Matplotlib绘图时，除了必要的数据信息展示外，还会遇到其他各种外在的问题，such as“中文显示”、“负号显示”以及绘制出的“图片大小”等。以下放了个人绘图时所用的代码，并将部分有用链接一并附上。
import matplotlib
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot')
from pylab import * 
mpl.rcParams['font.sans-serif'] = ['SimHei']  #用来显示中文
mpl.rcParams['axes.unicode_minus']=False #用来显示负号
matplotlib.rcParams['figure.figsize'] = [5.4, 4] # for square canvas
plt.rcParams['savefig.dpi'] = 140 #图片像素
plt.rcParams['figure.dpi'] = 130 #分辨率


中文、负号显示

多个子图的绘制

很全面的绘图讲解

文章目录

坐标操作

	
对数坐标轴
		
坐标刻度及其标记
		
横坐标值逆序显示
	
	
边框操作
	
隐藏边框
	
	
图例操作
	
图例置于边框外
	
	
标题操作
	
标题位置
	
	
注释操作
	
注释字体
	
	
配色操作
	
饼图颜色
	
	
子图操作
	
子图间距
	
 

坐标操作

参考博文
Python绘图总结(Matplotlib篇)之坐标轴及刻度

对数坐标轴

import numpy as np
import matplotlib.pyplot as plt
import scipy.stats as sts

if __name__ == '__main__':
    a = 0.031 / 10000 + 0.0337 / 10000
    print(0.0336 * 100 / np.sqrt(a))  # 1320.95

    r = sts.lognorm.rvs(0.954, size=1000)
    c = plt.hist(r, bins=500)
    plt.show()

    # 双对数坐标下
    fig, ax = plt.subplots()
    ax.set_xscale("log")
    ax.set_yscale("log")
    ax.set_adjustable("datalim")
    ax.plot(c[1][:-1], c[0], 'o')
    ax.set_xlim(1e-1, 1e6)
    ax.set_ylim(1e-2, 1e6)
    ax.grid()
    plt.draw()
    plt.show()

    # 半对数坐标
    fig1, ax1 = plt.subplots()
    ax1.hist(r, bins=500)
    ax1.set_xscale("log")
    ax1.set_xlim(1e-1, 1e6)
    ax1.grid()
    plt.draw()
    plt.show()


效果如下


坐标刻度及其标记

python_matplotlib改变横坐标和纵坐标上的刻度(ticks)

font = {'family': 'arial', 'weight': 'normal', 'size': 14} 
ax1.set_xticks([1,2,3,4,5)
ax1.set_xticklabels(x,font) 
ax1.set_yticks([0,0.2,0.4,0.6,0.8,1])
ax1.set_yticklabels([0,0.2,0.4,0.6,0.8,1],font) 


横坐标值逆序显示

逆序前的代码和图

import matplotlib.pyplot as plt

plt.figure()
ax1 = plt.subplot(111)
x_list = [1, 2, 3, 4, 5]
y_list = [5, 10, 20, 40, 80]
plt.sca(ax1)
plt.title("Test 1")
plt.xlabel("X")
plt.ylabel("Y")
plot1, = plt.plot(x_list, y_list, 'bo')
plt.show()




逆序后的代码和图

import matplotlib.pyplot as plt

plt.figure()
ax1 = plt.subplot(111)
x_list = [1, 2, 3, 4, 5]
y_list = [5, 10, 20, 40, 80]
plt.sca(ax1)
plt.title("Test 1")
plt.xlabel("X")
plt.ylabel("Y")
plot1, = plt.plot(x_list, y_list, 'bo')
plt.gca().invert_xaxis()  # 就这句话
plt.show()




边框操作

隐藏边框

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)


图例操作

参考博文
matplotlib命令与格式：图例legend语法及设置

图例置于边框外

参考博文

ax.legend(loc='center left', bbox_to_anchor=(0.2, 1.12),ncol=3)


标题操作

官方教程

标题位置

ax.set_title('AUC', font, x=0.5, y=1.05)


x,y可以控制标题的位置，但是记住要放在 font 后面，不然会报错.

SyntaxError: positional argument follows keyword argument


注释操作

matplotlib.axes.Axes.annotate
matplotlib命令与格式：标题(title),标注(annotate),文字说明(text)

注释字体

ax.annotate('{}'.format(height),
             xy=(rect.get_x() + rect.get_width() / 2, height),
             xytext=(0, 3),  # 3 points vertical offset
             textcoords="offset points", 
             # 字体属性
			 size=12, family='arial', 
             ha='center', va='bottom')


配色操作

用色环图帮你搞定配色

饼图颜色

#  色系 浅灰色 #d8dcd6   天蓝色 #069af3 lightblue(#7bc8f6)  blue with a hint of purple(#533cc6)   # purple red(#990147)
# dark royal blue(#02066f) royal(#0c1793)
ax.plot(a,b,linestyle='dotted',linewidth=2.5,marker='o',color='#e377c2',label='CN')
ax.plot(a,c,linestyle='dotted',linewidth=2.5,marker='v',color='#533cc6',label='RA')
ax.plot(a,d,linestyle='solid',linewidth=2.5,marker='d',color='#7d7f7c',label='PA')
ax.plot(a,e,linestyle='dashed',linewidth=2.5,marker='8',color='#01ff07',label='LP')    


子图操作

子图间距

matplotlib.pyplot.subplots_adjust

plt.subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)

## 默认参数
left = 0.125  # the left side of the subplots of the figure
right = 0.9   # the right side of the subplots of the figure
bottom = 0.1  # the bottom of the subplots of the figure
top = 0.9     # the top of the subplots of the figure
wspace = 0.2  # the amount of width reserved for space between subplots,
              # expressed as a fraction of the average axis width
hspace = 0.2  # the amount of height reserved for space between subplots,
              # expressed as a fraction of the average axis height


 

import numpy as np
import matplotlib.pyplot as plt

X = np.linspace(-np.pi, np.pi, 256, endpoint = True)
C, S = np.cos(X), np.sin(X)

plt.rcParams['xtick.direction'] = 'inout'  # in; out; inout
plt.rcParams['ytick.direction'] = 'inout'

plt.plot(X,C)
plt.plot(X,S)

plt.show()

 

python使用matplotlib绘图时使刻度线向内

刻度向外的程序

import numpy as np
import matplotlib.pyplot as plt
num = np.arange(10).reshape(2,5)
##print(num[0])
plt.plot(num[0],num[1])
plt.show()


结果：


刻度向内的程序

import numpy as np
import matplotlib.pyplot as plt
num = np.arange(10).reshape(2,5)
##print(num[0])
plt.rcParams['xtick.direction'] = 'in'#将x周的刻度线方向设置向内
plt.rcParams['ytick.direction'] = 'in'#将y轴的刻度方向设置向内
plt.plot(num[0],num[1])
plt.show()


结果：