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Nov 7, 2020

AWS Data Wrangler

Pandas on AWS

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

AWS Data Wrangler

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

Release Python Version Code style: black License

Checked with mypy Coverage Static Checking Documentation Status

Source Downloads Installation Command
PyPi PyPI Downloads pip install awswrangler
Conda Conda Downloads 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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Table of contents

Quick Start

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
""")

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:

Community Resources

Please send a Pull Request with your resource reference and @githubhandle.

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)

Who uses AWS Data Wrangler?

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 Data Wrangler, please raise a "Support Data Wrangler" 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 Data Wrangler 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.

What is Amazon SageMaker Data Wrangler?

Amazon SageMaker Data Wrangler is a new SageMaker Studio feature that has a similar name but has a different purpose than the AWS Data Wrangler open source project.

  • AWS Data Wrangler is open source, runs anywhere, and is focused on code.

  • Amazon SageMaker Data Wrangler is specific for the SageMaker Studio environment and is focused on a visual interface.