I. Series Data Replacement()
(pat,repl,n=-1,case=None,flags=0,regex=None)
Functions in detail:
| pat | String to find |
| repl | The replaced string can be called with the function |
| n | Number of substitutions to be made, default all |
| case | case sensitive or not |
| flags | Flags in the re module |
| regex | Whether to set it as a regular expression |
1. General search and replace
Replace Cao Cao with Liu Bei
import pandas as pd
s=(['Cao Cao','Big Joe','Little Joe'])
('Cao Cao','Liu Bei')
2. Regular expression replacement
Replace '~' and '/' in strings with '-'
import pandas as pd
s=(['2022-5-5','2022/5/6','2022~6~9'])
('[~/]','-',regex=True)
3. Pre-compiled regular expression replacement
Replace '~' and '/' in strings with '-'
import pandas as pd
import re
s=(['2022-5-5','2022/5/6','2022~6~9'])
pat=('[~/]')
(pat,'-',regex=True)
4. Function replacement
Add parentheses to the names of people in the Series; add '-' between the name and the date.
import pandas as pd
import re
s=(['Aaron2022-5-5','Bob2022-5-6','judy2022-6-9'])
('[a-zA-Z]+',lambda x: '('+x[0]+')'+'-',regex=True)
5. Substitution in groups
In a different way, group substitution can accomplish the same thing as function substitution;
s=(['Aaron2022-5-5','Bob2022-5-6','judy2022-6-9'])
('([a-zA-Z]+)',r"【\1】-",regex=True)
II. DataFrame Data Replacement ()
(to_replace=None,value=None,inplace=False,limit=None,regex=False,method='pad)
Functions in detail:
| to_replace | Find the value to replace |
| value | Replacement of values matching the lookup |
| inplace | Modify original data |
| limit | Maximum size gap for forward or backward filling |
| regex | Whether regular expressions are supported |
| method | Replacement method |
1. Single-value substitution
Write the instance data:
df=({'Hero Attributes':['Assassin','Shooter','Mage','Warrior','Auxiliary'],
'Red's Heroes':['Jing Ke','Halogen Eggs','Zhen Ji','Xiahou Dun','Xiang Yu'],
'Red damage':[11.20,15.34,8.57,6.98,3.69],
'Number of deaths on the red side':['1 time','10 times','8 times','5 times','6 times'],
'Heroes of the blue side':['Zhao Yun','Mark','Dry General','Lü Bu','Liu Zen'],
'Blue damage':[10.82,11.36,10.87,9.69,6.53],
'Number of deaths on the blue side':['5 times','8 times','4 times','7 times','10 times']})
df
Replace Jing Ke with Sun Wukong;
('Jing Ke','Monkey King')
2. List replacement
Multiple values replace a single value, replacing Zhao Yun and Mark with Sun Ce
(['Zhao Yun','Mark'],'Sun Ce')
Multiple values replace multiple values, replacing Zhao Yun and Mark with Orange Righteousness and Yuji;
The lookup values are placed in a list, and the replacement values are placed in a list that needs to correspond one to the other;
(['Zhao Yun','Mark'],['Orange Righteousness','Yu Ji'])
3. Dictionary substitution
(1) Dictionary substitution, replacing Zhao Yun and Mark with Orange Right and Yu Ji
The key passed into the dictionary is the value to look up and the value is the value to replace;
({
'Zhao Yun':'Orange Righteousness',
'Mark':'Yu Ji'
})(2) Specify column replacement
Replace the red hero Zhen Ji with Sable Cicada and Xiang Yu with Zhong Kui; the blue hero Lu Bu with Sun Ce and Gan with Yang Yuhuan;
({
'Red's Heroes':{
'Zhen Ji':'Sable Cicada',
'Xiang Yu':'Zhong Kui'
},
'Heroes of the blue side':{
'Lü Bu':'Sun Ce',
'Dry General':'Yang Yuhuan'
}
})
(3) Multi-column replacement
Replace Red's damage of 11.2 and Blue's damage of 11.36, 9.69 with 9.999
({'Red damage':11.2,'Blue damage':[11.36,9.69]},9.999)
III. DataFrame Regular Replacement
1. Introduction to 'zero-width assertions' for regular expressions
| name (of a thing) | displayed formula | account for |
| Zero-width positive prior assertion | (?=exp) | Matches a string followed by an exp expression. |
| Zero-width negative prior assertion | (?!exp) | Matches a string that is not followed by an exp expression. |
| Zero-width positive backward assertion | (?<=exp) | Matches a string preceded by an exp expression. |
| Zero-width negative backward-looking assertion | (?<!exp) | Matches a string that is not preceded by an exp expression. |
2. Single-value regular substitution
Add '-' between the number of deaths on the red side and the number of deaths on the blue side and 'times'
(
to_replace='(^\d+)(?=\D)',
value=r'\1-',
regex=True
)3. List regular substitution
List Replacement, add '[ ]' to Red's heroes
df['Red's Heroes']=df['Red's Heroes'].replace(
regex=['^','$'],
value=['【','】'],
)
df
4. Dictionary regular substitution
Dictionary Regular Replacement, add '[]' to blue heroes
(
regex={
'Heroes of the blue side':{'^':'[','$':']'}
})
IV. DataFrame Replacement Example Applications
The original data is as follows: the number following the hero information column is the number;
df=({'Hero Attributes':['Assassin','Shooter','Mage','Warrior','Auxiliary'],
'Hero Information':['Thornhorn 36','Halogen Eggs 1','Zhen Ji 6','Xiahou Dun 10','Xiang Yu 66'],
'Red's Heroes':['Jing Ke','Halogen Eggs','Zhen Ji','Xiahou Dun','Xiang Yu'],
'Red damage':[11.20,15.34,8.57,6.98,3.69],
'Number of deaths on the red side':['1 time','10 times','8 times','5 times','6 times'],
'Heroes of the blue side':['Zhao Yun','Mark','Dry General','Lü Bu','Liu Zen'],
'Blue damage':[10.82,11.36,10.87,9.69,6.53],
'Number of deaths on the blue side':['5 times','8 times','4 times','7 times','10 times']})
df
1. Harmonize the numbering to four digits, using zeros to make up any shortfall of four digits;
df['Hero Information'].(
pat='(\d+)',
repl=lambda x: '0'*(4-len(x[0]))+x[0],
regex=True
)
2. Precede the number with Timi and use '-' as a delimiter.
df['Hero Information']=df['Hero Information'].(
pat='(\d+)',
repl=lambda x: '-Timi'+'0'*(4-len(x[0]))+x[0],
regex=True
)
df
summarize
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