You can use the following methods to remove specific characters from strings in a column in a pandas DataFrame:
Method 1: Remove Specific Characters from Strings
df['my_column'] = df['my_column'].str.replace('this_string', '')
Method 2: Remove All Letters from Strings
df['my_column'] = df['my_column'].str.replace('D', '', regex=True)
Method 3: Remove All Numbers from Strings
df['my_column'] = df['my_column'].str.replace('d+', '', regex=True)
The following examples show how to use each method in practice with the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team' : ['Mavs2', 'Nets44', 'Kings33', 'Cavs90', 'Heat576'], 'points' : [12, 15, 22, 29, 24]}) #view DataFrame print(df) team points 0 Mavs2 12 1 Nets44 15 2 Kings33 22 3 Cavs90 29 4 Heat576 24
Example 1: Remove Specific Characters from Strings
We can use the following syntax to remove ‘avs’ from each string in the team column:
#remove 'avs' from strings in team column df['team'] = df['team'].str.replace('avs', '') #view updated DataFrame print(df) team points 0 M2 12 1 Nets44 15 2 Kings33 22 3 C90 29 4 Heat576 24
Notice that ‘avs’ was removed from the rows that contained ‘Mavs’ and ‘Cavs’ in the team column.
Example 2: Remove All Letters from Strings
We can use the following syntax to remove all letters from each string in the team column:
#remove letters from strings in team column df['team'] = df['team'].str.replace('D', '', regex=True) #view updated DataFrame print(df) team points 0 2 12 1 44 15 2 33 22 3 90 29 4 576 24
Notice that all letters have been removed from each string in the team column.
Only the numerical values remain.
Example 3: Remove All Numbers from Strings
We can use the following syntax to remove all numbers from each string in the team column:
#remove numbers from strings in team column df['team'] = df['team'].str.replace('d+', '', regex=True) #view updated DataFrame print(df) team points 0 Mavs 12 1 Nets 15 2 Kings 22 3 Cavs 29 4 Heat 24
Notice that all numbers have been removed from each string in the team column.
Only the letters remain.
Additional Resources
The following tutorials explain how to perform other common tasks in pandas:
How to Replace NaN Values with Zeros in Pandas
How to Replace Empty Strings with NaN in Pandas
How to Replace Values in Column Based on Condition in Pandas