You can use the following basic syntax to convert a Boolean column to a string column in a pandas DataFrame:
df['my_bool_column'] = df['my_bool_column'].replace({True: 'True', False: 'False'})
This particular example replaces each True value with the string ‘True’ and each False value with the string ‘False’ in the column called my_bool_column.
The following example shows how to use this syntax in practice.
Example: Convert Boolean to String in Pandas
Suppose we have the following pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'B', 'C', 'D', 'E', 'F', 'G'], 'points': [18,20, 25, 40, 34, 32, 19], 'all_star': [True, False, True, True, True, False, False], 'starter': [False, True, True, True, False, False, False]}) #view DataFrame print(df) team points all_star starter 0 A 18 True False 1 B 20 False True 2 C 25 True True 3 D 40 True True 4 E 34 True False 5 F 32 False False 6 G 19 False False
We can use the dtypes function to check the data type of each column in the DataFrame:
#view data type of each column print(df.dtypes) team object points int64 all_star bool starter bool dtype: object
From the output we can see that the all_star and starter columns are both Booleans.
We can use the following syntax to convert the all_star column to a string column:
#convert Boolean values in all_star column to strings
df['all_star'] = df['all_star'].replace({True: 'True', False: 'False'})
#view updated DataFrame
print(df)
team points all_star starter
0 A 18 True False
1 B 20 False True
2 C 25 True True
3 D 40 True True
4 E 34 True False
5 F 32 False False
6 G 19 False False
#view updated data types of each column
print(df.dtypes)
team object
points int64
all_star object
starter bool
dtype: objectFrom the output we can see that the all_star column has been converted to a string column.
To convert the all_star and starter columns both from Boolean to strings, we can use the following syntax:
#convert Boolean values in all_star and starter columns to strings
df[['all_star', 'starter']] = df[['all_star', 'starter']].replace({True: 'True', False: 'False'})
#view updated DataFrame
print(df)
team points all_star starter
0 A 18 True False
1 B 20 False True
2 C 25 True True
3 D 40 True True
4 E 34 True False
5 F 32 False False
6 G 19 False False
#view updated data types of each column
print(df.dtypes)
team object
points int64
all_star object
starter object
dtype: object
From the output we can see that both Boolean columns have been converted to strings.
Note: You can find the complete documentation for the pandas replace() function .
Additional Resources
The following tutorials explain how to perform other common tasks in pandas: