Python data visualization ---- draw various graphics

1. Environment

System: windows10

python version: python3.6.1

Libraries used: matplotlib, numpy

2. numpy library generates several methods of random numbers

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) The query result is a standard normal distribution	In [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(low,high,size)	Generate between low and 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 between low and high (closed interval [low, high)), size data In [10]: x=np.random.random_integers(2,10,5) In [11]: x Out[11]: array([7, 4, 5, 4, 2])

3. Scatter plot

xx axis
yy轴
s dot area
c color
marker dot shape
Alpha dot transparency #Other pictures are similar to this configuration

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**7
plt.plot(x,y)
plt.show()

Line chart using plot function

5. Bar Chart

N=5
y=[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 sets the horizontal bar graph

N=5
y=[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() 加了h就是横向的条形图，不用设置orientation
plt.bar(left=0,bottom=index,width=y,color='red',height=0.5,orientation='horizontal')
plt.show()

6. Histogram

m1=100
sigma=20
x=m1+sigma*np.random.randn(2000)
plt.hist(x,bins=50,color="green",normed=True)
plt.show()

# # Histogram of two variables
# #The darker the color, the higher the frequency
## Study the joint distribution of bivariate

#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
x=np.random.rand(1000)+2
y=np.random.rand(1000)+3
plt.hist2d(x,y,bins=40)
plt.show()

7. Pie Chart

#Set the x,y axis ratio to 1:1, so as to achieve a positive circle

#labels label parameter, x is the corresponding data list, autopct shows the proportion of each area, explode highlights a block, shadow shadow

labes=['A','B','C','D']
fracs=[15,30,45,10]
explode=[0,0.1,0.05,0]
#设置x,y轴比例为1：1，从而达到一个正的圆
plt.axes(aspect=1)
#labels标签参数,x是对应的数据列表,autopct显示每一个区域占的比例,explode突出显示某一块,shadow阴影
plt.pie(x=fracs,labels=labes,autopct="%.0f%%",explode=explode,shadow=True)
plt.show()

8. Box Plot

import matplotlib.pyplot as plt
import numpy as np
data=np.random.normal(loc=0,scale=1,size=1000)
#sym 点的形状，whis虚线的长度
plt.boxplot(data,sym="o",whis=1.5)
plt.show()

#sym The shape of the point, the length of the whis dotted line

 

github address: https://github.com/nanxung/python-.git

Code cloud address: https://git.oschina.net/nanxun/pythonshujukeshihuajiantu.git