《python可视化工具之matplotlib》图表组成元素

作者: estate47 | 来源:发表于2019-04-16 19:45 被阅读3次

《python可视化工具之matplotlib》图表组成元素
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matplotlib库是python中绘制二维、三维图表的数据可视化工具。它的主要特点如下：
1.使用简单绘图语句实现复杂绘图效果
2.以交互式操作实现渐趋精细的图形效果
3.使用嵌入式的LaTex输出具有印刷级别的图表、科学表达式和符号文本
4.对图表的组成元素实现精细化控制
我们导入第三方包NumPy和快速绘图模块pyplot,其中科学计算包NumPy是matplotlib库的基础。

1.函数plot()---展现变量趋势变化

import matplotlib.pyplot as plt
import numpy as np
x = np.linspace(0.05,10,1000)#x轴上的数值
y = np.cos(x)#y轴上的数值
plt.plot(x,y,ls="-",lw=3,label="plot figure")
#ls折线图的线条风格，lw线条宽度、label标记图形内容的标签文本
plt.legend()
plt.show()

plot

2.函数scatter()---寻找变量之间的关系

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,c="b",label="scatter figure")
plt.legend()
plt.show()

scatter

3.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(0.05,10)
plt.ylim(0,1)
plt.show()

xlim

4.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=1,c="c",label="plot figure")
plt.legend()
plt.xlabel("x-axis")
plt.ylabel("y-axis")
plt.show()

xlabel

5.函数grid()---绘制刻度线的网络线

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=1,c="c",label="plot figure")
plt.legend()
plt.grid(linestyle=":",color="r")
plt.show()

grid

6.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 = np.linspace(0.05,10,1000)
y = np.sin(x)
plt.plot(x,y,ls="-.",lw=2,c="c",label="plot figure")
plt.legend()
plt.axhline(y=0.0,c="r",ls="--",lw=2)
plt.axvline(x=4.0,c="r",ls="--",lw=2)
plt.show()

axhline

7.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")
plt.legend()
plt.axvspan(xmin=4.0,xmax=6.0,facecolor="y",alpha=0.3)
plt.axhspan(ymin=0.0,ymax=0.5,facecolor="y",alpha=0.3)
plt.show()

axvspan

8.函数annotate()---添加图形内容细节的指向型注释文本

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")
plt.legend()
plt.annotate("maximum",
             xy=(np.pi/2,1.0),#被注释图形内容的位置坐标
             xytext=((np.pi/2)+1.0,.8),#注释文本的位置坐标
             weight="bold",
             color="b",
             arrowprops
=dict(arrowstyle="->",connectionstyle="arc3",color="b"))
#arrowprops指示被注释内容的箭头的属性字典
plt.show()

annotate

9.函数text()---添加图形内容细节的无指向型注释文本

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")
plt.legend()
plt.text(3.1,0.09,"y=sin(x)",weight="bold",color="b")
plt.show()

text

10.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")
plt.legend()
plt.title("y=sin(x)")
plt.show()

title

11.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="plot figure")
plt.legend(loc="lower left")#loc图例在图中的地理位置
plt.show()

legend

函数组合应用

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm as cm
#define data
x =np.linspace(0.5,3.5,100)
y =np.sin(x)
y1 = np.random.randn(100)

#scatter figure
plt.scatter(x,y1,c="0.25",label="scatter figure")

#plot figure
plt.plot(x,y,ls="--",lw=2,label="plot figure")

#some clear up(removing chartjunk)
#turn the top spine and the right spine off
for spine in plt.gca().spines.keys():
    if spine =="top" or spine =="right":
        plt.gca().spines[spine].set_color("none")

#turn bottom tick for x-axis on
plt.gca().xaxis.set_ticks_position("bottom")
#set tick_line position of bottom

#turn left ticks for y-axis on
plt.gca().yaxis.set_ticks_position("left")
#set tick_line position of left

#set x,yaxis limit
plt.xlim(0.0,4.0)
plt.ylim(-3.0,3.0)
#set axes labels
plt.ylabel("y_axis")
plt.xlabel("x_axis")

#set x,yaxis grid
plt.grid(True,ls=":",color="r")

#add a horizontal line across the axis
plt.axhline(y=0.0,c="r",ls="--",lw=2)

#add a vertical span across the axis
plt.axvspan(xmin=1.0,xmax=2.0,facecolor="y",alpha=.3)

#set annotate info
plt.annotate("maximum",xy=(np.pi/2,1.0),
             xytext=((np.pi/2)+0.15,1.5),weight="bold",color="r",
arrowprops=dict(arrowstyle="->",connectionstyle="arc3",color="r"))
plt.annotate("spines",xy=(0.75,-3),
             xytext=(0.35,-2.25),weight="bold",color="b",
arrowprops=dict(arrowstyle="->",connectionstyle="arc3",color="b"))
plt.annotate("",xy=(0,-2.78),
             xytext=(0.4,-2.32),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3", color="r"))
plt.annotate("",xy=(3.5,-2.98),
             xytext=(3.6,-2.7),
arrowprops=dict(arrowstyle="->", connectionstyle="arc3", color="r"))
#set text info
plt.text(3.6,-2.7,"'|' is tickline",weight="bold",color="b")
plt.text(3.6,-2.95,"3.5 is ticklabel",weight="bold",color="b")

#set title
plt.title("structure of matplotlib")
#set legend
plt.legend()
plt.show()

函数组合