awswrangler 1.9.6

Pandas on AWS.

AWS Data Wrangler

Pandas on AWS

An AWS Professional Service open source initiative | [e-mail]

Source	Downloads	Installation Command
PyPi		pip install awswrangler
Conda		conda install -c conda-forge awswrangler

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Table of contents

• Quick Start
• Read The Docs
• Community Resources
• Who uses AWS Data Wrangler?

Quick Start

Installation command: pip install awswrangler

import awswrangler as wr
import pandas as pd

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 Redshift connection (SQLAlchemy) from Glue and retrieving data from Redshift Spectrum
engine = wr.catalog.get_engine("my-redshift-connection")
df = wr.db.read_sql_query("SELECT * FROM external_schema.my_table", con=engine)

# Get MySQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into MySQL
engine = wr.catalog.get_engine("my-mysql-connection")
wr.db.to_sql(df, engine, schema="test", name="my_table")

# Get PostgreSQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into PostgreSQL
engine = wr.catalog.get_engine("my-postgresql-connection")
wr.db.to_sql(df, engine, schema="test", name="my_table")

Read The Docs

• What is AWS Data Wrangler?
• Install
  • PyPi (pip)
  • Conda
  • AWS Lambda Layer
  • AWS Glue Python Shell Jobs
  • AWS Glue PySpark Jobs
  • Amazon SageMaker Notebook
  • Amazon SageMaker Notebook Lifecycle
  • EMR
  • From source
• Tutorials
  • 001 - Introduction
  • 002 - Sessions
  • 003 - Amazon S3
  • 004 - Parquet Datasets
  • 005 - Glue Catalog
  • 006 - Amazon Athena
  • 007 - Databases (Redshift, MySQL and PostgreSQL)
  • 008 - Redshift - Copy & Unload.ipynb
  • 009 - Redshift - Append, Overwrite and Upsert
  • 010 - Parquet Crawler
  • 011 - CSV Datasets
  • 012 - CSV Crawler
  • 013 - Merging Datasets on S3
  • 014 - Schema Evolution
  • 015 - EMR
  • 016 - EMR & Docker
  • 017 - Partition Projection
  • 018 - QuickSight
  • 019 - Athena Cache
  • 020 - Spark Table Interoperability
  • 021 - Global Configurations
  • 022 - Writing Partitions Concurrently
  • 023 - Flexible Partitions Filter
  • 024 - Athena Query Metadata
  • 025 - Redshift - Loading Parquet files with Spectrum
• API Reference
  • Amazon S3
  • AWS Glue Catalog
  • Amazon Athena
  • Databases (Amazon Redshift, PostgreSQL, MySQL)
  • Amazon EMR
  • Amazon CloudWatch Logs
  • Amazon QuickSight
  • AWS STS
• License
• Contributing
• Legacy Docs (pre-1.0.0)

Community Resources

Please send a Pull Request with your resource reference and @githubhandle.

• Optimize Python ETL by extending Pandas with AWS Data Wrangler [@igorborgest]
• Reading Parquet Files With AWS Lambda [@anand086]
• Transform AWS CloudTrail data using AWS Data Wrangler [@anand086]
• Getting started on AWS Data Wrangler and Athena [@dheerajsharma21]
• Simplifying Pandas integration with AWS data related services [@bvsubhash]

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 with your company name and @githubhandle if you may.

• Amazon
• AWS
• Cepsa [@alvaropc]
• Cognitivo [@msantino]
• Digio [@afonsomy]
• DNX [@DNXLabs]
• Funcional Health Tech [@webysther]
• LINE TV [@bryanyang0528]
• M4U [@Thiago-Dantas]
• OKRA Technologies [@JPFrancoia, @schot]
• Pier [@flaviomax]
• Pismo [@msantino]
• Serasa Experian [@andre-marcos-perez]
• Shipwell [@zacharycarter]
• Thinkbumblebee [@dheerajsharma21]
• Zillow [@nicholas-miles]