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Pandas vs SciPy: What are the differences?
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; SciPy: Scientific Computing Tools for Python. Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Pandas and SciPy belong to "Data Science Tools" category of the tech stack.
Pandas and SciPy are both open source tools. It seems that Pandas with 20.2K GitHub stars and 8K forks on GitHub has more adoption than SciPy with 6.01K GitHub stars and 2.85K GitHub forks.
According to the StackShare community, Pandas has a broader approval, being mentioned in 73 company stacks & 49 developers stacks; compared to SciPy, which is listed in 12 company stacks and 4 developer stacks.
Pros of Pandas
- Easy data frame management21
- Extensive file format compatibility1