replace() to Replace all or some occurrence of matching string by using regular expressions

Pandas

Number of occurrences of pattern in a string.
Returns Series or Index

replacing string

All @ are replaced by #

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('@','#'))

Output

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

Case insensitive search and replace

By using option case=False we can make 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))

Output ( Ravi is replaced by Ronn )

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]','*'))

Output ( Char starting with A or C are replaced with * , so A at Alex is replaced )

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

Let us replace only digits

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]','*'))

Output

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

Let us replace a or b chars

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]','*'))

Output is here

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

Number of replacements

In above code we have replaced all the occurrences. Now let us limit to one only replacement.

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))

Output

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

Pandas contains() Converting char case slice() split()

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str.replace()

replacing string

Case insensitive search and replace

Using Regular expression

Number of replacements

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