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 | [email protected]
| Source | Downloads | Installation Command |
|---|---|---|
| PyPi | pip install awswrangler |
|
| Conda |  |
conda install -c conda-forge awswrangler |
⚠️ For platforms without PyArrow 3 support (e.g. EMR, Glue PySpark Job, MWAA):
➡️pip install pyarrow==2 awswrangler
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Installation command: pip install awswrangler
⚠️ For platforms without PyArrow 3 support (e.g. EMR, Glue PySpark Job, MWAA):
➡️pip install pyarrow==2 awswrangler
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
""")- What is AWS SDK for pandas?
- Install
- 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
- 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
- Amazon Neptune
- DynamoDB
- Amazon Timestream
- Amazon EMR
- Amazon CloudWatch Logs
- Amazon Chime
- Amazon QuickSight
- AWS STS
- AWS Secrets Manager
- Global Configurations
- License
- Contributing
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
Please send a Pull Request with your resource reference and @githubhandle.
- 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]
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)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]
- Infomach
- Informa Markets [@mateusmorato]
- LINE TV [@bryanyang0528]
- 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]