NumPy vs Pandas: What are the differences?
Developers describe NumPy as "Fundamental package for scientific computing with Python". Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. On the other hand, Pandas is detailed 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.
NumPy and Pandas can be primarily classified as "Data Science" tools.
Some of the features offered by NumPy are:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
On the other hand, Pandas provides the following key features:
- 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
NumPy and Pandas are both open source tools. It seems that Pandas with 20K GitHub stars and 7.92K forks on GitHub has more adoption than NumPy with 10.9K GitHub stars and 3.64K GitHub forks.
Instacart, SendGrid, and Sighten are some of the popular companies that use Pandas, whereas NumPy is used by Instacart, SendGrid, and SweepSouth. Pandas has a broader approval, being mentioned in 73 company stacks & 46 developers stacks; compared to NumPy, which is listed in 62 company stacks and 32 developer stacks.
What is NumPy?
What is Pandas?
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What are the cons of using NumPy?
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
We utilize NumPy, SciPy, Pandas, and iPython Notebooks to power our analysis and analytics tools.