Background

This is where I will be keeping my code snippets and notes I have on the popular python plotting library Matplotlib. As of now, I am making this to use as a quick reference for my Machine Learning class.

Basics

Template

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import numpy as np

data = getData()
x = data['x']
y = data['y']

fig, ax = plt.subplots(1, 1)  # create 2 subplots

ax[0, 0].plot(x=x, y=y, title='Left Graph', color='red', marker='o')
ax[0, 0].set_xlabel('X Label - Graph 1')
ax[0, 0].set_ylabel('Y Label - Graph 1')
ax[0, 0].set_xticks([1, 2, 3, 4, 5])
ax[0, 0].set_yticks([10, 20, 30, 40, 50])

ax[0, 1].scatter(x=x, y=y, title='Right Graph', color='blue', marker='x')
ax[0, 1].set_xlabel('X Label - Graph 2')
ax[0, 1].set_ylabel('Y Label - Graph 2')
ax[0, 1].set_xticks(np.arrange(1, 5, 1.0))
ax[0, 1].set_yticks(np.arrange(10, 50, 10.0))

plt.tight_layout()
plt.show()

Graph Types

plt.plot(x, y)
plt.scatter(x, y)
plt.bar(names, values)
plt.hist(data, bins)

Common Graph Properties

Some common properties to include in the plot and scatter graphs function include:

• Complete list
• x
• y
• label
• color
• marker
  • list of all available markers

Subplots

To break the graph into subplots, do the following:

fig, axs = plt.subplots(4, 4)                               # create 16 subplots

x = [1, 5, 7]                                               # x data
y = [20, 56, 3]                                             # y data
axs[0, 1].plot(x=x, y=y, label='First Subplot')     # axs[row, column]

x = [23, 56, 89]                                            # x data
y = [101, 23, 67]                                           # y data
axs[0, 2].plot(x=x, y=y, label='Second Subplot')    # axs[row, column]

Plotting 2 different datasets on the same graph

• Example
• Here, we want to plot 2 datasets with different values for the x and y axis on the same graph

movies    = np.array([2, 2, 2, 3, 1, 1, 2, 3, 4, 1, 4])
years     = np.array([1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009])
drownings = np.array([109, 102, 102, 98, 85, 95, 96, 98, 123, 94, 102])

fig, ax1 = plt.subplots()

# plot swimming pool drownings
color = 'tab:red'
ax1.set_xlabel('Years')
ax1.set_ylabel('Swimming pool drownings', color=color)
ax1.plot(years, drownings, color=color, marker='o')
ax1.tick_params(axis='y', labelcolor=color)
plt.yticks((80, 120 , 160))

# plot nick cage movie
color = 'tab:blue'                                 # change plot color
ax2 = ax1.twinx()                                  # instantiate a second axes that shares the same x-axis
ax2.set_ylabel('Nicholas Cage', color=color)       # we already handled the x-label with ax1
ax2.plot(years, movies, color=color, marker='x')
ax2.tick_params(axis='y', labelcolor=color)
plt.yticks((0, 4 , 8))

fig.tight_layout()
plt.xticks(np.arange(min(years), max(years)+1, 1.0))

plt.show()

Labels, Titles, and Ticks

Documentation

• Labels and titles
• Tick marks

Axis Labels

axs.set_xlabel('X label')
axs.set_ylabel('Y label')

Titles

axs.set_title('This is the title')

Ticks

There are a few different ways to set the x and y tick marks.

One way is to create an np.array() comprised of the desired tick mark values:

axs.set_yticks([1, 2, 3, 4, 5, 6, 7])
axs.set_xticks([45, 50, 100, 200, 300])

If you don't have a few number of ticks that you want to use, you can use the following to arrange them:

yticks = np.arrange(0, 100, 2.5)                   # np.arrange(min, max, incrementor)
xticks = np.arrange(min(x), max(x), 1.0)   # np.arrange(min, max, incrementor)

axs.set_yticks(yticks)
axs.set_xticks(xticks)

# or you could do
axs.set(yticks=yticks, xticks=xticks)

Legends

• Official documentation

Legend Location

axs.set_legend(loc=2)

Custom Legend

import matplotlib.patches as mpatches

blue_patch = mpatches.Patch(color='blue', label='setosa')       # setosa legend key
red_patch = mpatches.Patch(color='red', label='versicolor')     # versicolor legend key
green_patch = mpatches.Patch(color='green', label='virginica')  # virginica legend key
fig.legend(handles=[blue_patch, red_patch, green_patch])

General Appearance

Tight Layout

To make the graph look better, you can use:

plt.tight_layout()

White Space

• You can adjust the spacing between subplots by altering the figure's width space (wspace) and its height space (hspace).
• To compact all the subplots, set wspace and hspace equal to 0 by doing this:

fig.subplots_adjust(wspace=0)    # adjust width spacing
fig.subplots_adjust(hspace=0)    # adjust height spacing