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()

参考自：https://blog.csdn.net/sinat_36219858/article/details/79800460