# Matplotlib Introduction Tutorial

There are a few plotting libraries to choose from in Python, but matplotlib seems the most common. Once you know the basics of Matplotlib, you might find Seaborn helpful, and it’s built on top of Matplotlib, so it’s helpful to know both. This tutorial is an introduction to the basic features of Matplotlib, and all the examples run right in your browser. You can try playing with the features to see how they affect the plot.

This first example shows the default presentation of a sine curve and a cosine curve. Try adjusting the data range in linspace() or multiplying one of the curves by a coefficient. The plot updates immediately as you make your changes.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t)
plt.plot(t, cos_t)

plt.show()


## Line Style

That example was very short, because we just used all the default settings. The next example should look exactly the same, but it now has all those settings given as explicit values. Can you adjust each setting to match the goal plot below? The two plots are compared on the bottom right, with differences highlighted in red.

Hint The line widths in the goal plot are whole numbers.

If you want more details about the settings, read the matplotlib documentation on colours and line styles.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='tab:blue', linewidth=1.5, linestyle='-')
plt.plot(t, cos_t, color='tab:orange', linewidth=1.5, linestyle='-')

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='tab:orange', linewidth=1, linestyle='--')
plt.plot(t, cos_t, color='tab:blue', linewidth=3, linestyle='-')

plt.show()


## Limits

Now, I’ve switched to some brighter colours, and replaced the default limits on the axes. Can you adjust the limits to match the goal plot?

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

plt.xlim(t.min()*1.1, t.max()*1.1)
plt.ylim(sin_t.min()*1.1, sin_t.max()*1.1)

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

plt.xlim(t.min(), t.max())
plt.ylim(sin_t.min()*1.5, sin_t.max()*1.5)

plt.show()


## Tick Positions

The default ticks don’t show the important points at multiples of π, like the minimum and maximum points of the curves. xticks() and yticks let you choose exactly where to put the ticks.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks([-3, -2, -1, 0, 1, 2, 3])
plt.yticks([-1, -0.75, -0.5, -0.25, 0, 0.25, 0.5, 0.75, 1])

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks([-pi, -pi/2, 0, pi/2, pi])
plt.yticks([-1, 0, 1])

plt.show()


## Tick Labels

The important positions are now marked, but it’s not immediately clear what the values mean. It would be nicer to mark them with π and π/2. Luckily, xticks() and yticks() accept label text as well as values, and you can use LaTeX for mathematical symbols like π. The labels are started, can you fill out the rest to match the goal plot? The r prefix in r'$-\pi$' means that Python treats it as a raw string, and won’t try to interpret the backslashes that are meant for LaTeX.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'-pi/2', r'0', r'?', r'??'])
plt.yticks([-1, 0, 1])

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

plt.show()


## Spine Positions

The default locations for the axes lines and ticks, or spines, is along the outer edges of the plot. For some plots, it’s clearer to put them through the middle. The gca() function is short for “get current axes”, and it returns the set of X and Y axes, so you can adjust their display. In order to hide one of the spines, set its colour to 'none'.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',-1.1))
ax.spines['bottom'].set_color('black')
ax.spines['top'].set_color('black')
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',-1.1*pi))
ax.spines['left'].set_color('black')
ax.spines['right'].set_color('black')

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5)
plt.plot(t, cos_t, color='orange', linewidth=2.5)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.spines['bottom'].set_color('black')
ax.spines['top'].set_color('none')
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_color('black')
ax.spines['right'].set_color('none')

plt.show()


## Legendary Plot

Usually, it’s helpful to label what the data is in each line. That’s what a legend is for. The loc defaults to 'best', but you can choose 'upper right' or other common locations. The nice thing about 'best' is that it will pick the location where it fits best. Watch how it moves after you label the cosine line. Then try changing sin_t to -sin_t in the plot() call. Can you finish setting up the legend?

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine')
plt.plot(t, cos_t, color='orange', linewidth=2.5)

plt.legend(loc='best', frameon=True)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine')
plt.plot(t, cos_t, color='orange', linewidth=2.5, label='cosine')

plt.legend(loc='best', frameon=False)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

plt.show()


## Highlight Features

Often, you want to point out a particular feature in a plot, so matplotlib lets you annotate with arrows and text. In this example, we’ve annotated the sine and cosine of 3π/4. Can you update it to 2π/3?

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine')
plt.plot(t, cos_t, color='orange', linewidth=2.5, label='cosine')

pi = np.pi
t0 = 3*pi/4
plt.plot([t0,t0],[0,np.sin(t0)], color ='black', linewidth=1.5, linestyle="-")
plt.scatter([t0,],[np.sin(t0),], 50, color ='black')

plt.annotate(
r'$\sin(\frac{3\pi}{4})=\frac{\sqrt{2}}{2}$',
xy=(t0, np.sin(t0)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,

plt.plot([t0,t0],[0,np.cos(t0)], color ='black', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.cos(t0),], 50, color ='black')

plt.annotate(
r'$\cos(\frac{3\pi}{4})=-\frac{\sqrt{2}}{2}$',
xy=(t0, np.cos(t0)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,

plt.legend(loc='best', frameon=False)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine')
plt.plot(t, cos_t, color='orange', linewidth=2.5, label='cosine')

t0 = 2*np.pi/3
plt.plot([t0,t0],[0,np.sin(t0)], color ='blue', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.sin(t0),], 50, color ='blue')

plt.annotate(
r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
xy=(t0, np.sin(t0)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,

plt.plot([t0,t0],[0,np.cos(t0)], color ='orange', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.cos(t0),], 50, color ='orange')

plt.annotate(
r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
xy=(t0, np.cos(t0)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,

plt.legend(loc='best', frameon=False)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()
ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

plt.show()


Some of these tick labels are hard to read, because the lines cross over them. Try increasing the font size, fading the lines behind them, getting rid of one of the zeroes, and aligning the remaining zero to avoid the other axis. To make the lines draw behind the labels, we change the Z-order of the lines. It’s called that, because it moves objects up and down the Z axis that sticks up out of the paper, perpendicular to the X and Y axes.

### Canvas ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine', zorder=2)
plt.plot(t, cos_t, color='orange', linewidth=2.5, label='cosine', zorder=2)

pi = np.pi
t0 = 2*pi/3
plt.plot([t0,t0],[0,np.sin(t0)], color ='blue', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.sin(t0),], 50, color ='blue')

plt.annotate(
r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
xy=(t0, np.sin(t0)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,

plt.plot([t0,t0],[0,np.cos(t0)], color ='orange', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.cos(t0),], 50, color ='orange')

plt.annotate(
r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
xy=(t0, np.cos(t0)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,

plt.legend(loc='best', frameon=False)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 0, 1], [r'$-1$', r'$0$', r'$1$'])

ax = plt.gca()

ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

labels = ax.xaxis.get_majorticklabels() + ax.yaxis.get_majorticklabels()
labels[2].set_horizontalalignment('center')
for label in labels:
label.set_fontsize(10)
label.set_bbox(dict(facecolor='none', edgecolor='none', alpha=0.5))

plt.show()
### Goal ###
import numpy as np
import matplotlib.pyplot as plt

t = np.linspace(-np.pi, np.pi, 256, endpoint=True)
sin_t = np.sin(t)
cos_t = np.cos(t)

plt.plot(t, sin_t, color='blue', linewidth=2.5, label='sine', zorder=1)
plt.plot(t, cos_t, color='orange', linewidth=2.5, label='cosine', zorder=1)

pi = np.pi
t0 = 2*pi/3
plt.plot([t0,t0],[0,np.sin(t0)], color ='blue', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.sin(t0),], 50, color ='blue')

plt.annotate(
r'$\sin(\frac{2\pi}{3})=\frac{\sqrt{3}}{2}$',
xy=(t0, np.sin(t0)), xycoords='data',
xytext=(+10, +30), textcoords='offset points', fontsize=16,

plt.plot([t0,t0],[0,np.cos(t0)], color ='orange', linewidth=1.5, linestyle="--")
plt.scatter([t0,],[np.cos(t0),], 50, color ='orange')

plt.annotate(
r'$\cos(\frac{2\pi}{3})=-\frac{1}{2}$',
xy=(t0, np.cos(t0)), xycoords='data',
xytext=(-90, -50), textcoords='offset points', fontsize=16,

plt.legend(loc='best', frameon=False)

pi = np.pi
plt.xticks(
[-pi, -pi/2, 0, pi/2, pi],
[r'$-\pi$', r'$-\pi/2$', r'$0$', r'$\pi/2$', r'$\pi$'])
plt.yticks([-1, 1], [r'$-1$', r'$1$'])

ax = plt.gca()

ax.spines['bottom'].set_position(('data',0))
ax.spines['top'].set_color('none')
ax.spines['left'].set_position(('data',0))
ax.spines['right'].set_color('none')

labels = ax.xaxis.get_majorticklabels() + ax.yaxis.get_majorticklabels()
labels[2].set_horizontalalignment('right')
for label in labels:
label.set_fontsize(16)
label.set_bbox(dict(facecolor='white', edgecolor='none', alpha=0.5))

plt.show()


Now you’ve seen some basic features of matplotlib, you might find the matplotlib tutorials a good place to learn more. You could also try Seaborn to add more plot types and styles. This tutorial was inspired by the work of Nicolas P. Rougier.