Python Matplotlib

Matplotlib is the most basic library of data visualization with Python. It created to try to replicate MatLab’s (another programming language) plotting capabilities in Python. So if you happen to be familiar with matlab, matplotlib will feel natural to you.

It is an excellent 2D and 3D graphics library for generating scientific figures.

Some of the major Pros of Matplotlib are:

• Generally easy to get started for simple plots
• Support for custom labels and texts
• Great control of every element in a figure
• High-quality output in many formats
• Very customizable in general

Let’s try an example with a simple line.

import matplotlib.pyplot as plt
import numpy as np

x = np.arange(0, 100, 2)
y = x ** (1/2)

plt.plot(x, y, 'r') # 'r' is the color red
plt.xlabel('X Axis Title Here')
plt.ylabel('Y Axis Title Here')
plt.title('String Title Here')
plt.show()

Creating Multiplots on same canvas

# plt.subplot(nrows, ncols, plot_number)
plt.subplot(1,2,1)
plt.plot(x, y, 'r--') # More on color options later
plt.subplot(1,2,2)
plt.plot(y, x, 'g*');

Matplotlib Object Oriented Method

Now that we’ve seen the basics, let’s break it all down with a more formal introduction of Matplotlib’s Object Oriented API. This means we will instantiate figure objects and then call methods or attributes from that object.

The main idea in using the more formal Object Oriented method is to create figure objects and then just call methods or attributes off of that object. This approach is nicer when dealing with a canvas that has multiple plots on it.

To begin we create a figure instance. Then we can add axes to that figure:

# Create Figure (empty canvas)
fig = plt.figure()

# Add set of axes to figure
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # left, bottom, width, height (range 0 to 1)

# Plot on that set of axes
axes.plot(x, y, 'b')
axes.set_xlabel('Set X Label') # Notice the use of set_ to begin methods
axes.set_ylabel('Set y Label')
axes.set_title('Set Title')

Code is a little more complicated, but the advantage is that we now have full control of where the plot axes are placed.

Legends, Labels, and Titles

Now that we have covered the basics of how to create a figure canvas and add axes instances to the canvas, let’s look at how decorate a figure with titles, axis labels, and legends.

fig = plt.figure()

ax = fig.add_axes([0,0,1,1])

ax.plot(x, y, label="y")
ax.plot(x, x/8, label="x/8")
ax.legend(loc=4)
# 1 Upper Right, 2 Upper Left, 3 Lower Left, 4 lower right, 0 Default from matplotlib

Setting colors, linewidth, linetypes

Matplotlib gives you a lot of options for customizing colors, linewidths, and linetypes.

With matplotlib, we can define the colors of lines and other graphical elements in a number of ways. First of all, we can use the MATLAB-like syntax where 'b' means blue, 'g' means green, etc. The MATLAB API for selecting line styles are also supported: where, for example, ‘b.-‘ means a blue line with dots.

More complete instruction you can check on matplotlib documentation here.

Let’s just try fully different samples

fig, ax = plt.subplots(figsize=(12,6))

ax.plot(x, x+1, color="red", linewidth=0.25)
ax.plot(x, x+2, color="red", linewidth=0.50)
ax.plot(x, x+3, color="red", linewidth=1.00)
ax.plot(x, x+4, color="red", linewidth=2.00)

# possible linestype options ‘-‘, ‘–’, ‘-.’, ‘:’, ‘steps’
ax.plot(x, x+5, color="green", lw=3, linestyle='-')
ax.plot(x, x+6, color="green", lw=3, ls='-.')
ax.plot(x, x+7, color="green", lw=3, ls=':')

# custom dash
line, = ax.plot(x, x+8, color="black", lw=1.50)
line.set_dashes([5, 10, 15, 10]) # format: line length, space length, ...

# possible marker symbols: marker = '+', 'o', '*', 's', ',', '.', '1', '2', '3', '4', ...
ax.plot(x, x+ 9, color="blue", lw=3, ls='-', marker='+')
ax.plot(x, x+10, color="blue", lw=3, ls='--', marker='o')
ax.plot(x, x+11, color="blue", lw=3, ls='-', marker='s')
ax.plot(x, x+12, color="blue", lw=3, ls='--', marker='1')

# marker size and color
ax.plot(x, x+13, color="purple", lw=1, ls='-', marker='o', markersize=2)
ax.plot(x, x+14, color="purple", lw=1, ls='-', marker='o', markersize=4)
ax.plot(x, x+15, color="purple", lw=1, ls='-', marker='o', markersize=8, markerfacecolor="red")
ax.plot(x, x+16, color="purple", lw=1, ls='-', marker='s', markersize=8, 
        markerfacecolor="yellow", markeredgewidth=3, markeredgecolor="green");

Saving figures

Matplotlib can generate high-quality output in a number formats, including PNG, JPG, EPS, SVG, PGF and PDF. To save a figure to a file we can use the savefig method in the Figure class :

fig.savefig("filename.png")