Book Recommendations for Learning Python for Mathematics.

Although I myself have not read this book, but I think "Doing Math With Python" (published by No Starch Press) can be a good start.

I believe it focuses on learning Python through the lens of using it for mathematics purposes. Here are its contents:

• Acknowledgements
• Introduction
• Working with Numbers
• Visualizing Data with Graphs
• Describing Data with Statistics
• Algebra and Symbolic Math with SymPy
• Playing with Sets and Probability
• Drawing Geometric Shapes and Fractals
• Solving Calculus Problems
• Afterword
• Appendix A: Software installation
• Appendix B: Overview of Python Topics
• Index

You can find a PDF version of this book online for free. Here is its cover page:

Everyone learns differently, and for something like Python that's meant to be so organic and intuitive and more of a friendly wrapper or interface for huge chunks of compiled code that is community supported and tested, you may find that once you get the hang of Python's basic premise - everything is an object, and it's the object's attributes called methods that do stuff - then you may find you can put the book down, and just start trying things.

When you get stuck, try Python's incredibly useful help(object.attribute) for usually quite helpful documentation, or go to the online documentation for the package you are using which should be about the same.

I probably type

import numpy as np
import matplotlib.pyplot as plt
import scipy.special as spe
from scipy.optimize import minimize

several times a day.

Want to diagonalize a matrix? Type help(np.linalg.eig).

Want to minimize? Type help(minimize).

Need a specific kind of Bessel function or elliptic integral, type help(scipy.special)

The first hurdle is that everything is an object. There is a moment where it makes no sense, then suddenly it becomes easy and intuitive.

The second hurdle is numpy arrays. Numpy also has matrix objects, but a lot of the time we actually need n-dimensional arrays. Numpy's arrays can do amazing things with indexing and broadcasting, so go straight to those chapters first.

The third hurdle is to learn to read and trust the verbose and (usually) very helpful error messages. We all make syntax errors regularly, or mix dimensions of arrays. When you see an error message, treat it as a trusted friend. Embrace it, and read through carefully. If you don't understand the error message, paste the key part into a Google search and you will immediately see how other people solved the same problem!

Despite the title, you may find that Jake VanderPlas' Python Data Science Handbook has some explanations (especially for Numpy) that are very accessible and get you started quickly.

I think that once you start playing with Python and especially reading documentation and error messages, you'll put the book down and just dig in in your own, unique learning-style way.

Here's an example of something I got from one of Jake's blogpost in my answer to How do I create a 3D line plot in matplotlib from the data in arrays?

Here is the Lorenz attractor both in 3D and animated...

Here is a minimal, simplified example of plotting lines in 3D based on the above:

def lorentz_deriv((x, y, z), t0, sigma=10., beta=8./3, rho=28.0):
    """Compute the time-derivative of a Lorentz system."""
    return [sigma * (y - x), x * (rho - z) - y, x * y - beta * z]

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.integrate import odeint as ODEint

x = np.linspace(0, 20, 1000)
y, z = 10.*np.cos(x), 10.*np.sin(x) # something simple

fig = plt.figure()
ax = fig.add_subplot(1,2,1,projection='3d')
ax.plot(x, y, z)

# now Lorentz
times = np.linspace(0, 4, 1000) 

start_pts = 30. - 15.*np.random.random((20,3))  # 20 random xyz starting values

trajectories = []
for start_pt in start_pts:
    trajectory = ODEint(lorentz_deriv, start_pt, times)
    trajectories.append(trajectory)

ax = fig.add_subplot(1,2,2,projection='3d')
for trajectory in trajectories:
    x, y, z = trajectory.T  # transpose and unpack 
    # x, y, z = zip(*trajectory)  # this also works!
    ax.plot(x, y, z)

plt.show()

Exploring University Mathematics with Python by Siri Chongchitnan https://link.springer.com/book/10.1007/978-3-031-46270-2

I know of an online book which deals with statistics and use r studio to plot, not Python. I think it is great. You can have a try! http://rafalab.dfci.harvard.edu/dsbook/summary-statistics.html

Well, there are lots of good books that may help you in learning Python in the service of mathematics. Here below there are some books that I would like to recommend to you.

1. Doing Math with Python by Amit Saha
2. Applying Math with Python by Sam Morley
3. Python for Mathematicians by Ms Menaka B
4. Python Programming for Mathematics by Stephen Lynch
5. Mathematical Logic Through Python by José María Almira
6. Explore Python for Mathematics by Vijay Shinde
7. Python for Mathematics by Vincent Knight
8. Mathematical Python by Patrick J. Walls and Jay Gopalakrishnan

I hope it will help you.