.com/Thiago-Dantas)]
        - [NBCUniversal](https://www.nbcuniversal.com/) [[@vibe](https://github.com/vibe)]
        - [nrd.io](https://nrd.io/) [[@mrtns](https://github.com/mrtns)]
        - [OKRA Technologies](https://okra.ai) [[@JPFrancoia](https://github.com/JPFrancoia), [@schot](https://github.com/schot)]
        - [Pier](https://www.pier.digital/) [[@flaviomax](https://github.com/flaviomax)]
        - [Pismo](https://www.pismo.io/) [[@msantino](https://github.com/msantino)]
        - [ringDNA](https://www.ringdna.com/) [[@msropp](https://github.com/msropp)]
        - [Serasa Experian](https://www.serasaexperian.com.br/) [[@andre-marcos-perez](https://github.com/andre-marcos-perez)]
        - [Shipwell](https://shipwell.com/) [[@zacharycarter](https://github.com/zacharycarter)]
        - [strongDM](https://www.strongdm.com/) [[@mrtns](https://github.com/mrtns)]
        - [Thinkbumblebee](https://www.thinkbumblebee.com/) [[@dheerajsharma21]](https://github.com/dheerajsharma21)
        - [Zillow](https://www.zillow.com/) [[@nicholas-miles]](https://github.com/nicholas-miles)
        
        ## What is Amazon SageMaker Data Wrangler?
        
        **Amazon SageMaker Data Wrangler** is a new SageMaker Studio feature that has a similar name but has a different purpose than the **AWS Data Wrangler** open source project.
        
        - **AWS Data Wrangler** is open source, runs anywhere, and is focused on code.
        
        - **Amazon SageMaker Data Wrangler** is specific for the SageMaker Studio environment and is focused on a visual interface.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6, <3.10
Description-Content-Type: text/markdown
Provides-Extra: sqlserver
Provides-Extra: excel-py3.6
