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Bumping version to 0.3.0
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‎README.md‎

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> DataFrames on AWS
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[![Release](https://img.shields.io/badge/release-0.2.6-brightgreen.svg)](https://pypi.org/project/awswrangler/)
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[![Release](https://img.shields.io/badge/release-0.3.0-brightgreen.svg)](https://pypi.org/project/awswrangler/)
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[![Downloads](https://img.shields.io/pypi/dm/awswrangler.svg)](https://pypi.org/project/awswrangler/)
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[![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7-brightgreen.svg)](https://pypi.org/project/awswrangler/)
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[![Documentation Status](https://readthedocs.org/projects/aws-data-wrangler/badge/?version=latest)](https://aws-data-wrangler.readthedocs.io/en/latest/?badge=latest)
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[![Coverage](https://img.shields.io/badge/coverage-89%25-brightgreen.svg)](https://pypi.org/project/awswrangler/)
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[![Average time to resolve an issue](http://isitmaintained.com/badge/resolution/awslabs/aws-data-wrangler.svg)](http://isitmaintained.com/project/awslabs/aws-data-wrangler "Average time to resolve an issue")
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[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
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**[Read the Docs!](https://aws-data-wrangler.readthedocs.io)**
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## [Read the Docs](https://aws-data-wrangler.readthedocs.io)
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**[Read the Tutorials](https://github.com/awslabs/aws-data-wrangler/tree/master/tutorials): [Catalog & Metadata](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/catalog_and_metadata.ipynb) | [Athena Nested](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/athena_nested.ipynb) | [S3 Write Modes](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/s3_write_modes.ipynb)**
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## [Read the Tutorials](https://github.com/awslabs/aws-data-wrangler/tree/master/tutorials)
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- [Catalog & Metadata](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/catalog_and_metadata.ipynb)
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- [Athena Nested](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/athena_nested.ipynb)
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- [S3 Write Modes](https://github.com/awslabs/aws-data-wrangler/blob/master/tutorials/s3_write_modes.ipynb)
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---
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*Contents:* **[Use Cases](#Use-Cases)** | **[Installation](#Installation)** | **[Examples](#Examples)** | **[Diving Deep](#Diving-Deep)** | **[Step By Step](#Step-By-Step)** | **[Contributing](#Contributing)**
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---
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## Contents
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- [Use Cases](#Use-Cases)
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- [Installation](#Installation)
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- [Examples](#Examples)
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- [Diving Deep](#Diving-Deep)
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- [Step By Step](#Step-By-Step)
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- [Contributing](#Contributing)
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## Use Cases
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### Pandas
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* Pandas -> Parquet (S3) (Parallel)
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* Pandas -> CSV (S3) (Parallel)
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* Pandas -> Glue Catalog Table
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* Pandas -> Athena (Parallel)
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* Pandas -> Redshift (Append/Overwrite/Upsert) (Parallel)
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* Pandas -> Aurora (MySQL/PostgreSQL) (Append/Overwrite) (Via S3) (NEW :star:)
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* Parquet (S3) -> Pandas (Parallel)
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* CSV (S3) -> Pandas (One shot or Batching)
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* Glue Catalog Table -> Pandas (Parallel)
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* Athena -> Pandas (One shot, Batching or Parallel)
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* Redshift -> Pandas (Parallel)
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* CloudWatch Logs Insights -> Pandas
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* Aurora -> Pandas (MySQL) (Via S3) (NEW :star:)
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* Encrypt Pandas Dataframes on S3 with KMS keys
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* Glue Databases Metadata -> Pandas (Jupyter output compatible)
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* Glue Table Metadata -> Pandas (Jupyter output compatible)
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| FROM | TO | Features |
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|--------------------------|-----------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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| Pandas DataFrame | Amazon S3 | Parquet, CSV, Partitions, Parallelism, Overwrite/Append/Partitions-Upsert modes,<br>KMS Encryption, Glue Metadata (Athena, Spectrum, Spark, Hive, Presto) |
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| Amazon S3 | Pandas DataFrame| Parquet (Pushdown filters), CSV, Partitions, Parallelism,<br>KMS Encryption, Multiple files |
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| Amazon Athena | Pandas DataFrame| Workgroups, S3 output path, Encryption, and two different engines:<br><br>- ctas_approach=False **->** Batching and restrict memory environments<br>- ctas_approach=True **->** Blazing fast, parallelism and enhanced data types |
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| Pandas DataFrame | Amazon Redshift | Blazing fast using parallel parquet on S3 behind the scenes<br>Append/Overwrite/Upsert modes |
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| Amazon Redshift | Pandas DataFrame| Blazing fast using parallel parquet on S3 behind the scenes |
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| Pandas DataFrame | Amazon Aurora | Supported engines: MySQL, PostgreSQL<br>Blazing fast using parallel CSV on S3 behind the scenes<br>Append/Overwrite modes |
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| Amazon Aurora | Pandas DataFrame| Supported engines: MySQL<br>Blazing fast using parallel CSV on S3 behind the scenes |
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| CloudWatch Logs Insights | Pandas DataFrame| Query results |
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| Glue Catalog | Pandas DataFrame| List and get Tables details. Good fit with Jupyter Notebooks. |
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### PySpark
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* PySpark -> Redshift (Parallel)
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* Register Glue table from Dataframe stored on S3
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* Flatten nested DataFrames
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| FROM | TO | Features |
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|-----------------------------|---------------------------|------------------------------------------------------------------------------------------|
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| PySpark DataFrame | Amazon Redshift | Blazing fast using parallel parquet on S3 behind the scenesAppend/Overwrite/Upsert modes |
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| PySpark DataFrame | Glue Catalog | Register Parquet or CSV DataFrame on Glue Catalog |
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| Nested PySpark<br>DataFrame | Flat PySpark<br>DataFrames| Flatten structs and break up arrays in child tables |
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### General
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* List S3 objects (Parallel)
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* Delete S3 objects (Parallel)
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* Delete listed S3 objects (Parallel)
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* Delete NOT listed S3 objects (Parallel)
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* Copy listed S3 objects (Parallel)
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* Get the size of S3 objects (Parallel)
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* Get CloudWatch Logs Insights query results
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* Load partitions on Athena/Glue table (repair table)
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* Create EMR cluster (For humans)
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* Terminate EMR cluster
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* Get EMR cluster state
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* Submit EMR step(s) (For humans)
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* Get EMR step state
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* Get EMR step state
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* Athena query to receive the result as python primitives (*Iterable[Dict[str, Any]*)
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* Load and Unzip SageMaker jobs outputs
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* Load and Unzip SageMaker models
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* Redshift -> Parquet (S3)
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* Aurora -> CSV (S3) (MySQL) (NEW :star:)
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* Get Glue Metadata
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| Feature | Details |
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|---------------------------------------------|-------------------------------------|
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| List S3 objects | e.g. wr.s3.list_objects("s3://...") |
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| Delete S3 objects | Parallel |
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| Delete listed S3 objects | Parallel |
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| Delete NOT listed S3 objects | Parallel |
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| Copy listed S3 objects | Parallel |
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| Get the size of S3 objects | Parallel |
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| Get CloudWatch Logs Insights query results | |
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| Load partitions on Athena/Glue table | Through "MSCK REPAIR TABLE" |
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| Create EMR cluster | "For humans" |
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| Terminate EMR cluster | "For humans" |
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| Get EMR cluster state | "For humans" |
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| Submit EMR step(s) | "For humans" |
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| Get EMR step state | "For humans" |
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| Query Athena to receive python primitives | Returns *Iterable[Dict[str, Any]* |
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| Load and Unzip SageMaker jobs outputs | |
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| Dump Amazon Redshift as Parquet files on S3 | |
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| Dump Amazon Aurora as CSV files on S3 | Only for MySQL engine |
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## Installation
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‎awswrangler/__version__.py‎

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__title__ = "awswrangler"
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__description__ = "DataFrames on AWS."
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__version__ = "0.2.6"
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__version__ = "0.3.0"
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__license__ = "Apache License 2.0"

‎awswrangler/pandas.py‎

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elif pandas_type == "date":
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dataframe[col] = pd.to_datetime(dataframe[col]).dt.date.replace(to_replace={pd.NaT: None})
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else:
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dataframe[col] = dataframe[col].astype(pandas_type, skipna=True)
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dataframe[col] = dataframe[col].astype(pandas_type)
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return dataframe
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@staticmethod

‎docs/source/api/awswrangler.dynamodb.rst‎

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awswrangler.dynamodb module
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===========================
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.. automodule:: awswrangler.dynamodb
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/api/awswrangler.rst‎

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awswrangler.aurora
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awswrangler.cloudwatchlogs
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awswrangler.data_types
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awswrangler.dynamodb
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awswrangler.emr
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awswrangler.exceptions
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awswrangler.glue

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