str.replace()

1. Replacing matching string in all columns
2. Replacing matching number in all columns
3. Replacing (to_replace) matching List in all columns
4. Using dictionary as to_replace to match in all columns
5. Using regular expression pattern matching
6. Replacing matching in a Series

import pandas as pd 
my_dict={
  'id':[1,2,3,4,5,4,2],
  'name':['John','Max','Arnold','Krish','John','Krish','Max'],
  'class1':['Four','Three','Three','Four','Four','Four','Three'],
  'mark':[75,85,55,60,60,60,85],
  'gender':['female','male','male','female','female','female','male']
	}
df = pd.DataFrame(data=my_dict)

String to replace in all columns

df=df.replace('Max','Jim') # replace in all columns

   id    name class1  mark  gender
0   1    John   Four    75  female
1   2     Jim  Three    85    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5    John   Four    60  female
5   4   Krish   Four    60  female
6   2     Jim  Three    85    male

Number to replace in all columns

df=df.replace(85,100) # replace in all columns

   id    name class1  mark  gender
0   1    John   Four    75  female
1   2     Max  Three   100    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5    John   Four    60  female
5   4   Krish   Four    60  female
6   2     Max  Three   100    male

Using List to replace matching string and number

df=df.replace(['John',85],['Jim',100])

   id    name class1  mark  gender
0   1     Jim   Four    75  female
1   2     Max  Three   100    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5     Jim   Four    60  female
5   4   Krish   Four    60  female
6   2     Max  Three   100    male

Using dictionary

df=df.replace({'John':'Jim',85:100}) # using dictionary

   id    name class1  mark  gender
0   1     Jim   Four    75  female
1   2     Max  Three   100    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5     Jim   Four    60  female
5   4   Krish   Four    60  female
6   2     Max  Three   100    male

Replace a list of matching number with one value

df=df.replace([75,85,60],100) # Matching list with one 

   id    name class1  mark  gender
0   1    John   Four   100  female
1   2     Max  Three   100    male
2   3  Arnold  Three    55    male
3   4   Krish   Four   100  female
4   5    John   Four   100  female
5   4   Krish   Four   100  female
6   2     Max  Three   100    male

Replace a list of matching string with one value

df=df.replace({'name':'John','class1':'Four'},'Jim')

   id    name class1  mark  gender
0   1     Jim    Jim    75  female
1   2     Max  Three    85    male
2   3  Arnold  Three    55    male
3   4   Krish    Jim    60  female
4   5     Jim    Jim    60  female
5   4   Krish    Jim    60  female
6   2     Max  Three    85    male

Based on specific column we can replace string.

df['class1']=df['class1'].str.replace('Three','Ten')

   id    name class1  mark  gender
0   1    John   Four    75  female
1   2     Max    Ten    85    male
2   3  Arnold    Ten    55    male
3   4   Krish   Four    60  female
4   5    John   Four    60  female
5   4   Krish   Four    60  female
6   2     Max    Ten    85    male

Using regular expression

df=df.replace(regex='^[AF]',value='*') 

   id    name class1  mark  gender
0   1    John   *our    75  female
1   2     Max  Three    85    male
2   3  *rnold  Three    55    male
3   4   Krish   *our    60  female
4   5    John   *our    60  female
5   4   Krish   *our    60  female
6   2     Max  Three    85    male

df=df.replace(regex={r'^M..$':'foo'}) 

   id    name class1  mark  gender
0   1    John   Four    75  female
1   2     foo  Three    85    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5    John   Four    60  female
5   4   Krish   Four    60  female
6   2     foo  Three    85    male

df=df.replace(regex={r'hn$':'foo'}) 

   id    name class1  mark  gender
0   1   Jofoo   Four    75  female
1   2     Max  Three    85    male
2   3  Arnold  Three    55    male
3   4   Krish   Four    60  female
4   5   Jofoo   Four    60  female
5   4   Krish   Four    60  female
6   2     Max  Three    85    male

Series

replacing string

import pandas as pd 
my_dict={'email':['Ravi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('@','#'))

0    Ravi#example.com
1    Raju#example.com
2    Alex#example.com

Case insensitive search and replace

import pandas as pd 
my_dict={'email':['Ravi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('ravi','Ronn',case=False))

0    Ronn@example.com
1    Raju@example.com
2    Alex@example.com

Using Regular expression

import pandas as pd 
my_dict={'email':['Ra2vi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('^[AC]','*'))

0    Ra2vi@example.com
1     Raju@example.com
2     *lex@example.com

import pandas as pd 
my_dict={'email':['Ra2vi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('[0-9]','*'))

0    Ra*vi@example.com
1     Raju@example.com
2     Alex@example.com

import pandas as pd 
my_dict={'email':['Ra2vi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('[a|b]','*'))

0    R*2vi@ex*mple.com
1     R*ju@ex*mple.com
2     Alex@ex*mple.com

Number of replacements

import pandas as pd 
my_dict={'email':['Ra2vi@example.com','Raju@example.com','Alex@example.com']}
df = pd.DataFrame(data=my_dict)
print(df.email.str.replace('[a|b]','*',n=1))

0    R*2vi@example.com
1     R*ju@example.com
2     Alex@ex*mple.com