AWS SDK for pandas (awswrangler)
Pandas on AWS
Easy integration with Athena, Glue, Redshift, Timestream, OpenSearch, Neptune, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
An AWS Professional Service open source initiative | [email protected]
| Source |
Downloads |
Installation Command |
| PyPi |
 |
pip install awswrangler |
| Conda |
 |
conda install -c conda-forge awswrangler |
⚠️ Starting version 3.0, optional modules must be installed explicitly:
➡️pip install 'awswrangler[redshift]'
Table of contents
Quick Start
Installation command: pip install awswrangler
⚠️ Starting version 3.0, optional modules must be installed explicitly:
➡️pip install 'awswrangler[redshift]'
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
""")
At scale
AWS SDK for pandas can also run your workflows at scale by leveraging Modin and Ray. Both projects aim to speed up data workloads by distributing processing over a cluster of workers.
Read our docs or head to our latest tutorials to learn more.
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:
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)
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