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Xiaobian to share with you matplotlib Chinese display of the chart example analysis, I hope you read this article after all have something to gain, let's discuss it together!

Prepare #Import Package and Configure Chinese

%matplotlib inline

import matplotlib

import matplotlib.pyplot as plt

import numpy as np

from matplotlib.font_manager import FontProperties

# simhei.ttf can replace any Chinese ttf font, note that the path is correct

font = FontProperties(fname='simhei.ttf', size=16)

#Set matplotlib to display Chinese and minus sign normally

matplotlib.rcParams['font.sans-serif']=['SimHei']

matplotlib.rcParams['axes.unicode_minus']=False

Histogram #Generate canvas

plt.figure(figsize=(10, 6), dpi=80)

#abscissa city name

x = ['Heilongjiang',' Hongkong','Taiwan',' Shanghai','Inner Mongolia',' Shanxi','Beijing',' Shanxi','Guangdong',' Macao']

#Number of existing diagnoses on the day

y = [339, 222, 116, 54, 52, 50, 46, 37, 26, 12]

plt.bar(x,y,width=0.5)

#Title

plt.title ('2020.04.29 existing diagnoses top10', fontproperties=font)

#abscissa label

plt.xlabel ('top10 City', fontproperties=font)

#Coordinates label

plt.ylabel ('Number of existing diagnostics', fontproperties=font)

#Make coordinate scales

plt.xticks(x, fontproperties=font)

plt.show()

pie chart #generate canvas

plt.figure(figsize=(10, 6), dpi=80)

#abscissa city name

labels = ['Heilongjiang',' Hongkong','Taiwan',' Shanghai','Inner Mongolia',' Shanxi','Beijing',' Shanxi','Guangdong',' Macao']

#Number of existing diagnoses on the day

sizes = [339, 222, 116, 54, 52, 50, 46, 37, 26, 12]

explode = np.linspace(0, 0.4, 10)

plt.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=False, startangle=-45, textprops={'fontproperties':font})

plt.title("2020.04.29 Number of existing diagnoses top10 city proportion", fontproperties=font)

#Legend

plt.legend(loc='right', bbox_to_anchor=[0.75, 0.4, 0.5, 0.5], prop=font)

plt.show()

After reading this article, I believe you have a certain understanding of "matplotlib Chinese display chart example analysis," if you want to know more related knowledge, welcome to pay attention to the industry information channel, thank you for reading!

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