How to use python library matplotlib to draw different charts

This article mainly introduces how to use python library matplotlib to draw different charts, the article is very detailed, has a certain reference value, interested friends must read it!

1. Draw a simple graph

Idea: draw a curve through three coordinate points

Import matplotlib.pyplot as plt plt.plot ([1JEI 3JEI 5], [4JEI 8JEI 10]) # Abscissa: 1pje 3pr 5, ordinate: 4je 8je 1Ji displays the drawing plt.show ()

The running effect is as shown in the figure:

2. Draw a single graph

Idea: first draw a straight line through linspace, then define 100 elements between-pi~pi

Import matplotlib.pyplot as pltimport numpy as np x = np.linspace (- np.pi, np.pi, 100) # x axis is defined from-pi to pi, and then define 100elements plt.plot (x, np.sin (x)) # between-pi~pi to show the drawing plt.show ()

The running effect is as shown in the figure:

3. Draw multiple curves

Idea: on the basis of drawing a curve, modify the definition field, and then loop through plt.plot ()

Import matplotlib.pyplot as pltimport numpy as np x = np.linspace (- np.pi * 2, np.pi * 2100) # the definition domain of the x axis is-2pi~2piplt.figure (1, dpi=50) # create Chart 1 with an accuracy of 50. The higher the precision, the larger the volume of the picture. The clearer the picture is, the clearer for i in range (1,5): # such as drawing four curves plt.plot (x, np.sin (x / I)) # shows the drawing plt.show ()

The running effect is as shown in the figure:

4. Draw histogram

Idea: use the hist () method

Import matplotlib.pyplot as plt plt.figure (1) dpi = 50) # create chart 1 import matplotlib.pyplot as plt plt.figure DPI represents the fineness of the picture. The larger the dpi, the larger the file. Data = [1, 1, 1, 2, 2, 2, 3, 3, 4, 5, 5, 6, 4] plt.hist (data) # as long as the data is passed in, the histogram will count the number of times the histogram appears # display the drawing plt.show ()

The running effect is as shown in the figure:

5. Draw scatter plot

Idea: use the scatter () method

Import matplotlib.pyplot as pltimport numpy as np x = np.arange (1,10) y = xfig = plt.figure () # create a chart plt.scatter (x, y, cased scatterers, marker='o') # cased scattersThe color of the scatter is red, and marker indicates that the shape of the scatter is specified as a circle. # display the drawing plt.show ()

The running effect is as shown in the figure:

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