Gain insights into Python for MATLAB users, covering syntax, data structures, file handling, and data visualization. Learn about NumPy, pandas, Matplotlib, Seaborn, and practice through hands-on challenges.

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Python is a versatile programming language with many open-source packages for different applications. Even scientists using MATLAB for complex tasks are shifting toward Python. This course focuses on MATLAB users learning Python.

First, you’ll be introduced to MATLAB and Python’s key differences and similarities. You’ll learn the fundamentals of Python syntax and data types and structures, particularly lists, dictionaries, sets, and tuples. You’ll learn the control flow of programs, functions, and file handling. You’ll learn about NumPy, pandas, Matplotlib, and Seaborn packages. You’ll learn different Python operators and how to store, manipulate, and visualize data. You’ll also get hands-on experience with three practice challenges at the end of the course. The course also discusses the Zen of Python.

After completing this course, you can program in Python and embark on any advanced Python, data science, or machine learning course.

Introduction to Python for MATLAB Users

# import commands
import matplotlib.pyplot as plt
import numpy as np 
import math
from mpl_toolkits.mplot3d import Axes3D

# create plot
gs = plt.GridSpec(3,3)
fig = plt.figure()
plt.subplots_adjust(hspace = 0.5, wspace = 0.5)

# plot 1: 3D surface plot on row # 1,2 and col # 1,2
s1 = fig.add_subplot(gs[:2,:2],projection='3d')
X = np.arange(-1,1,0.1)
Y = np.arange(-1,1,0.1)
X,Y = np.meshgrid(X,Y)
def f(x,y): 
    return y**2 + x**2
s1.plot_surface(X,Y,f(X,Y),cmap='viridis')
s1.view_init(elev=30,azim=210) 

# plot 2: Polar plot on row # 1 and col # 3
s2 = fig.add_subplot(gs[0,2],polar=True)
t = np.arange(0, 360, 0.02)
r = 0.5*t/60
plt.polar(np.radians(t), r)

# plot 3: horizontal bar plot on row # 2 and col # 3
s3 = fig.add_subplot(gs[1,2])
index = np.arange(1,6)
values = np.array([6,2,7,3,8])
plt.barh(index,values,color = (.42, .37, .73))
plt.yticks(index,['A','B','C','D','E'])
plt.xlim(0,10)

# plot 4: 2D Scatter plot on row # 3 and col # 1,2,3
s4 = fig.add_subplot(gs[2,:3])
t = np.arange(0,2.6,0.1)
y1 = list(map(math.sin,math.pi*t))
y2 = list(map(math.sin,math.pi*t+math.pi/2))
y3 = list(map(math.sin,math.pi*t-math.pi/2))
plt.plot(t,y1,'b*',t,y2,'g^',t,y3,'rs')


plt.savefig('output/subplot.png')

Look into the solution of the subplots exercise. 

Introduction to the Course

Universe of the Two Worlds

Basic Data Types and Operations

Control Flow

Functions

File Handling

Data Visualization

Practice Challenges

Conclusion

Appendix

Solution: Subplots

Solution