Pandas vs PyXLL: 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 PyXLL? The Python Add-In for Microsoft Excel. Integrate Python into Microsoft Excel Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python.
Works with all 3rd party and open source Python packages. No need to write any VBA!.
Pandas and PyXLL can be categorized as "Data Science" tools.
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, PyXLL provides the following key features:
- User Defined Functions: Write Excel worksheet functions in Python - no VBA required
- Ribbon Customization: Give your users a rich Excel native experience
- Macros: No need for VBA, access to the full Excel Object Model in Python
Pandas is an open source tool with 20.7K GitHub stars and 8.16K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
What is Pandas?
What is PyXLL?
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What are the cons of using Pandas?
What are the cons of using PyXLL?
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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