awslabs/aws-data-wrangler

Pandas on AWS


repo name	awslabs/aws-data-wrangler
repo link	https://github.com/awslabs/aws-data-wrangler
homepage	https://aws-data-wrangler.readthedocs.io
language	Python
size (curr.)	3524 kB
stars (curr.)	617
created	2019-02-26
license	Apache License 2.0

AWS Data Wrangler

Pandas on AWS

NOTE

We just released a new major version 1.0 with breaking changes. Please make sure that all your old projects has dependencies frozen on the desired version (e.g. pip install awswrangler==0.3.2).

AWS Data Wrangler

Source	Downloads	Page	Installation Command
PyPi		Link	`pip install awswrangler`
Conda		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)