import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(np.random.rand(10,2),columns = ['A','B'])
f = plt.figure(figsize=(10,10))
fig = df.plot(figsize=(6,4))
#figsize:创建图表窗口,设置窗口大小
#创建图表对象,并赋值于fig
#print(fig,type(fig))
#print(f,type(f))
print(df)
plt.title('aa')
plt.xlabel('x')
plt.ylabel('y')
plt.legend(loc = 0)
#显示图例,loc表示位置 plt.xlim([0,12])#x轴边界
plt.ylim([0,1.5])#y轴边界
plt.xticks(range(10))#设置x刻度
plt.yticks([0,0.2,0.4,0.6,0.8,1.0,1.2])#设置y刻度
fig.set_xticklabels("%.1f" %i for i in range(10))# x轴刻度标签
fig.set_yticklabels("%.2f" %i for i in [0,0.2,0.4,0.6,0.8,1.0,1.2])#y轴刻度标签
#范围只限定图表的长度,刻度则是决定显示的标尺->这里x轴范围是0-12,但是刻度只是0-9,刻度标签使得其显示1位小数
#轴标签则是显示刻度的标签 print(fig,type(fig))
 
 
 
          A         B
0 0.194396 0.158268
1 0.441188 0.381923
2 0.302473 0.426713
3 0.090546 0.049909
4 0.646411 0.679785
5 0.414922 0.090133
6 0.456562 0.735721
7 0.943635 0.057677
8 0.232065 0.676686
9 0.418662 0.107795
AxesSubplot(0.125,0.125;0.775x0.755) <class 'matplotlib.axes._subplots.AxesSubplot'>
<matplotlib.figure.Figure at 0x19d584a8>

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" alt="" />

 #其他可视性
x = np.linspace(-np.pi,np.pi,256,endpoint=True)
c,s =np.cos(x),np.sin(x)
# 通过ndarry创建图表
plt.plot(x,c)
plt.plot(x,s)
plt.grid(True,linestyle = '--',color = 'gray',linewidth = '0.5',axis = 'both')
#显示网格
#linestyle:线型
#color:颜色
#linewidth:宽度
#axis:x,y,both,显示 x/y/的网格 plt.tick_params(bottom='on',top='off',left='on',right='off')
#刻度显示 import matplotlib
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'inout'
#设置刻度的方向
#这里需要导入matplotlib整个包 frame = plt.gca()
#plt.axis('off')
#关闭坐标轴
#frame.axes.get_xaxis().set_visible(False)
#frame.axes.get_yaxis().set_visible(False)
#x/y轴不可见

 
 

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