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Programming language: Python
License: Apache License 2.0
Tags: Data Analysis    
Latest version: v2.0.0

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README

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](docs/source/_static/logo2.png?raw=true "AWS Data Wrangler")

An AWS Professional Service open source initiative | [email protected]

Release Python Version Code style: black License

Checked with mypy Coverage Static Checking Build Status 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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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

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.

Please send a Pull Request with your company name and @githubhandle if you may.

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.


*Note that all licence references and agreements mentioned in the AWS Data Wrangler README section above are relevant to that project's source code only.