SciPy is a free and open-source Python library used for technical computing and scientific computing. SciPy 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. In this lecture, we introduce all SciPy’s functions one will most probably pass through when Jupyter Notebook on Google Chrome, which is one of my favorite. Finally, the lecture is outlined as follows:

00:00 Intro

00:21 What is SciPy ?

02:06 SciPy subpackages

05:15 Installing SciPy

08:34 Concatenate of NumPy

09:03 Concatenating by rows

09:51 Concatenating by columns

10:34 Slicing Matrices

11:01 Mesh Grids

12:17 Polynomials

13:02 Polynomial Multiplication

13:32 Integrating Polynomials

14:17 Polynomial Derivatives

15:02 Polynomial as Array

15:31 Vectorizing Functions

18:52 Special SciPy Functions

19:15 Airy Functions

22:35 Exponentially Scaled Airy Functions

24:43 Bessel Functions

25:44 Thin Drumhead Example

31:51 Logit Function

33:18 Gamma Function

35:56 Error Functions

37:04 Entropy Function

38:01 Huber Function