# aws/aws-sdk-pandas

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

Repository: https://github.com/aws/aws-sdk-pandas
Canonical: https://ross.abutalabs.com/products/aws-sdk-pandas
Homepage: https://aws-sdk-pandas.readthedocs.io
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, aws, pandas, apache-arrow, apache-parquet, data-engineering, etl, data-science, redshift, athena, lambda, aws-lambda, aws-glue, emr, amazon-athena, glue-catalog, mysql, amazon-sagemaker-notebook, modin, ray
Last push: 2026-08-26T14:16:39+00:00

## Health v2 (maintenance only)
Score: 94/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 84, longevity 100
- inputs: {"age_days": 2746, "days_push": 7, "days_rel": 30, "gap_med": 43.0, "n_releases_24m": 13}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4118, forks 742 (observed 2026-08-28T04:08:35.898882+00:00)

## What it is
AWS SDK for pandas (awswrangler) is a Python library that extends pandas with high-level APIs for reading and writing data across AWS services like S3, Athena, Glue, Redshift, Timestream, DynamoDB, and OpenSearch. It simplifies building data lakes and ETL pipelines by bridging pandas DataFrames with AWS analytics and storage services.

## Use cases
- read and write parquet datasets on S3 from pandas
- query Amazon Athena and get results as a pandas DataFrame
- load pandas DataFrames into Redshift
- build a data lake on S3 with Glue catalog integration
- run ETL jobs in AWS Lambda with pandas
- export data to DynamoDB or Timestream from pandas
- read CSV, JSON, and Excel files from S3 into pandas

## When to choose
- you work with pandas and need seamless AWS data lake integration
- you want to move data between S3, Athena, Redshift, and other AWS analytics services
- you build ETL pipelines in Python on AWS
- you need Glue Catalog-backed datasets with partitioning

## When to avoid
- you work outside AWS or with non-AWS data stores
- you need a general-purpose ORM or database driver rather than analytics integration
- your project does not use pandas or Arrow-based workflows

## Facets
- artifact type: library
- maturity: active
- function: etl, database, data-science, serialization
- domain: data-science, cloud-computing, databases, big-data
- platform: python, cloud, serverless
- tags: pandas, aws, athena, redshift, glue, s3, parquet, data-lake, awswrangler, apache-arrow, data-engineering

## Member repositories
- aws/aws-sdk-pandas (main) score 94

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:35.898882+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:23:08.120459+00:00, confidence not recorded.
  - readme: https://github.com/aws/aws-sdk-pandas (fetched 2026-08-28T04:08:35.898882+00:00, sha f248295b1f9f)
- Data as of 2026-08-30T08:39:29.467469+00:00.