# -*- coding: utf-8 -*-
import pandas as pd
from pandas import Series,DataFrame
import numpy as np
'''
索引对象
'''
obj=Series(range(3),index=['a','b','c'])
print(obj)
# a 0
# b 1
# c 2
# dtype: int64
#展示index
index=obj.index
print(index[1])#b
'''
index是不能修改，是为了多个数据索引的安全共享
'''
#index[1]='d'
#TypeError: Index does not support mutable operations
index=pd.Index(np.arange(4))
obj2=Series([1,2,3,5],index=index)
print(obj2.index is index)
#True
'''
判断行索引 列索引是否存在
'''
dict={'hhb':{'2000':1.2,'2001':1.3,'2003':1.5},'zjx':{'2001':1.2,'2003':1.4}}
data=DataFrame(dict)
print(data)
# hhb zjx
# 2000 1.2 NaN
# 2001 1.3 1.2
# 2003 1.5 1.4
print('hh' in data.columns)#False
print('2003' in data.index)#True
'''
重新定义索引
'''
obj=Series([1,3,4,5],index=['a','b','c','d'])
print(obj)
obj2=obj.reindex(['a','b','c','d','e'])
print(obj2)
# a 1.0
# b 3.0
# c 4.0
# d 5.0
# e NaN
#加入默认值
obj2=obj.reindex(['a','b','c','d','e'],fill_value=0)
print(obj2)
# a 1
# b 3
# c 4
# d 5
# e 0
#前向填充
obj3=Series(['blue','purple','yellow'],index=[0,2,4])
obj4=obj3.reindex(range(6),method='ffill')
print(obj4)
# 0 blue
# 1 blue
# 2 purple
# 3 purple
# 4 yellow
# 5 yellow
'''
DataFrame,重定义索引
'''
frame=DataFrame(np.arange(9).reshape((3,3)),index=['a','b','c'],columns=['Ohio','Texas','California'])
print(frame)
# Ohio Texas California
# a 0 1 2
# b 3 4 5
# c 6 7 8
frame2=frame.reindex(['a','b','c','d'])
print(frame2)
# Ohio Texas California
# a 0.0 1.0 2.0
# b 3.0 4.0 5.0
# c 6.0 7.0 8.0
# d NaN NaN NaN
statue=["Ohio","Utah","California"]
frame3=frame.reindex(columns=statue)
print(frame3)
# Ohio Utah California
# a 0 NaN 2
# b 3 NaN 5
# c 6 NaN 8
#向上赋值
frame4=frame3.reindex(index=['a','b','c','d'],method='ffill',columns=statue)
print(frame4)
# Ohio Utah California
# a 0 NaN 2
# b 3 NaN 5
# c 6 NaN 8
# d 6 NaN 8
#使用ix简介加入
# frame5=frame.ix[['a','b','c','d'],statue]
# print(frame5)
'''
丢弃指定轴上的项
'''
obj=Series(np.arange(5),index=['a','b','c','d','e'])
new_obj=obj.drop('c')
print(new_obj)
# a 0
# b 1
# d 3
# e 4
new_obj=obj.drop(['a','b'])
print(new_obj)
# c 2
# d 3
# e 4
'''
DataFrame同
'''
data=DataFrame(np.arange(16).reshape((4,4)),index=['Ohio','Colorado','Utah','New York'],columns=['one','two','three','four'])
print(data)
# one two three four
# Ohio 0 1 2 3
# Colorado 4 5 6 7
# Utah 8 9 10 11
# New York 12 13 14 15
data2=data.drop('New York')
print(data2)
# one two three four
# Ohio 0 1 2 3
# Colorado 4 5 6 7
# Utah 8 9 10 11
data3=data2.drop(['four','three'],axis=1)
print(data3)
# one two
# Ohio 0 1
# Colorado 4 5
# Utah 8 9
'''
索引的选取和过滤
'''
#Series
obj=Series(np.arange(4),index=['a','b','c','d'])
print(obj)
# a 0
# b 1
# c 2
# d 3
print(obj['b'])#1
print(obj[1])#1
print(obj[['b','c']])
# b 1
# c 2
print(obj[1:3])
# b 1
# c 2
print(obj[obj<2])
# a 0
# b 1
print(obj['b':'c'])
# b 1
# c 2
#赋值
obj['b':'c']=5
print(obj)
# a 0
# b 5
# c 5
# d 3
data=DataFrame(np.arange(16).reshape(4,4),index=['Oh','Ny','CN','USA'],columns=['one','two','three','four'])
print(data)
#行
print(data[['one','three']])
# one three
# Oh 0 2
# Ny 4 6
# CN 8 10
# USA 12 14
#列
print(data[:2])
# one two three four
# Oh 0 1 2 3
# Ny 4 5 6 7
print(data[data['three']>5])
# one two three four
# Ny 4 5 6 7
# CN 8 9 10 11
# USA 12 13 14 15
#返回blur
print(data>5)
# one two three four
# Oh False False False False
# Ny False False True True
# CN True True True True
# USA True True True True
#赋值
data[data<5]=0
print(data)
# one two three four
# Oh 0 0 0 0
# Ny 0 5 6 7
# CN 8 9 10 11
# USA 12 13 14 15
'''
根据索引获取值
'''
data2=data.ix['USA',['one','two']]
print(data2)
# one 12
# two 13
# Name: USA, dtype: int32
data3=data.ix[['USA','CN'],[3,2,0]]
print(data3)
# four three one
# USA 15 14 12
# CN 11 10 8
print(data.ix[2])
# one 8
# two 9
# three 10
# four 11
# Name: CN, dtype: int32
print(data.ix[:'CN','two'])
# Oh 0
# Ny 5
# CN 9
# Name: two, dtype: int32
print(data.ix[data.three>5,:3])
# one two three
# Ny 0 5 6
# CN 8 9 10
# USA 12 13 14
