1. Introduce the matpltlib library
matplotlibIt is an excellentpythonThird-party libraries for data visualization
utilizationmatpltlibWhen the library draws, bring it in first, load the pyplot inside and name it plt, then use theplotGraphing functions
import as plt #plt is an alias for the introduced module
Summary of Basic Iconic Functions
Function Graphing Syntax Rules
The plot function argument:plot([x],y,[format],**kwargs)
There are too many different kinds of grammar, so I'll steal a few MOOC charts and put them up.
4. Line graphs
from matplotlib import pyplot as plt
# Generate data
# Horizontal coordinate data from 2017 to 2022, the third parameter can control the step size, can write or not write
x = range(2017, 2022)
The value of the vertical coordinate of #y
y1 = [49, 48, 45, 52, 50]
y2 = [60, 62, 61, 65, 63]
# Generate graphics
("LMY and her mother's weight")
('year')
('kg')
(x, y1, color='green', label='LMY')
(x, y2, color='purple', label='mother')
(alpha=0.5)
(loc='upper right')
#Display Graphics
()
4. Scatterplot
from matplotlib import pyplot as plt
import numpy as np
# Generate data
# Horizontal data from 2017 to 2022, the third parameter controls the step size and can be written or not.
x = range(2017, 2022)
# y corresponds to the value of the vertical coordinate
y1 = [49, 48, 45, 52, 50]
y2 = [60, 62, 61, 65, 63]
# Generate graphics
("LMY and her mother's weight")
('year')
('kg')
# The size of the point
area = *4**2
(x, y1, s=area, c='yellow', alpha=1)
(x, y2, s=area, c='blue', alpha=1)
()
(())
()
5. Histogram
from matplotlib import pyplot as plt
import numpy as np
# Generate data
# Horizontal data from 2017 to 2022, the third parameter controls the step size and can be written or not.
x = [2017, 2018, 2019, 2020, 2021]
# y corresponds to the value of the vertical coordinate
y1 = [49, 48, 45, 52, 50]
y2 = [60, 62, 61, 65, 63]
# Generate graphics
("LMY and her mother's weight")
('frequency')
('kg')
# The size of the point
(y1, bottom=None, color='purple')
(y2, bottom=None, color='pink')
()
# n, bins, patches = (arr, bins=50, normed=1, facecolor='green', alpha=0.75)
'''
arr:a one-dimensional array to calculate the histogram.
bins:the number of bars in the histogram, optional, default is 10
normalized:whether to normalize the histogram vector, default is 0.
facecolor:color of the histogram.
alpha:transparency
'''
6. Bar charts
vertically
from matplotlib import pyplot as plt
import numpy as np
arr = (2017, 2022)
x = [49, 48, 45, 52, 50] # x-axis
y = [2017, 2018, 2019, 2020, 2021]
rect = (arr, x, width=0.5)
('LMY')
('weight')
('year')
()
()
orthogonal
many
from matplotlib import pyplot as plt
import numpy as np
arr = (2017, 2022)
x1 = [49, 48, 45, 52, 50] # x-axis
x2 = [60, 62, 61, 65, 63]
y = [2017, 2018, 2019, 2020, 2021]
rects1 = (arr, x1, 0.5, color='purple', label='LMY')
rects2 = (arr, x2, 0.5, color='yellow', label='Mother', alpha=0.3)
("LMY and her mother's weight")
('weight')
('year')
()
()
7. Pie charts
from matplotlib import patches, pyplot as plt
import numpy as np
label_list = ['49', '48', '45', '52', '50']
size = [20, 20, 20, 20, 20]
# The color of the parts
color = ['red', 'pink', 'blue', 'green', 'purple']
explode = [0, 0, 0.15, 0, 0]
patches, l_text, p_text = (size, explode=explode, colors=color, labels=label_list,
labeldistance=1.2, autopct="%1.2f%%", shadow=False, startangle=90, pctdistance=0.6)
('equal')
("LMY's weight")
(loc='upper left')
()
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