3.Python 学习系列--------Python 可视化实战条形图

import numpy as np
import matplotlib.pyplot as plt

N=5
y= [20,10,15,13,13]
index = np.arange(N)
pl = plt.bar(left=index,height=y,color='blue',width=0.8)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

N=5
y= [20,10,15,13,13]
index = np.arange(N)
pl = plt.bar(left=0,bottom=index,width=y,color='red',height=0.5,orientation='horizontal')
plt.show()

import numpy as np
import matplotlib.pyplot as plt

N=5
y= [20,10,15,13,13]
index = np.arange(N)

pl = plt.barh(left=0,bottom=index,width=y)   #用barh函数
plt.show()

2.多个项目在一起的条形图(在开始的时候加上一个线宽，+bar_width)

import numpy as np
import matplotlib.pyplot as plt

index=np.arange(5)
sales_A = [20,10,15,13,13]
sales_B = [12,14,14,15,15]

bar_width = 0.3

pl = plt.bar(index,sales_A,bar_width,color='r')
pl = plt.bar(index+bar_width,sales_B ,bar_width,color='b')
plt.show()

3.生成层叠的条形图

import numpy as np
import matplotlib.pyplot as plt

index=np.arange(5)
sales_A = [20,10,15,13,13]
sales_B = [12,14,14,15,15]

bar_width = 0.3

pl = plt.bar(index,sales_A,bar_width,color='r')
pl = plt.bar(index,sales_B ,bar_width,color='b')
plt.show()

2.直方图的画法

 

import numpy as np
import matplotlib.pyplot as plt

mu = 100  #均值
sigma = 20 # 标准差
x= mu + sigma*np.random.randn(2000) 
plt.hist(x,bins= 100,color = 'red',normed=True)# 直方图用hist函数，三个比较常用的函数，几个直方，bins ,normed 是指的是否进行标准化
plt.show()

import numpy as np
import matplotlib.pyplot as plt
x=np.random.randn(1000)+2
y= np.random.randn(1000)+3
plt.hist2d(x,y,bins=40)
plt.show()

绘制饼状图

import numpy as np
import matplotlib.pyplot as plt
labels='A', 'B','D','C'
explode=[0,0.2,0,0]
fraces = [15,30,45,10]
plt.axes(aspect=1)  #正圆的比例是1
plt.pie(x=fraces,labels =labels,autopct='%.of%%',explode=explode,shadow=True) #autopct是显示每个部分所占的比例，explode是突出显示某个部分
#0.5是距离中心的位置，shadow是是否利用立体阴影
plt.show()

4.箱型图又称为盒须图  上边缘，上四分位数，中位数，下四分位数，下边缘，异常值