数据分析之Pandas-03绘图函数

01-线型图

简单的Series图标示例

np.random.seed(0)
s = Series(np.random.randn(10).cumsum(),index = np.arange(0,100,10))
s.plot()

简单的DataFrame图标示例

np.random.seed(0)
df = DataFrame(np.random.randn(10,4).cumsum(0),
columns= ['A','B','C','D'],
index = np.arange(0,100,10))
plt.show(df.plot())

02-柱状图

水平和垂直柱状图

fig,axes = plt.subplots(2,1)
data = Series(np.random.rand(16),index = list('abcdefghijklmnop'))
data.plot(kind = 'bar',ax = axes[0],color = 'b',alpha = 0.9)
data.plot(kind = 'barh',ax = axes[1],color = 'b',alpha = 0.9)

DataFrame柱状图示例

ig,axes = plt.subplots(2,1)
data = Series(np.random.rand(16),index = list('abcdefghijklmnop'))
data.plot(kind = 'bar',ax = axes[0],color = 'b',alpha = 0.9)
data.plot(kind = 'barh',ax = axes[1],color = 'b',alpha = 0.9)

03-直方图

直方图是一种可以对值频率进行离散化显示的柱状图

通过Series的hist方法

random随机数百分比的直方图

a = np.random.random(10)
b = a/a.sum()
s = Series(b)
plt.show(s.hist(bins = 100)) #bins直方图的柱数

04-密度图

random随机数百分比的密度图

带有密度估计的规格化直方图

%matplotlib inline
comp1 = np.random.normal(0,1,size = 200)
comp2 = np.random.normal(10,2,size = 200)
values = Series(np.concatenate([comp1,comp2]))
p1 = values.hist(bins = 100,alpha = 0.3,color = 'k',normed = True)

p2 = values.plot(kind = 'kde',style = '--',color = 'r')

05-散布图

一张简单散布图

df = DataFrame(np.random.randint(0,100,size = 100).reshape(50,2),columns = ['A','B'])

df.plot('A','B',kind = 'scatter',title = 'x Vs y')

散布图矩阵

import numpy as np
import pandas as pd
from pandas import Series,DataFrame
%matplotlib inline
df = DataFrame(np.random.randn(200).reshape(50,4),columns = ['A','B','C','D'])
pd.plotting.scatter_matrix(df,diagonal = 'kde',color = 'k')