Pandas vs Dataform: What are the differences?
Developers describe Pandas as "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. On the other hand, Dataform is detailed as "A framework for managing SQL based data operations". Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
Pandas and Dataform are primarily classified as "Data Science" and "Business Intelligence" tools respectively.
Some of the features offered by Pandas are:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
On the other hand, Dataform provides the following key features:
- Version ontrol
- Notifications and logging
Pandas and Dataform are both open source tools. It seems that Pandas with 22.4K GitHub stars and 8.92K forks on GitHub has more adoption than Dataform with 117 GitHub stars and 11 GitHub forks.
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