awswrangler 3.4.1

Pandas on AWS.

AWS SDK for pandas (awswrangler)

AWS Data Wrangler is now AWS SDK for pandas (awswrangler). We’re changing the name we use when we talk about the library, but everything else will stay the same. You’ll still be able to install using pip install awswrangler and you won’t need to change any of your code. As part of this change, we’ve moved the library from AWS Labs to the main AWS GitHub organisation but, thanks to the GitHub’s redirect feature, you’ll still be able to access the project by its old URLs until you update your bookmarks. Our documentation has also moved to aws-sdk-pandas.readthedocs.io, but old bookmarks will redirect to the new site.

Pandas on AWS

Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).

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

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

⚠️ Starting version 3.0, optional modules must be installed explicitly:
➡️pip install 'awswrangler[redshift]'

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Table of contents

• Quick Start
• At Scale
• Read The Docs
• Getting Help
• Community Resources
• Logging
• Who uses AWS SDK for pandas?

Quick Start

Installation command: pip install awswrangler

⚠️ Starting version 3.0, optional modules must be installed explicitly:
➡️pip install 'awswrangler[redshift]'

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
""")

At scale

AWS SDK for pandas can also run your workflows at scale by leveraging Modin and Ray. Both projects aim to speed up data workloads by distributing processing over a cluster of workers.

The quickest way to get started is to use AWS Glue with Ray. Read our docs, our blogs (1/2), or head to our latest tutorials to discover even more features.

Read The Docs

• What is AWS SDK for pandas?
• 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
• At scale
  • Getting Started
  • Supported APIs
  • Resources
• Tutorials
  • 001 - Introduction
  • 002 - Sessions
  • 003 - Amazon S3
  • 004 - Parquet Datasets
  • 005 - Glue Catalog
  • 006 - Amazon Athena
  • 007 - Databases (Redshift, MySQL, PostgreSQL, SQL Server and Oracle)
  • 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
  • 026 - Amazon Timestream
  • 027 - Amazon Timestream 2
  • 028 - Amazon DynamoDB
  • 029 - S3 Select
  • 030 - Data Api
  • 031 - OpenSearch
  • 032 - Lake Formation Governed Tables
  • 033 - Amazon Neptune
  • 034 - Distributing Calls Using Ray
  • 035 - Distributing Calls on Ray Remote Cluster
  • 036 - Distributing Calls with Glue Interactive Sessions on Ray
  • 037 - Glue Data Quality
  • 038 - OpenSearch Serverless
  • 039 - Athena Iceberg
  • 040 - EMR Serverless
  • 041 - Apache Spark on Amazon Athena
• API Reference
  • Amazon S3
  • AWS Glue Catalog
  • Amazon Athena
  • AWS Lake Formation
  • Amazon Redshift
  • PostgreSQL
  • MySQL
  • SQL Server
  • Oracle
  • Data API Redshift
  • Data API RDS
  • OpenSearch
  • AWS Glue Data Quality
  • Amazon Neptune
  • DynamoDB
  • Amazon Timestream
  • Amazon EMR
  • Amazon CloudWatch Logs
  • Amazon Chime
  • Amazon QuickSight
  • AWS STS
  • AWS Secrets Manager
  • Global Configurations
  • Distributed - Ray
• License
• Contributing

Getting Help

The best way to interact with our team is through GitHub. You can open an issue and choose from one of our templates for bug reports, feature requests... You may also find help on these community resources:

• The #aws-sdk-pandas Slack channel
• Ask a question on Stack Overflow and tag it with awswrangler
• Runbook for AWS SDK for pandas with Ray

Community Resources

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

• YouTube channel [@AdrianoNicolucci]
• Optimize Python ETL by extending Pandas with AWS SDK for pandas [@igorborgest]
• Reading Parquet Files With AWS Lambda [@anand086]
• Transform AWS CloudTrail data using AWS SDK for pandas [@anand086]
• Rename Glue Tables using AWS SDK for pandas [@anand086]
• Getting started on AWS SDK for pandas and Athena [@dheerajsharma21]
• Simplifying Pandas integration with AWS data related services [@bvsubhash]
• Build an ETL pipeline using AWS S3, Glue and Athena [@taupirho]

Logging

Enabling internal logging examples:

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:

import logging
logging.getLogger("awswrangler").setLevel(logging.DEBUG)

Who uses AWS SDK for pandas?

Knowing which companies are using this library is important to help prioritize the project internally. If you would like us to include your company’s name and/or logo in the README file to indicate that your company is using the AWS SDK for pandas, please raise a "Support Us" issue. If you would like us to display your company’s logo, please raise a linked pull request to provide an image file for the logo. Note that by raising a Support Us issue (and related pull request), you are granting AWS permission to use your company’s name (and logo) for the limited purpose described here and you are confirming that you have authority to grant such permission.

• Amazon
• AWS
• Cepsa [@alvaropc]
• Cognitivo [@msantino]
• Digio [@afonsomy]
• DNX [@DNXLabs]
• Fortescue Future Industries [@spencervoorend]
• Funcional Health Tech [@webysther]
• Funding Circle [@pfig]
• Infomach
• Informa Markets [@mateusmorato]
• LINE TV [@bryanyang0528]
• LogicalCube [@zolabud]
• Magnataur [@brianmingus2]
• M4U [@Thiago-Dantas]
• NBCUniversal [@vibe]
• nrd.io [@mrtns]
• OKRA Technologies [@JPFrancoia, @schot]
• Pier [@flaviomax]
• Pismo [@msantino]
• ringDNA [@msropp]
• Serasa Experian [@andre-marcos-perez]
• Shipwell [@zacharycarter]
• strongDM [@mrtns]
• Thinkbumblebee [@dheerajsharma21]
• VTEX [@igorborgest]
• Zillow [@nicholas-miles]