Anaconda vs Pandas: What are the differences?
What is Anaconda? The Enterprise Data Science Platform for Data Scientists, IT Professionals and Business Leaders. A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
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.
Anaconda and Pandas belong to "Data Science Tools" category of the tech stack.
Pandas is an open source tool with 20.2K GitHub stars and 8K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
According to the StackShare community, Pandas has a broader approval, being mentioned in 73 company stacks & 49 developers stacks; compared to Anaconda, which is listed in 4 company stacks and 5 developer stacks.
What is Anaconda?
What is Pandas?
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Why do developers choose Anaconda?
What are the cons of using Anaconda?
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