Plotting Equations with Python

We are going to look at a few examples, and then I will provide the code to do create the plots through Google Colab!

Goals:

1. Learn to create a vector array.
2. Manipulate vector to match an equation.
3. Create beautiful plots with a title, axis labels, and grid.

Let’s go ahead and start by working on one of the simplest and most common equations.

y = x²

To do this, we are going to be doing a few things but first of all, we will be using 2 modules: Matplotlib.pyplot and Numpy.

Numpy

It is a Python library that provides.
Is the fundamental package for scientific computing in Python.

• A multidimensional array object.
• Derived objects such as arrays and matrices.

and an assortment of routines for fast operations on arrays, including mathematical and logical:

• Shape manipulation
• Sorting
• Selecting
• Discrete Fourier Transforms
• Basic linear algebra
• Basic statistical operations
• Random simulation
• much more

Matplotlib.pyplot

Is a collection of command style functions that make matplotlib work like MATLAB.
Each pyplot function makes some change to a figure:

• Creates a figure.
• Plots some lines in a plotting area.
• Decorates the plot with labels.
• etc.

Creating an X and a Y vector

We want to plot from 0–10 on the x-axis, but let’s think about what each value would be:
x = [1,2,3,4,…,9,10]
y = [1,4,9,16,…,99,100]
In this case, every value in y is just the x value at the same index squared.
Now that we have all of these values, we can just put them into a python variable and plot!

1. Import our modules that we are using.

   import matplotlib.pyplot as plt
   import numpy as np
   

2. Create the vectors X and Y.

   x = np.array(range(10))
   y = x ** 2
   

3. Create the plot

   plt.plot(x, y)
   

and then the resulting image:

I will go into a few options that we can add to make our plots look better!

1. When plotting add a label for the legend.
2. Adding a title.
3. Add X and Y label.
4. Add a grid with opacity of .9 and linestyle of ‘-‘.
5. Show the legend.

Finally, you can add multiple plots to the same figure.

Link:1 matplotlib pyplot.ipynb