One 、 In the test matplotlib When I met X The problem of axis Chinese character not displaying , Refer to the Internet

The source code is as follows

from matplotlib import pyplot as plt
import random
import matplotlib
from matplotlib import font_manager # my_font = {'family' : 'MicroSoft YaHei',
# 'weight' : 'bold',
# 'size' : 'larger'}
# matplotlib.rc("font",**my_font)
# matplotlib.rc("font",)
my_font = font_manager.FontProperties(fname="C:\\Windows\\Fonts\\msyh.ttf") plt.figure(figsize=(,),dpi=) x = range(,)
y = [random.randint(,) for i in range()] _xticks_labels = ["10 spot {} branch ".format(i) for i in range()]
_xticks_labels += ["11 spot {} branch ".format(i) for i in range()]
# plt.xticks(list(x)[::])
plt.xticks(list(x)[::],_xticks_labels[::],rotation=,fontproperties=my_font)
plt.plot(x,y)
plt.show()

Two 、 Refer to online play

Computer environment :Windows7-64bit,Anaconda3-4.2.0( Corresponding python 3.5.2 edition ),IDE yes Anaconda Self contained Spyder 3.

1、 find Windows The font library directory of the system , Default in C:\Windows\Fonts Under the table of contents .

 

2、 Choose the font you like , then “ Right click ”→“ attribute ”, You can see the name of the font .

This tutorial takes Microsoft YaHei font as an example , You can also choose other Fonts , If your own font library can't meet your needs , You can download other fonts online , Unzip the font to C:\Windows\Fonts directory .

 

3、 stay IDE In the tools , Import matplotlib library , Start defining the path of the font .

  • 1 Don't set parameters first , You can see that the Chinese font is displayed as a box ( What is used here is SVC Three classification model ).
 
 
  • 2 After setting the parameters , You can see that the Chinese font can be displayed normally .( The code has been tested , Safe to use )

Where need Chinese settings , Make sure the function has fontproperties = my_font Parameters can be . such as x The label of the shaft is : The calyx length of iris . Then you can use :

plt.xlabel(' The calyx length of iris ', fontproperties = my_font)
 
 
# coding:utf-8
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets # The following two lines address other common problems
plt.rcParams['font.sans-serif'] = ['SimHei'] # Specify default font
plt.rcParams['axes.unicode_minus'] = False # Resolve save image is negative '-' Questions displayed as squares import matplotlib.font_manager as mf # Import font manager
my_font= mf.FontProperties(fname='C:\\Windows\\Fonts\\msyh.ttf') # Load Fonts iris = datasets.load_iris()
X = iris.data[:, :2]
Y = iris.target def my_kernel(X, Y):
M = np.array([[2, 0], [0, 1.0]])
return np.dot(np.dot(X, M), Y.T) h = 0.02 clf = svm.SVC(kernel = my_kernel)
clf.fit(X, Y) x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()]) Z = Z.reshape(xx.shape)
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired) plt.scatter(X[:, 0], X[:, 1], c=Y, cmap=plt.cm.Paired)
plt.title(' test !!!3-Class classification using SVM with custom kernel',
fontproperties = my_font)
plt.axis('tight')
plt.show()
 
 

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