NumPy vs AWS Data Wrangler: What are the differences?
Developers describe NumPy as "Fundamental package for scientific computing with Python". Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. On the other hand, AWS Data Wrangler is detailed as "Move pandas/spark dataframes across AWS services". It is a utility belt to handle data on AWS. It aims to fill a gap between AWS Analytics Services (Glue, Athena, EMR, Redshift) and the most popular Python data libraries (Pandas, Apache Spark).
NumPy and AWS Data Wrangler can be primarily classified as "Data Science" tools.
NumPy and AWS Data Wrangler are both open source tools. It seems that NumPy with 12.6K GitHub stars and 4.11K forks on GitHub has more adoption than AWS Data Wrangler with 378 GitHub stars and 35 GitHub forks.
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