pandas數據新索引：reindex

DataFrame.reindex(labels=None, index=None, columns=None, axis=None, method=None, copy=True, level=None, fill_value=nan, limit=None, tolerance=None)

常用關鍵參數：

method：插值填充方法

fill_value：引入缺失數據值

columns：列的重新索引

level：在多層索引上匹配簡單索引

(1)、fill_value默認爲nan

import pandas as pd
obj = pd.Series([4.5, 7.2, -5.3, 3.6], index=['d', 'b', 'a', 'c'])

d    4.5
b    7.2
a   -5.3
c    3.6
dtype: float64

obj2 = obj.reindex(['a', 'b', 'c', 'd', 'e'])

a   -5.3
b    7.2
c    3.6
d    4.5
e    NaN
dtype: float64

仔細觀察會發現obj2還實現了index的重新索引

(2)、fill_value填充指定值

obj3 = obj.reindex(['a', 'b', 'c', 'd', 'e'], fill_value=0)

a   -5.3
b    7.2
c    3.6
d    4.5
e    0.0
dtype: float64

(3)、method用於控制填充值方式
‘backfill’/’bfill’：向後填充

obj4 = pd.Series(['blue', 'purple', 'yellow'], index=[0, 2, 4])
obj4.reindex(range(6), method='bfill')

0      blue
1    purple
2    purple
3    yellow
4    yellow
5       NaN
dtype: object

‘pad’/’ffill’,：向前填充

obj4.reindex(range(6), method='ffill')

0      blue
1      blue
2    purple
3    purple
4    yellow
5    yellow
dtype: object

（4）、columns用於列的重新索引

import pandas as pd
import numpy as np
frame = pd.DataFrame(np.arange(9).reshape((3, 3)), index=['a', 'c', 'd'],columns=['Ohio', 'Texas', 'California'])

 	Ohio 	Texas 	California
a 	0 	1 	2
c 	3 	4 	5
d 	6 	7 	8

frame.reindex(columns=['Texas', 'Utah', 'California'],fill_value = 0)

 	Texas 	Utah 	California
a 	1 	0 	2
c 	4 	0 	5
d 	7 	0 	8