awswrangler
Pandas on AWS.
Decision gist · record as of 2026-08-14
Yes. Active maintenance, permissive license, low install friction, no known vulnerabilities, and top-1000 popularity make it a safe choice. Install it if you work with pandas and AWS data services—it eliminates boilerplate and integrates naturally into existing pandas workflows. Be aware that optional service modules require explicit installation and that AWS credentials must be pre-configured.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- AWS credentials must be configured (via environment variables, IAM role, or AWS CLI config).
- Optional modules (e.g., redshift, timestream) require explicit installation via extras syntax.
- Low friction: pure Python wheel with 8 runtime dependencies (boto3, botocore, numpy, pandas, pyarrow, packaging, setuptools, typing-extensions).
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most enterprise and open-source projects.
last release 2026-08-03 (11 days) · last repo commit 2026-08-13 · 4,116 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 88,078,486 downloads/mo, #387 on PyPI
Alternatives
Verify before relying
pip install awswrangler
import awswrangler as wr
import pandas as pd
df = pd.DataFrame({"id": [1, 2], "value": ["foo", "boo"]})
wr.s3.to_parquet(df=df, path="s3://bucket/dataset/", dataset=True)
df = wr.s3.read_parquet("s3://bucket/dataset/", dataset=True)- Performance characteristics when operating on large datasets or at distributed scale via Modin/Ray.
- Specific AWS service version compatibility and any breaking changes in version 3.0+ optional module requirement.
What it is and what it does
AWS SDK for pandas (awswrangler) is a bridge between pandas DataFrames and AWS data services. It wraps boto3 calls into pandas-native methods, so you can read from and write to S3, Athena, Redshift, DynamoDB, Timestream, and other AWS services using familiar DataFrame syntax instead of constructing raw API requests. The package handles format conversions (Parquet, CSV, JSON, Excel), manages Glue Catalog metadata, and abstracts connection pooling and credential handling.
It's designed for data engineers and analysts working in AWS environments who want to avoid boilerplate AWS SDK code. The core dependencies are boto3, botocore, pandas, pyarrow, and numpy. Starting in version 3.0, service-specific modules (Redshift, Timestream, etc.) must be installed explicitly via extras. The package supports distributed execution via Modin and Ray for workloads that exceed single-machine capacity.
Use it for
- Load a Parquet dataset from S3 into a pandas DataFrame and perform local analysis without downloading raw files.
- Write a DataFrame to S3 as a partitioned Parquet dataset and register it in the Glue Catalog for Athena queries.
- Query an Athena table directly into a DataFrame using SQL, bypassing manual result pagination.
- Read from and write to Redshift using Spectrum or direct connections, integrating data warehouse operations into pandas workflows.
- Ingest time-series data into Amazon Timestream from a pandas DataFrame with automatic dimension and measure mapping.
- Crawl and merge multiple CSV or Parquet files from S3 into a single DataFrame with schema evolution handling.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active maintenance, permissive license, low install friction, no known vulnerabilities, and top-1000 popularity make it a safe choice. Install it if you work with pandas and AWS data services—it eliminates boilerplate and integrates naturally into existing pandas workflows. Be aware that optional service modules require explicit installation and that AWS credentials must be pre-configured.
Install
awswrangler on PyPI
Before you install
Low friction: pure Python wheel with 8 runtime dependencies (boto3, botocore, numpy, pandas, pyarrow, packaging, setuptools, typing-extensions). Actively maintained—last commit 2026-08-13, 4116 repository stars, released 11 days ago.
AWS credentials must be configured (via environment variables, IAM role, or AWS CLI config). Optional modules (e.g., redshift, timestream) require explicit installation via extras syntax.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most enterprise and open-source projects.
Quickstart
pip install awswrangler
import awswrangler as wr
import pandas as pd
df = pd.DataFrame({"id": [1, 2], "value": ["foo", "boo"]})
wr.s3.to_parquet(df=df, path="s3://bucket/dataset/", dataset=True)
df = wr.s3.read_parquet("s3://bucket/dataset/", dataset=True)Verify before relying
- Performance characteristics when operating on large datasets or at distributed scale via Modin/Ray.
- Specific AWS service version compatibility and any breaking changes in version 3.0+ optional module requirement.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4.0,>=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesboto3botocorenumpypackagingpandaspyarrowsetuptoolstyping-extensions |
| Maintenance | Actively maintained 11 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 88,078,486 / month, #387 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: awswrangler-3.17.1-py3-none-any.whl
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