Flash cards
Review the key moves
What is the main idea behind Matplotlib Line?
Lesson checks
Practice each idea before moving on
Short Mimo-style checks built from this lesson's code, terms, and sequence.
Which statement best captures the main point of this lesson?
Complete the missing token from the example code.
___ matplotlib.pyplot as pltPut the learning moves in the order that makes the concept easiest to apply.
Linestyle
You can use the keyword argument linestyle , or shorter ls , to change the style of the plotted line:
Example
import matplotlib.pyplot as plt
import numpy as np
ypoints = np.array([3, 8, 1, 10])
plt.plot(ypoints, linestyle = 'dotted')
plt.show()Example
plt.plot(ypoints, linestyle = 'dashed')Shorter Syntax
The line style can be written in a shorter syntax:
linestyle can be written as ls .
dotted can be written as : .
dashed can be written as -- .
Example
plt.plot(ypoints, ls = ':')Line Styles
You can choose any of these styles:
| Style | Or |
|---|---|
| 'solid' (default) | '-' |
| 'dotted' | ':' |
| 'dashed' | '--' |
| 'dashdot' | '-.' |
| 'None' | '' or ' ' |
Line Color
You can use the keyword argument color or the shorter c to set the color of the line:
Example
import matplotlib.pyplot as plt
import numpy as np
ypoints = np.array([3, 8, 1, 10])
plt.plot(ypoints, color = 'r')
plt.show()You can also use Hexadecimal color values :
Example
...
plt.plot(ypoints, c = '#4CAF50')
...Or any of the 140 supported color names .
Example
...
plt.plot(ypoints, c = 'hotpink')
...Line Width
You can use the keyword argument linewidth or the shorter lw to change the width of the line.
The value is a floating number, in points:
Example
import matplotlib.pyplot as plt
import numpy as np
ypoints = np.array([3, 8, 1, 10])
plt.plot(ypoints, linewidth = '20.5')
plt.show()Multiple Lines
You can plot as many lines as you like by simply adding more plt.plot() functions:
plt.plot()You can also plot many lines by adding the points for the x- and y-axis for each line in the same plt.plot() function.
(In the examples above we only specified the points on the y-axis, meaning that the points on the x-axis got the the default values (0, 1, 2, 3).)
The x- and y- values come in pairs:
Example
import matplotlib.pyplot as plt
import numpy as np
x1 = np.array([0, 1, 2, 3])
y1 = np.array([3, 8, 1, 10])
x2 = np.array([0, 1, 2, 3])
y2 = np.array([6, 2, 7, 11])
plt.plot(x1, y1, x2, y2)
plt.show()