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To add a string to each value of a pandas column, use the pandas.Series.str.cat() method along with the desired string. This method will concatenate the values of the column and the given string and return the resulting series. The code would look something like this: df[‘column_name’] = df[‘column_name’].str.cat(string)
This method can be used to add a prefix or a suffix to each value in the column. It can also be used to add a space between each value and the string. Additionally, the str.cat() method can be used to add multiple strings to each value in the column at once.
You can use the following methods to add a string to each value in a column of a pandas DataFrame:
Method 1: Add String to Each Value in Column
df['my_column'] = 'some_string' + df['my_column'].astype(str)
Method 2: Add String to Each Value in Column Based on Condition
#define condition mask = (df['my_column'] == 'A') #add string to values in column equal to 'A' df.loc[mask, 'my_column'] = 'some_string' + df['my_column'].astype(str)
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': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'points': [18, 22, 19, 14, 14, 11, 20, 28], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame print(df) team points assists rebounds 0 A 18 5 11 1 A 22 7 8 2 A 19 7 10 3 A 14 9 6 4 B 14 12 6 5 B 11 9 5 6 B 20 9 9 7 B 28 4 12
Example 1: Add String to Each Value in Column
The following code shows how to add the string ‘team_’ to each value in the team column:
#add string 'team_' to each value in team column df['team'] = 'team_' + df['team'].astype(str) #view updated DataFrame print(df) team points assists rebounds 0 team_A 18 5 11 1 team_B 22 7 8 2 team_C 19 7 10 3 team_D 14 9 6 4 team_E 14 12 6 5 team_F 11 9 5 6 team_G 20 9 9 7 team_H 28 4 12
Notice that the prefix ‘team_’ has been added to each value in the team column.
You can also use the following syntax to instead add ‘_team’ as a suffix to each value in the team column:
#add suffix 'team_' to each value in team column df['team'] = df['team'].astype(str) + '_team' #view updated DataFrame print(df) team points assists rebounds 0 A_team 18 5 11 1 A_team 22 7 8 2 A_team 19 7 10 3 A_team 14 9 6 4 B_team 14 12 6 5 B_team 11 9 5 6 B_team 20 9 9 7 B_team 28 4 12
Example 2: Add String to Each Value in Column Based on Condition
The following code shows how to add the prefix ‘team_’ to each value in the team column where the value is equal to ‘A’:
#define condition
mask = (df['team'] == 'A')
#add string 'team_' to values that meet the condition
df.loc[mask, 'team'] = 'team_' + df['team'].astype(str)
#view updated DataFrame
print(df)
team points assists rebounds
0 team_A 18 5 11
1 team_A 22 7 8
2 team_A 19 7 10
3 team_A 14 9 6
4 B 14 12 6
5 B 11 9 5
6 B 20 9 9
7 B 28 4 12 Notice that the prefix ‘team_’ has only been added to the values in the team column whose value was equal to ‘A’.