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This is a legacy version, please check out the latest one here.

DataFrames on AWSΒΆ
- Install
- Read the Tutorials
- Examples
- Pandas
- Writing Pandas Dataframe to S3 + Glue Catalog
- Writing Pandas Dataframe to S3 as Parquet encrypting with a KMS key
- Reading from AWS Athena to Pandas
- Reading from AWS Athena to Pandas in chunks (For memory restrictions)
- Reading from AWS Athena to Pandas with the blazing fast CTAS approach
- Reading from Glue Catalog (Parquet) to Pandas
- Reading from S3 (Parquet) to Pandas
- Reading from S3 (CSV) to Pandas
- Reading from S3 (CSV) to Pandas in chunks (For memory restrictions)
- Reading from CloudWatch Logs Insights to Pandas
- Typical Pandas ETL
- Loading Pandas Dataframe to Redshift
- Extract Redshift query to Pandas DataFrame
- Loading Pandas Dataframe to Aurora (MySQL/PostgreSQL)
- Extract Aurora query to Pandas DataFrame (MySQL)
- PySpark
- General
- Pandas
- Diving Deep
- awswrangler package
- Submodules
- awswrangler.athena module
- awswrangler.aurora module
- awswrangler.cloudwatchlogs module
- awswrangler.data_types module
- awswrangler.dynamodb module
- awswrangler.emr module
- awswrangler.exceptions module
- awswrangler.glue module
- awswrangler.pandas module
- awswrangler.redshift module
- awswrangler.s3 module
- awswrangler.sagemaker module
- awswrangler.session module
- awswrangler.spark module
- awswrangler.utils module
- Module contents
- Submodules
- Contributing
- License