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It provides high-level APIs for S3 (Parquet, CSV, JSON, Excel), Athena, Redshift, Glue Catalog, Timestream, DynamoDB, OpenSearch, Neptune, and more — reading databases, landing data in a lake, and running SQL queries take only a line or two. With Modin and Ray, the same code can scale out to a cluster.\u003C\u002Fp>\n\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Value\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Platform\u003C\u002Ftd>\n\u003Ctd>Any Python environment (incl. Lambda, Glue, EMR, SageMaker)\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Integrations\u003C\u002Ftd>\n\u003Ctd>S3, Athena, Redshift, Glue, Timestream, DynamoDB, OpenSearch, Neptune and more\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>License\u003C\u002Ftd>\n\u003Ctd>Apache-2.0\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Cblockquote>\n\u003Cp>The official high-speed channel between pandas and AWS data services.\u003C\u002Fp>\n\u003C\u002Fblockquote>\n\u003Ch2 id=\"key-features\">Key Features\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>S3 data lake I\u002FO\u003C\u002Fstrong>: \u003Ccode>wr.s3.to_parquet\u003C\u002Fcode> \u002F \u003Ccode>read_parquet\u003C\u002Fcode> work with datasets on S3 and register Glue tables automatically\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Athena queries\u003C\u002Fstrong>: \u003Ccode>wr.athena.read_sql_query\u003C\u002Fcode> runs SQL and returns results straight into a DataFrame\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Redshift integration\u003C\u002Fstrong>: connections from Glue Catalog, with query, COPY\u002FUNLOAD, and upsert support\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Multiple databases\u003C\u002Fstrong>: bulk read\u002Fwrite for MySQL, PostgreSQL, SQL Server, and Oracle\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Timestream time series\u003C\u002Fstrong>: write and query between DataFrames and Amazon Timestream\u003C\u002Fli>\n\u003Cli>\u003Cstrong>DynamoDB and OpenSearch\u003C\u002Fstrong>: wrappers for key-value data and search clusters\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Scale-out execution\u003C\u002Fstrong>: distribute processing across clusters with Modin and Ray\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Fully typed\u003C\u002Fstrong>: type annotations throughout, checked with mypy, IDE-friendly\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"target-audience\">Target Audience\u003C\u002Fh2>\u003Cul>\n\u003Cli>Data engineers on AWS\u003C\u002Fli>\n\u003Cli>Data scientists analyzing with pandas\u003C\u002Fli>\n\u003Cli>Developers building ETL pipelines\u003C\u002Fli>\n\u003Cli>Teams running data jobs on Lambda \u002F Glue \u002F EMR\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"quick-start\">Quick Start\u003C\u002Fh2>\u003Col>\n\u003Cli>Install: \u003Ccode>pip install awswrangler\u003C\u002Fcode> (optional modules need extras like \u003Ccode>pip install 'awswrangler[redshift]'\u003C\u002Fcode>)\u003C\u002Fli>\n\u003Cli>Configure AWS credentials and start using:\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Cdiv class=\"code-block\">\u003Cpre>\u003Ccode class=\"hljs language-python\">import awswrangler as wr\nimport pandas as pd\n\ndf = pd.DataFrame({'id': [1, 2], 'value': ['foo', 'boo']})\n\n# Write to the data lake and register a Glue table\nwr.s3.to_parquet(df=df, path='s3:\u002F\u002Fbucket\u002Fdataset\u002F', dataset=True,\n                 database='my_db', table='my_table')\n\n# Query back into a DataFrame via Athena\ndf = wr.athena.read_sql_query('SELECT * FROM my_table', database='my_db')\u003C\u002Fcode>\u003C\u002Fpre>\u003C\u002Fdiv>\u003Col start=\"3\">\n\u003Cli>See the official docs and the tutorials directory of Notebooks for more\u003C\u002Fli>\n\u003C\u002Fol>\n\u003Ch2 id=\"core-advantages\">Core Advantages\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Minimal code\u003C\u002Fstrong>: operations that take dozens of boto3 lines usually take one or two\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Officially maintained\u003C\u002Fstrong>: service coverage and compatibility handled by the AWS team\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Local to cloud consistency\u003C\u002Fstrong>: one API moves seamlessly from notebooks to production pipelines\u003C\u002Fli>\n\u003C\u002Ful>\n\u003Ch2 id=\"system-requirements\">System Requirements\u003C\u002Fh2>\u003Ctable>\n\u003Cthead>\n\u003Ctr>\n\u003Cth>Item\u003C\u002Fth>\n\u003Cth>Requirement\u003C\u002Fth>\n\u003C\u002Ftr>\n\u003C\u002Fthead>\n\u003Ctbody>\u003Ctr>\n\u003Ctd>Language\u003C\u002Ftd>\n\u003Ctd>Python\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>OS\u003C\u002Ftd>\n\u003Ctd>Linux, macOS, Windows\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003Ctr>\n\u003Ctd>Dependencies\u003C\u002Ftd>\n\u003Ctd>AWS credentials with permissions for the target services\u003C\u002Ftd>\n\u003C\u002Ftr>\n\u003C\u002Ftbody>\u003C\u002Ftable>\n\u003Ch2 id=\"notes\">Notes\u003C\u002Fh2>\u003Cul>\n\u003Cli>\u003Cstrong>Optional modules since 3.0\u003C\u002Fstrong>: install extras explicitly, e.g. \u003Ccode>awswrangler[redshift]\u003C\u002Fcode> or \u003Ccode>awswrangler[mysql]\u003C\u002Fcode>; the default install includes core features only\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Incurs AWS costs\u003C\u002Fstrong>: Athena scans and S3 requests are billed by usage — watch data scan volume before querying\u003C\u002Fli>\n\u003Cli>\u003Cstrong>Lambda deployment\u003C\u002Fstrong>: an official Lambda Layer is provided, so no manual dependency packaging is needed\u003C\u002Fli>\n\u003C\u002Ful>\n",1793228994848]