pandas replace 替换功能function

1. list like replace method
2. dict like replace method
3. regex expression

import pandas as pd
import numpy as np

s = pd.Series([0,1,2,3,4])

s.replace(0,5)  # single value to replace

0    5
1    1
2    2
3    3
4    4
dtype: int64

df = pd.DataFrame({'A':[0,1,2,3,4],
                  "B":[5,6,7,8,9],
                  "C":['a','b','c','d','e']})

df.replace(0,5)  # replace all 0 to 5

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	A	B	C
0	5	5	a
1	1	6	b
2	2	7	c
3	3	8	d
4	4	9	e

df  # the default parameter in_place= False
# DataFrame.replace(to_replace=None, value=None, inplace=False, limit=None, regex=False, method='pad')
# to_place can be number,string list or dict and even regex expression
# limit   Maximum size gap to forward or backward fill.

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	A	B	C
0	0	5	a
1	1	6	b
2	2	7	c
3	3	8	d
4	4	9	e

1. list like replace method

df.replace([1,2,3,4],[4,3,2,1])  # content to replace . to_replace=[1,2,3,4],value=[4,3,2,1]

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	A	B	C
0	0	5	a
1	4	6	b
2	3	7	c
3	2	8	d
4	1	9	e

df.replace([1,2,3,4],100)  # to_replace=[1,2,3,4],value=4

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	A	B	C
0	0	5	a
1	100	6	b
2	100	7	c
3	100	8	d
4	100	9	e

df.replace([1,2],method='bfill')   # . like fillna with mehtod bfill(backfill), and the default mehtod was pad

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	A	B	C
0	0	5	a
1	3	6	b
2	3	7	c
3	3	8	d
4	4	9	e

2. dict like replace method

df.replace({2:20,6:100})  # to_replace =2 value=20,to_replace=6,value =100

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	A	B	C
0	0	5	a
1	1	100	b
2	20	7	c
3	3	8	d
4	4	9	e

df.replace({'A':2,'B':7},1000)  # . to_replace={'A':2,"B":7}, value=1000

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	A	B	C
0	0	5	a
1	1	6	b
2	1000	1000	c
3	3	8	d
4	4	9	e

df.replace({'A':{1:1000,4:20}})   # in colomn A to_replace=1,value=1000, to_replace=4, value=20

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	A	B	C
0	0	5	a
1	1000	6	b
2	2	7	c
3	3	8	d
4	20	9	e

3. regex expression

df = pd.DataFrame({'A':['bat','foot','bait'],
                  'B':['abc','bar','foot']})

df.replace(to_replace=r'^ba.$',value='vvvv',regex=True)  # to define to_replace and value in the function

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	A	B
0	vvvv	abc
1	foot	vvvv
2	bait	foot

df.replace({'A': r'^ba.$'}, {'A': 'new'}, regex=True)  # in column A  to_replce=r'^ba.$' value='new'

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	A	B
0	new	abc
1	foot	bar
2	bait	foot

df.replace({'A':{r"^ba.$":"new"}},regex=True)  #  same as above

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	A	B
0	new	abc
1	foot	bar
2	bait	foot

df.replace(regex=r'^ba.$',value='vvv')  # in the whole dataframe

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	A	B
0	vvv	abc
1	foot	vvv
2	bait	foot

df.replace(regex={r'^ba.$':'vvv','foot':'xyz'})

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	A	B
0	vvv	abc
1	xyz	vvv
2	bait	xyz

df.replace(regex=[r'^ba.$','foo.$'],value='vvv')

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	A	B
0	vvv	abc
1	vvv	vvv
2	bait	vvv