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AWS SDK for pandas (awswrangler)

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).

AWS SDK for pandas tracker

An AWS Professional Service open source initiative | aws-proserve-opensource@amazon.com

PyPi Conda Python Version Code style: ruff License

Checked with mypy Static Checking Documentation Status

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

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

Table of contents

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.

Read our docs or head to our latest tutorials to learn more.

⚠️ Ray is currently not available for Python 3.12. While AWS SDK for pandas supports Python 3.12, it cannot be used at scale.

Read The Docs

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:

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)