You can use the following basic syntax to remove special characters from a column in a pandas DataFrame:

df['my_column'] = df['my_column'].str.replace('W', '', regex=True)

This particular example will remove all characters in my_column that are not letters or numbers.

The following example shows how to use this syntax in practice.

Example: Remove Special Characters from Column in Pandas

Suppose we have the following pandas DataFrame that contains information about various basketball players:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team' : ['Mavs$', 'Nets', 'Kings!!', 'Spurs%', '&Heat&'],
                   'points' : [12, 15, 22, 29, 24]})

#view DataFrame
print(df)

      team  points
0    Mavs$      12
1     Nets      15
2  Kings!!      22
3   Spurs%      29
4   &Heat&      24

Suppose we would like to remove all special characters from values in the team column.

We can use the following syntax to do so:

#remove special characters from team column
df['team'] = df['team'].str.replace('W', '', regex=True)

#view updated DataFrame
print(df)

    team  points
0   Mavs      12
1   Nets      15
2  Kings      22
3  Spurs      29
4   Heat      24

Notice that all special characters have been removed from values in the team column.

Note: The regex W is used to find all non-word characters, i.e. characters which are not alphabetical or numerical.

In this example, we replaced each non-word character with an empty value which is equivalent to removing the non-word characters.

The following tutorials explain how to perform other common tasks in pandas:

How to Replace NaN Values with Zeros in Pandas