Skip to content

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).

License

Notifications You must be signed in to change notification settings

aws/aws-sdk-pandas

Repository files navigation

AWS Data Wrangler

Pandas on AWS

AWS Data Wrangler

Release Python Version Code style: black License

Checked with mypy Coverage Static Checking Documentation Status

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

Quick Start

Install the Wrangler with: pip install awswrangler

import awswrangler as wr
import pandas as pd

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")

# Getting Redshift connection (SQLAlchemy) from Glue Catalog Connections
engine = wr.catalog.get_engine("my-redshift-connection")

# Retrieving the data from Amazon Redshift Spectrum
df = wr.db.read_sql_query("SELECT * FROM external_schema.my_table", con=engine)