Pandas on AWS.
Project description
AWS Data Wrangler
Pandas on AWS
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")
# Get Redshift connection (SQLAlchemy) from Glue and retrieving data from Redshift Spectrum
engine = wr.catalog.get_engine("my-redshift-connection")
df = wr.db.read_sql_query("SELECT * FROM external_schema.my_table", con=engine)
# Creating QuickSight Data Source and Dataset to reflect our new table
wr.quicksight.create_athena_data_source("athena-source", allowed_to_manage=["username"])
wr.quicksight.create_athena_dataset(
name="my-dataset",
database="my_db",
table="my_table",
data_source_name="athena-source",
allowed_to_manage=["username"]
)
# Get MySQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into MySQL
engine = wr.catalog.get_engine("my-mysql-connection")
wr.db.to_sql(df, engine, schema="test", name="my_table")
# Get PostgreSQL connection (SQLAlchemy) from Glue Catalog and LOAD the data into PostgreSQL
engine = wr.catalog.get_engine("my-postgresql-connection")
wr.db.to_sql(df, engine, schema="test", name="my_table")
Read The Docs
-
- 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
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
awswrangler-1.6.1.tar.gz
(74.8 kB
view hashes)
Built Distributions
awswrangler-1.6.1-py3.6.egg
(198.7 kB
view hashes)
Close
Hashes for awswrangler-1.6.1-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 020259751425ca41f94824afa5e180b07e5d62389765009cf542c411e4987998 |
|
MD5 | 48063c62da144e1742ab47886d418af0 |
|
BLAKE2b-256 | 78b2fec014441579b3e8de128a77014e684b7063f7b256fdef94cd46ff8dd6ca |