Example of drawing with Python

1. Environment

System: WINDOWS10

Python version: python3.6.1

Libraries used: Matplotlib,numpy

Several methods of generating random numbers in 2.numpy library

Import NumPy as NP

Numpy.random

rand (D0, D1, ..., dn)	In [2]: X=np.random.rand (2,5) In [3]: x OUT[3]: Array ([[0.84286554, 0.50007593, 0.66500549, 0.97387807, 0.03993009], [0.46391661, 0.50717355, 0.21527461, 0.92692517, 0.2567891]])
Randn (D0, D1, ..., DN) query result as standard normal distribution	I n [4]: X=np.random.randn (2,5) In [5]: x Out[5]: Array ([[-0.77195196, 0.26651203,-0.35045793,-0.0210377, 0.89749635], [ -0.20229338, 1.44852833, -0.10858996, -1.65034606, -0.39793635]])
randint (lo w,high,size)	Generate low to high (half open interval [low, high), size data In [6]: X=np.random.randint (1,8,4) in [7 ]: x Out[7]: Array ([4, 4, 2, 7])
random_integers (low,high,size)	Generate low to High ( Closed interval [Low, high), size data In [ten]: X=np.random.random_integers (2,10,5) In [all]: x Out[11]: Array ([7, 4, 5 , 4, 2])

3. Scatter chart

x x axis y y axis s   dot Area C   color marker dot  shape alpha   dot transparency                #其他图也类似这种配置

n=50# Height=np.random.randint (150,180,20) # weight=np.random.randint (80,150,20) x=np.random.randn (N) y= Np.random.randn (N) plt.scatter (x,y,s=50,c= ' R ', marker= ' o ', alpha=0.5) plt.show ()

4. Line chart

x=np.linspace(-10000,10000,100) #将-10到10等区间分成100份y=x**2+x**3+x**7plt.plot(x,y)plt.show()

Line chart using the plot function

5. Bar chart

N=5y=[20,10,30,25,15]y1=np.random.randint (10,50,5) x=np.random.randint (10,1000,n) index=np.arange (N) Plt.bar (left =index,height=y,color= ' Red ', width=0.3) plt.bar (left=index+0.3,height=y1,color= ' black ', width=0.3) plt.show ()

Orientation setting a horizontal bar chart

N=5y=[20,10,30,25,15]y1=np.random.randint (10,50,5) x=np.random.randint (10,1000,n) Index=np.arange (N) # Plt.bar ( Left=index,height=y,color= ' Red ', width=0.3) # Plt.bar (left=index+0.3,height=y1,color= ' black ', width=0.3) #plt. Barh ( Plus h is the horizontal bar chart without setting Orientationplt.bar (left=0,bottom=index,width=y,color= ' red ', height=0.5,orientation= ' Horizontal ') plt.show ()

6. Histogram

M1=100sigma=20x=m1+sigma*np.random.randn (plt.hist) (x,bins=50,color= "green", Normed=true) Plt.show ()

# #双变量的直方图 # #颜色越深频率越高 # #研究双变量的联合分布

#双变量的直方图 # The darker the color the higher the frequency # study on the joint distribution of Bivariate X=np.random.rand (+2y=np.random.rand) +3plt.hist2d (x,y,bins=40) plt.show ()

7. Pie chart

#设置x, the y-axis ratio is 1:1, thus achieving a positive circle

#labels标签参数, X is the corresponding data list, autopct shows the proportion of each area, explode highlights a piece, Shadow Shadow

Labes=[' A ', ' B ', ' C ', ' D ']fracs=[15,30,45,10]explode=[0,0.1,0.05,0] #设置x, the y-axis ratio is 1:1, thus reaching a positive circle Plt.axes (aspect=1) # Labels tag parameter, x is the corresponding data list, autopct shows the proportion of each area, explode highlight a piece, Shadow Shadow Plt.pie (x=fracs,labels=labes,autopct= "%.0f%%", explode=explode,shadow=true) Plt.show ()

8. Box-type diagram

import Matplotlib.pyplot as Pltimport numpy as Npdata=np.random.normal (loc=0, scale=1,size=1000) The shape of the #sym point, whis the length of the dashed line Plt.boxplot (data,sym= "O", whis=1.5) plt.show () 

# Sym the shape of the point, whis the length of the dashed line