在交互环境中查看帮助文档：

import matplotlib.pyplot as plt
help(plt.plot)

以下是对帮助文档重要部分的翻译：

plot函数的一般的调用形式：

#单条线：
plot([x], y, [fmt], data=None, **kwargs)
#多条线一起画
plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

可选参数[fmt] 是一个字符串来定义图的基本属性如：颜色（color），点型（marker），线型（linestyle），

具体形式  fmt = '[color][marker][line]'

fmt接收的是每个属性的单个字母缩写，例如：

plot(x, y, 'bo-') # 蓝色圆点实线

若属性用的是全名则不能用*fmt*参数来组合赋值，应该用关键字参数对单个属性赋值如：

plot(x,y2,color='green', marker='o', linestyle='dashed', linewidth=1, markersize=6)

plot(x,y3,color='#900302',marker='+',linestyle='-')

常见的颜色参数：**Colors**

也可以对关键字参数color赋十六进制的RGB字符串如 color='#900302'

 ============= ===============================
 character  color
 ============= ===============================
 ``'b'``   blue 蓝
 ``'g'``   green 绿
 ``'r'``   red 红
 ``'c'``   cyan 蓝绿
 ``'m'``   magenta 洋红
 ``'y'``   yellow 黄
 ``'k'``   black 黑
 ``'w'``   white 白
 ============= ===============================

点型参数**Markers**,如：marker='+' 这个只有简写，英文描述不被识别

============= ===============================
 character  description
 ============= ===============================
 ``'.'``   point marker
 ``','``   pixel marker
 ``'o'``   circle marker
 ``'v'``   triangle_down marker
 ``'^'``   triangle_up marker
 ``'<'``   triangle_left marker
 ``'>'``   triangle_right marker
 ``'1'``   tri_down marker
 ``'2'``   tri_up marker
 ``'3'``   tri_left marker
 ``'4'``   tri_right marker
 ``'s'``   square marker
 ``'p'``   pentagon marker
 ``'*'``   star marker
 ``'h'``   hexagon1 marker
 ``'H'``   hexagon2 marker
 ``'+'``   plus marker
 ``'x'``   x marker
 ``'D'``   diamond marker
 ``'d'``   thin_diamond marker
 ``'|'``   vline marker
 ``'_'``   hline marker
 ============= ===============================

线型参数**Line Styles**，linestyle='-'

 ============= ===============================
 character  description
 ============= ===============================
 ``'-'``   solid line style 实线
 ``'--'``   dashed line style 虚线
 ``'-.'``   dash-dot line style 点画线
 ``':'``   dotted line style 点线
 ============= ===============================

样例1

函数原型：matplotlib.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

>>> plot('xlabel', 'ylabel', data=obj)

解释：All indexable objects are supported. This could e.g. be a dict, a pandas.DataFame or a structured numpy array.

data 参数接受一个对象数据类型，所有可被索引的对象都支持，如 dict 等

import matplotlib.pyplot as plt 
import numpy as np
'''read file 
fin=open("para.txt")
a=[]
for i in fin:
 a.append(float(i.strip()))
a=np.array(a)
a=a.reshape(9,3)
'''
a=np.random.random((9,3))*2 #随机生成y

y1=a[0:,0]
y2=a[0:,1]
y3=a[0:,2]

x=np.arange(1,10)

ax = plt.subplot(111)
width=10
hight=3
ax.arrow(0,0,0,hight,width=0.01,head_width=0.1, head_length=0.3,length_includes_head=True,fc='k',ec='k')
ax.arrow(0,0,width,0,width=0.01,head_width=0.1, head_length=0.3,length_includes_head=True,fc='k',ec='k')

ax.axes.set_xlim(-0.5,width+0.2)
ax.axes.set_ylim(-0.5,hight+0.2)

plotdict = { 'dx': x, 'dy': y1 }
ax.plot('dx','dy','bD-',data=plotdict)

ax.plot(x,y2,'r^-')
ax.plot(x,y3,color='#900302',marker='*',linestyle='-')

plt.show()

样例2，

import matplotlib.pyplot as plt 
import numpy as np 

x = np.arange(0, 2*np.pi, 0.02) 
y = np.sin(x) 
y1 = np.sin(2*x) 
y2 = np.sin(3*x) 
ym1 = np.ma.masked_where(y1 > 0.5, y1) 
ym2 = np.ma.masked_where(y2 < -0.5, y2) 

lines = plt.plot(x, y, x, ym1, x, ym2, 'o') 
#设置线的属性
plt.setp(lines[0], linewidth=1) 
plt.setp(lines[1], linewidth=2) 
plt.setp(lines[2], linestyle='-',marker='^',markersize=4) 
#线的标签
plt.legend(('No mask', 'Masked if > 0.5', 'Masked if < -0.5'), loc='upper right') 
plt.title('Masked line demo') 
plt.show()

例3 ：圆

import numpy as np
import matplotlib.pyplot as plt

theta = np.arange(0, 2*np.pi, 0.01)
xx = [1,2,3,10,15,8]
yy = [1,-1,0,0,7,0]
rr = [7,7,3,6,9,9]

fig = plt.figure()
axes = flg.add_subplot(111)

i = 0
while i < len(xx):
 x = xx[i] + rr[i] *np.cos(theta)
 x = xx[i] + rr[i] *np.cos(theta)
 axes.plot(x,y)
 axes.plot(xx[i], yy[i], color='#900302', marker='*')
  i = i+1
width = 20
hight = 20
axes.arrow(0,0,0,hight,width=0.01,head_width=0.1,head_length=0.3,fc='k',ec='k')
axes.arrow(0,0,width,0,width=0.01,head_width=0.1,head_length=0.3,fc='k',ec='k')
plt.show()

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