To access code from a Python package, you can either import the entire package or its specific modules and sub-packages.

Python packages streamline many significant processes, like analyzing and visualizing data, building machine learning models, capturing unstructured data from the web, and processing image and text information efficiently.

1. NumPy

NumPy is the primary tool for scientific computing in Python. It combines the flexibility and simplicity of Python with the speed of languages like C and Fortran.

NumPy is used for:

• Advanced array operations (e.g. add, multiply, slice, reshape, index).
• Comprehensive mathematical functions.
• Random number generation.
• Linear algebra routines.
• Fourier transforms, etc.

With NumPy, you are getting the computational power of compiled code, while using accessible Python syntax. No wonder that there is a huge ecosystem of Python packages and libraries drawing on the power of NumPy. These include such popular packages as pandas, Seaborn, SciPy, OpenCV, and others.