Python - SciPy


The SciPy library of Python is built to work with NumPy arrays and provides many user-friendly and efficient numerical practices such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install and are free of charge. NumPy and SciPy are easy to use, but powerful enough to depend on by some of the world's leading scientists and engineers.

SciPy Sub-packages

SciPy is organized into sub-packages covering different scientific computing domains. These are summarized in the following table −

scipy.constants Physical and mathematical constants
scipy.fftpack Fourier transform
scipy.integrate Integration routines
scipy.interpolate Interpolation Data input and output
scipy.linalg Linear algebra routines
scipy.optimize Optimization
scipy.signal Signal processing
scipy.sparse Sparse matrices
scipy.spatial Spatial data structures and algorithms
scipy.special Any special mathematical functions
scipy.stats Statistics

Data Structure

The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for Linear Algebra, Fourier Transforms and Random Number Generation, but not with the generality of the equivalent functions in SciPy.

We will see lots of examples on using SciPy library of python in Data science work in the next chapters.

Useful Video Courses


Python Online Training

187 Lectures 17.5 hours

Malhar Lathkar


Python Essentials Online Training

55 Lectures 8 hours

Arnab Chakraborty


Learn Python Programming in 100 Easy Steps

136 Lectures 11 hours

In28Minutes Official


Python with Data Science

Best Seller

75 Lectures 13 hours

Eduonix Learning Solutions


Python 3 from scratch to become a developer in demand

Best Seller

70 Lectures 8.5 hours

Lets Kode It


Python Data Science basics with Numpy, Pandas and Matplotlib

Most Popular

63 Lectures 6 hours

Abhilash Nelson