Summary of using Python to draw charts-Python Tutorial-php.cn

Summary of using Python to draw charts

高洛峰

Release： 2017-02-13 13:38:11

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This article mainly introduces a comprehensive summary of using Python to draw charts. The editor thinks it is quite good. Now I will share it with you and give it as a reference. Let’s follow the editor and take a look.

Before using Python to draw charts, we need to install two library files, numpy and matplotlib.

Numpy is an open source numerical computing extension for Python, which can be used to store and process large matrices and is more efficient than Python's own data structure; matplotlib is a Python image framework, using its graphic effects and drawing under MATLAB The graphics are similar.

Below I will introduce how to use Python to draw through some simple code.

1. Graphic drawing

Summary of using Python to draw charts

##Histogram

importmatplotlib.pyplotasplt

importnumpyasnp

mu=100

sigma=20

x=mu+sigma*np.random.randn(20000)# 样本数量

plt.hist(x,bins=100,color='green',normed=True)# bins显示有几个直方,normed是否对数据进行标准化

plt.show()

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Bar chart

importmatplotlib.pyplotasplt

importnumpyasnp

y=[20,10,30,25,15]

index=np.arange(5)

plt.bar(left=index,height=y,color='green',width=0.5)

plt.show()

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Line chart

importmatplotlib.pyplotasplt

importnumpyasnp

x=np.linspace(-10,10,100)

y=x**3

plt.plot(x,y,linestyle='--',color='green',marker='<')

plt.show()

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scatterplot

importmatplotlib.pyplotasplt

importnumpyasnp

x=np.random.randn(1000)

y=x+np.random.randn(1000)*0.5

plt.scatter(x,y,s=5,marker='<')# s表示面积，marker表示图形

plt.show()

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pie chart

importmatplotlib.pyplotasplt

importnumpyasnp

labels='A','B','C','D'

fracs=[15,30,45,10]

plt.axes(aspect=1)#使x y轴比例相同

explode=[0,0.05,0,0]# 突出某一部分区域

plt.pie(x=fracs,labels=labels,autopct='%.0f%%',explode=explode)#autopct显示百分比

plt.show()

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boxplot

Mainly used to display the dispersion of data. The graph is divided into upper edge, upper quartile, median, lower quartile, and lower edge. The outside points are outliers

importmatplotlib.pyplotasplt

importnumpyasnp

np.random.seed(100)

data=np.random.normal(size=(1000,4),loc=0,scale=1)

labels=['A','B','C','D']

plt.boxplot(data,labels=labels)

plt.show()

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2. Image adjustment

1. 23 point shapes

"."point","pixel"o"circle"v"triangle_down

"^"triangle_up"<"triangle_left">"triangle_right"1"tri_down

"2"tri_up"3"tri_left"4"tri_right"8"octagon

"s"square"p"pentagon"*"star"h"hexagon1"H"hexagon2

"+"plus"x"x"D"diamond"d"thin_diamond

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2. 8 built-in default color abbreviations

b:blueg:greenr:redc:cyan

m:magentay:yellowk:blackw:white

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3. 4 types of linearity

- solid line--dashed line-.dash line: dotted line

4. Draw sub-pictures on one picture

Summary of using Python to draw charts

importmatplotlib.pyplotasplt

importnumpyasnp

x=np.arange(1,100)

plt.subplot(221)#2行2列第1个图

plt.plot(x,x)

plt.subplot(222)

plt.plot(x,-x)

plt.subplot(223)

plt.plot(x,x*x)

plt.subplot(224)

plt.plot(x,np.log(x))

plt.show()

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5. Generate grid

Summary of using Python to draw charts

importmatplotlib.pyplotasplt

importnumpyasnp

y=np.arange(1,5)

plt.plot(y,y*2)

plt.grid(True,color='g',linestyle='--',linewidth='1')

plt.show()

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6. Generate legend

Summary of using Python to draw charts

importmatplotlib.pyplotasplt

importnumpyasnp

x=np.arange(1,11,1)

plt.plot(x,x*2)

plt.plot(x,x*3)

plt.plot(x,x*4)

plt.legend(['Normal','Fast','Faster'])

plt.show()

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