Pandas vs AWS Data Wrangler: What are the differences?
What is Pandas? High-performance, easy-to-use data structures and data analysis tools for the Python programming language. Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
What is AWS Data Wrangler? 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).
Pandas and AWS Data Wrangler belong to "Data Science Tools" category of the tech stack.
Pandas and AWS Data Wrangler are both open source tools. It seems that Pandas with 22.8K GitHub stars and 9.1K forks on GitHub has more adoption than AWS Data Wrangler with 378 GitHub stars and 35 GitHub forks.
What is AWS Data Wrangler?
What is Pandas?
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Why do developers choose AWS Data Wrangler?
What are the cons of using AWS Data Wrangler?
What are the cons of using Pandas?
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Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead