# -*- coding: utf-8 -*-
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
'''
数据分类
'''
data=Series(np.random.randn(10),index=[['a','a','a','b','b','b','c','c','d','d'],[1,2,3,1,2,3,1,2,6,2]])
print(data)
# a 1 -0.130178
# 2 0.137005
# 3 1.941608
# b 1 0.768033
# 2 0.739661
# 3 -0.227032
# c 1 -0.106498
# 2 0.158814
# d 6 -0.291577
# 2 0.170918
#获取子集
print(data['a'])
# 1 -0.322706
# 2 0.474766
# 3 -1.054212
# dtype: float64
print(data['a':'c'])
# a 1 0.080364
# 2 0.216486
# 3 -0.038789
# b 1 1.365482
# 2 -0.283908
# 3 1.202427
# c 1 1.054704
# 2 0.120473
print(data[['a','c']])
# a 1 1.753260
# 2 -0.169620
# 3 -0.263574
# c 1 -0.840767
# 2 -0.901693
#获取内层
print(data[:,2])
# a 0.137005
# b 0.739661
# c 0.158814
# d 0.170918
#生成表格
print(data.unstack())
# 1 2 3 6
# a -0.130178 0.137005 1.941608 NaN
# b 0.768033 0.739661 -0.227032 NaN
# c -0.106498 0.158814 NaN NaN
# d NaN 0.170918 NaN -0.291577
#生成表格的逆运算
print(data.unstack().stack())
# a 1 -0.130178
# 2 0.137005
# 3 1.941608
# b 1 0.768033
# 2 0.739661
# 3 -0.227032
# c 1 -0.106498
# 2 0.158814
# d 2 0.170918
# 6 -0.291577
'''
dataFrame分类
'''
data=DataFrame(np.arange(12).reshape(4,3),index=[['a','a','b','c'],['1','2','1','2']],columns=[['hhb','hhb','zjx'],['man','men','man']])
print(data)
# hhb zjx
# man men wemen
# a 1 0 1 2
# 2 3 4 5
# b 1 6 7 8
# c 2 9 10 11
#给各层目录指定名称
data.index.names=['str','num']
data.columns.names=['name','sex']
print(data)
# name hhb zjx
# sex man men wemen
# str num
# a 1 0 1 2
# 2 3 4 5
# b 1 6 7 8
# c 2 9 10 11
#进行筛查获取
print(data['hhb'])
# sex man men
# str num
# a 1 0 1
# 2 3 4
# b 1 6 7
# c 2 9 10
print(data.T)
# str a b c
# num 1 2 1 2
# name sex
# hhb man 0 3 6 9
# men 1 4 7 10
# zjx wemen 2 5 8 11
'''
重新指定分级顺序，有时需求会调换分级顺序
'''
print(data.swaplevel('str','num'))
# sex man men wemen
# num str
# 1 a 0 1 2
# 2 a 3 4 5
# 1 b 6 7 8
# 2 c 9 10 11
#层次重排进行汇总
print(data.sortlevel(1))
# name hhb zjx
# sex man men wemen
# str num
# a 1 0 1 2
# b 1 6 7 8
# a 2 3 4 5
# c 2 9 10 11
print(data.swaplevel(0,1).sortlevel(0))
# name hhb zjx
# sex man men wemen
# num str
# 1 a 0 1 2
# b 6 7 8
# 2 a 3 4 5
# c 9 10 11
'''
统计
'''
print(data.sum(level='num'))
# name hhb zjx
# sex man men man
# num
# 1 6 8 10
# 2 12 14 16
print(data.sum(level='sex',axis=1))
# sex man men
# str num
# a 1 2 1
# 2 8 4
# b 1 14 7
# c 2 20 10
'''
Dataframe的列
'''
frame=DataFrame({'a':range(7),'b':range(7,0,-1),'c':['one','one','one','two','two','two','two'],'d':[0,1,2,0,1,2,3]})
print(frame)
# a b c d
# 0 0 7 one 0
# 1 1 6 one 1
# 2 2 5 one 2
# 3 3 4 two 0
# 4 4 3 two 1
# 5 5 2 two 2
# 6 6 1 two 3
#将set_index函数将一个或多个列索引转换为行索引，并创建一个新的Dataframe
data=frame.set_index(['c','d'])
print(data)
# a b
# c d
# one 0 0 7
# 1 1 6
# 2 2 5
# two 0 3 4
# 1 4 3
# 2 5 2
# 3 6 1
#保存原有的值
data2=frame.set_index(['c','d'],drop=False)
print(data2)
# a b c d
# c d
# one 0 0 7 one 0
# 1 1 6 one 1
# 2 2 5 one 2
# two 0 3 4 two 0
# 1 4 3 two 1
# 2 5 2 two 2
# 3 6 1 two 3
#还原层次化的列
frame=data.reset_index()
print(frame)
# c d a b
# 0 one 0 0 7
# 1 one 1 1 6
# 2 one 2 2 5
# 3 two 0 3 4
# 4 two 1 4 3
# 5 two 2 5 2
# 6 two 3 6 1
# from pandas_datareader import data, wb
#
# #我们使用pandas_datareader获得苹果股票信息:
#
# import pandas_datareader as pdr
#
# print(pdr.get_data_yahoo('AAPL'))
python数据分析九：pandas层次化索引
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转载自blog.csdn.net/qq_38788128/article/details/80695563
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