An example Analysis of the Chart displayed in matplotlib
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()
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