awswrangler 2.1.0

Pandas on AWS.

AWS Data Wrangler

Pandas on AWS

An AWS Professional Service open source initiative | [e-mail]

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

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Table of contents

• Quick Start
• Read The Docs
• Community Resources
• Logging
• Who uses AWS Data Wrangler?
• Amazon SageMaker Data Wrangler?

Quick Start

Installation command: pip install 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

• What is AWS Data Wrangler?
• Install
  • PyPi (pip)
  • Conda
  • AWS Lambda Layer
  • AWS Glue Python Shell Jobs
  • AWS Glue PySpark Jobs
  • Amazon SageMaker Notebook
  • Amazon SageMaker Notebook Lifecycle
  • EMR
  • From source
• Tutorials
  • 001 - Introduction
  • 002 - Sessions
  • 003 - Amazon S3
  • 004 - Parquet Datasets
  • 005 - Glue Catalog
  • 006 - Amazon Athena
  • 007 - Databases (Redshift, MySQL and PostgreSQL)
  • 008 - Redshift - Copy & Unload.ipynb
  • 009 - Redshift - Append, Overwrite and Upsert
  • 010 - Parquet Crawler
  • 011 - CSV Datasets
  • 012 - CSV Crawler
  • 013 - Merging Datasets on S3
  • 014 - Schema Evolution
  • 015 - EMR
  • 016 - EMR & Docker
  • 017 - Partition Projection
  • 018 - QuickSight
  • 019 - Athena Cache
  • 020 - Spark Table Interoperability
  • 021 - Global Configurations
  • 022 - Writing Partitions Concurrently
  • 023 - Flexible Partitions Filter
  • 024 - Athena Query Metadata
  • 025 - Redshift - Loading Parquet files with Spectrum
  • 026 - Amazon Timestream
  • 027 - Amazon Timestream 2
• API Reference
  • Amazon S3
  • AWS Glue Catalog
  • Amazon Athena
  • Amazon Redshift
  • PostgreSQL
  • MySQL
  • Amazon Timestream
  • Amazon EMR
  • Amazon CloudWatch Logs
  • Amazon QuickSight
  • AWS STS
  • AWS Secrets Manager
• License
• Contributing
• Legacy Docs (pre-1.0.0)

Community Resources

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

• Optimize Python ETL by extending Pandas with AWS Data Wrangler [@igorborgest]
• Reading Parquet Files With AWS Lambda [@anand086]
• Transform AWS CloudTrail data using AWS Data Wrangler [@anand086]
• Rename Glue Tables using AWS Data Wrangler [@anand086]
• Getting started on AWS Data Wrangler and Athena [@dheerajsharma21]
• Simplifying Pandas integration with AWS data related services [@bvsubhash]

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.

• Amazon
• AWS
• Cepsa [@alvaropc]
• Cognitivo [@msantino]
• Digio [@afonsomy]
• DNX [@DNXLabs]
• Funcional Health Tech [@webysther]
• Informa Markets [@mateusmorato]
• LINE TV [@bryanyang0528]
• M4U [@Thiago-Dantas]
• nrd.io [@mrtns]
• OKRA Technologies [@JPFrancoia, @schot]
• Pier [@flaviomax]
• Pismo [@msantino]
• ringDNA [@msropp]
• Serasa Experian [@andre-marcos-perez]
• Shipwell [@zacharycarter]
• strongDM [@mrtns]
• Thinkbumblebee [@dheerajsharma21]
• Zillow [@nicholas-miles]

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.