1.使用函数绘制matplotlib的图表组成元素
（1）函数plot---变量的变化趋势
import matplotlib.pyplot as plt
import numpy as np
x = np.linespace(0.05, 10, 1000) #在x轴均匀取1000个点
y = np.cos(x) #对应的y值
plt.plot(x,y,ls="-", lw=2, label="plot figure")
'''
ls-------->线条的风格
lw--------->线条的宽度
label-------->标记图形内容的标签文本
'''
（2）函数scatter------寻找变量间的关系
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05, 10, 1000)
y = np.random.rand(1000)
plt.scatter(x, y, label="scatter figure")
plt.legend()
plt.show()
(三) 函数xlim()----------设置x轴的数值显示范围
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.random.rand(1000)
plt.scatter(x, y, label="scatter figure")
plt.legend()
plt.xlim(2, 10) #x轴的显示范围
plt.ylim(0,1)
plt.show()
(四)函数xlabel()--------设置x轴的标签文本
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.xlabel("x-axis") #x轴的标签
plt.ylabel("y-axis")
plt.show()
（五）函数grid---------绘制刻度线的网格线
import matplotlib.pyplot as plt
import numpy as np
x = p.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.grid(linestyle="-", color="r")#linestyle------>线型=ls color------->颜色=c
plt.show()
（六）函数axhline()------绘制平行于x轴的水平参考线
import matplotlib.pyplot as plt
import numpy as np
x = p.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.axhline(y = 0.0, c="c", ls="--", lw=2) #axh轴代表水平
plt.axvline(x = 4.0, c="c", ls="--", lw=2) #axv代表竖直
plt.show()
（七）函数axvspan()---------绘制垂直于x轴的参考区域
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.axvspan(xmin=4.0, xmax=6, facecolor="y", alpha = 0.3) #设置x轴的范围，范围颜色用facecolor
plt.axhspan(ymin=0, ymax=0.5, facecolor="y", alpha = 0.3)
plt.show()
（八）函数annotate（）-----------添加图形内容细节的指向型注释文本，text（）函数与其差不多plt.text(x, y, string, weight, color)
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.annotate("maximum",xy=(np.pi/2, 1),xytext = ((np.pi/2)+1.0, 0.8), weight = "bold", color = "b", arrowprops = dict(arrowstyle="->",connectionstyle = "arc3",color = "b"))
'''
string----->图形内容的注释文本
xy------->被注释图形内容的位置坐标
xytext------>注释文本的内容
weight------->注释文本的字体颜色
arrowprops------>指示被注释内容的箭头的属性字典
'''
plt.show()
(九)函数title()-----添加图形内容的标题
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c", label="plot figure") #c为颜色设置
plt.legend()
plt.title("y=sin(x)")#添加标题
plt.show()
（十）函数legend------标示不同图形的文本标签图例
import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x, y, ls="-.", lw=2, c="c",label = "flot figure") #c为颜色设置
plt.legend(loc="upper right")#flot figure的位置，upper，left，right，lower等组合而成
plt.show()
