10 Essential Python Libraries for Data Professionals
Indispensable additions to your Python toolkit
Over the last few years, Python has seen a huge surge in popularity and is fast becoming the language of choice for many budding data professionals. It is, without doubt, one of the fastest-growing and most in-demand programming languages, which is no surprise given its relatively simple and easy-to-learn syntax, extensive collection of libraries, incredible community support, and all-around versatility.
If you’re looking to step up your game in data analysis with Python, then the following list of 10 libraries is a good place to start. Ranging from data manipulation to data visualization and statistical computation, you’ll find these libraries an essential addition to your Python toolkit.
1. Numpy
Numpy, which stands for “numerical python”, is the fundamental scientific computing package for Python. Numpy offers a comprehensive list of mathematical functions which include linear algebra routines, random number generators, basic statistical calculations, Fourier transforms, and many more. Several commonly used Python libraries, including a few in this list, are built on top of Numpy. At the core of Numpy is the array, which offers 1-dimensional, 2-dimensional, or multi-dimensional vectorization, indexing, and broadcasting…
