Detailed explanation of examples of drawing graphics with python

1. Environment

System: windows10

python version: python3.6.1

Libraries used: matplotlib, numpy

2. Several ways for the numpy library to generate random numbers

import numpy as np

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numpy.random##random_integers(low,high,size)

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 is 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, 1.44 852833 , -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])
	Generate size data between low and high (closed interval [low, high)) In [10]: x=np.random.random_integers(2,10,5) In [11]: x Out[11]: array([7, 4, 5, 4, 2])

3.Scatter chart

x x轴
y y轴
s   圆点面积
c   颜色
marker  圆点形状
alpha   圆点透明度                #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()

<code class="language-python hljs"># 来源：百度网盘搜索 <br/>x=np.linspace(<span class="hljs-number">-10000,<span class="hljs-number">10000,<span class="hljs-number">100) <span class="hljs-comment">#将-10到10等区间分成100份
y=x**<span class="hljs-number">2+x**<span class="hljs-number">3+x**<span class="hljs-number">7
plt.plot(x,y)
plt.show()</span></span></span></span></span></span></span></code>

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 set horizontal 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.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()

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##6. Histogram

m1=100
sigma=20
x=m1+sigma*np.random.randn(2000)
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

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#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()

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7. Pie Chart

#设置x,y轴比例为1：1，从而达到一个正的圆

#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

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 pltimport numpy as npdata=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 点的形状，whis虚线的长度