
        
        
            
                
                
                    import pandas as pd
s = pd.Series(["aAA", "Ab"])
s.str.replace("A", "")
                
            
            0    a
1    b
dtype: object

By default, regex=True, which means that the pattern (first argument) is treated as a regular expression:


        
        
            
                
                
                    s = pd.Series(["aAb", "Ab"])
s.str.replace("[aA]", "")   # regex=True
                
            
            0    b
1    b
dtype: object

You can also toggle case-sensitivity for the matches using the case parameter:


        
        
            
                
                
                    s = pd.Series(["aAb", "Ab"])
s.str.replace("a", "", case=False)
                
            
            0    b
1    b
dtype: object

Removing substrings from strings in Pandas DataFrame

Consider the following DataFrame:


        
        
            
                
                
                    import pandas as pd
df = pd.DataFrame({"col1":["aAA", "Ab"],"col2":["aB","AAB"]})
df
                
            
               col1  col2
0  aAA   aB
1  Ab    AAB

To remove the substring AA from column col1:


        
        
            
                
                
                    df["col1"].str.replace("AA", "")   # returns a Series
                
            
            0    a
1    b
Name: col1, dtype: object

If you want to replace col1 with this Series, then perform the following:


        
        
            
                
                
                    df["col1"] = df["col1"].str.replace("A", "")
df
                
            
               col1  col2
0   a     aB
1   b     AA

Note that str.replace is a function defined only for Pandas Series (columns) and not for DataFrames, so make sure to extract the individual columns as a Series first.

Removing substrings from strings for multiple Pandas DataFrame columns

To removing substrings from strings for multiple columns:


        
        
            
                
                
                    for column in ["col1","col2"]:
    df[column] = df[column].str.replace("AA", "")

df
                
            
               col1  col2
0   a     aB
1   b     B