利用python绘制线型图

目录

• x,y
• color
• linstyle
• 缩写方式
• marker, markersize
• label
• 一次性绘制三个线条图

用法：

matplot.pyplot.plot(*args, scalex=True, scaley=True, data=None, **kwargs)

参数解释：

x,y

import numpy as np
import matplotlib.pyplot as plt

x = np.arange(0.2, 2.0, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = np.sin(4*np.pi*x)

plt.figure(1)
plt.subplot(211)
plt.plot(x,y1)

plt.subplot(212)
plt.plot(x,y2)
plt.show()

color

Colors的值：

import numpy as np
import matplotlib.pyplot as plt

# 需要解释下,下面两行代码是防止出现中文时,会报警告
# 因为我们的title里面写的是中文
plt.rcParams[\'font.family\'] = \'SimHei\'
plt.rcParams[\'axes.unicode_minus\']=False
x = np.arange(0.2, 2.0, 0.01)
y1 = np.sin(2*np.pi*x)
y2 = np.sin(4*np.pi*x)

plt.figure(1)
plt.subplot(211)
plt.title(\'不添加颜色\')
plt.plot(x,y1)

plt.subplot(212)
plt.title(\'添加颜色\')
plt.plot(x,y2,color=\'c\')
plt.show()

linstyle

\'b\'    # blue markers with default shape
\'or\'   # red circles
\'-g\'   # green solid line
\'--\'   # dashed line with default color
\'^k:\'  # black triangle_up markers connected by a dotted line

import numpy as np
import matplotlib.pyplot as plt

plt.figsize=((10,8))
plt.rcParams[\'font.family\'] = \'SimHei\'
plt.rcParams[\'axes.unicode_minus\']=False

x = [1, 2, 3, 4]
y = [1, 4, 9, 16]

plt.subplot(221)
plt.title(\'样式: -\')
plt.plot(x,y,\'-\')

plt.subplot(222)
plt.title(\'样式: --\')
plt.plot(x,y,\'--\')

plt.subplot(223)
plt.title(\'样式: -.\')
plt.plot(x, y, \'-.\')

plt.subplot(224)
plt.title(\'样式: :\')
plt.plot(x, y, \':\')
plt.show()

缩写方式

import numpy as np
import matplotlib.pyplot as plt
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.subplot()
# 线形状 \'-\',颜色\'g\'
plt.plot(x, y, \'-g\')
plt.show()

marker, markersize

marker在scatter里面我已经有所解释过了，有好多种情况，可以在scatter散点图这里会将颜色和marker连接起来,可以有个很清楚的了解，并且较为清楚，也是缩写

import matplotlib.pyplot as plt
plt.figsize=((12,6))
plt.rcParams[\'font.family\'] = \'SimHei\'
plt.rcParams[\'axes.unicode_minus\']=False
x = [1, 2, 3, 4]
y = [1, 4, 9, 16]
plt.subplot(131)
plt.title(\'默认情况\')
plt.plot(x, y)

plt.subplot(132)
plt.title(\'红色圆圈\')
# marker为o 颜色r
plt.plot(x, y, \'or\')

plt.subplot(133)
plt.title(\'正三角黑色\')
# marker为^ 颜色k->black
plt.plot(x, y, \'^k\')
plt.show()

label

标签,这个在所有图形中都可以使用,在这里展示下,包括之前的alpha也是,都所属**kwargs里面，在任何绘图中都可以添加，legend为图例

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(-np.pi/2, np.pi/2, 31)
y = np.cos(x)**3

# 1) remove points where y > 0.7
x2 = x[y <= 0.7]
y2 = y[y <= 0.7]

# 2) mask points where y > 0.7
y3 = np.ma.masked_where(y > 0.7, y)

# 3) set to NaN where y > 0.7
y4 = y.copy()
y4[y3 > 0.7] = np.nan

plt.plot(x*0.1, y, \'o-\', color=\'lightgrey\', label=\'No mask\')
plt.plot(x2*0.4, y2, \'o-\', label=\'Points removed\')
plt.plot(x*0.7, y3, \'o-\', label=\'Masked values\')
plt.plot(x*1.0, y4, \'o-\', label=\'NaN values\')
plt.legend()
plt.show()

下面就是一些案例

一次性绘制三个线条图

import numpy as np
import matplotlib.pyplot as plt
t = np.arange(0., 5., 0.2)
# 红色虚线,蓝色方块,浅蓝六边形
plt.plot(t, t, \'r--\', t, t**2, \'bs\', t, t**3, \'cH\')
plt.show()

import numpy as np
import matplotlib.pyplot as plt
x1 = np.linspace(0.0, 5.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
x2 = np.linspace(0.0, 2.0)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(211)
plt.plot(x1, y1, \'o-\')
plt.subplot(212)
plt.plot(x1, y1, \'.-\')
plt.show()