Pandas Convert Column Values to String

Pandas Convert Column Values to String

  1. Convert the Data Type of Column Values of a DataFrame to String Using the apply() Method
  2. Convert the Data Type of All DataFrame Columns to string Using the applymap() Method
  3. Convert the Data Type of Column Values of a DataFrame to string Using the astype() Method

This tutorial explains how we can convert the data type of column values of a DataFrame to the string.

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})

print(employees_df)

Output:

     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

We will use the DataFrame displayed in the above example to explain how we can convert the data type of column values of a DataFrame to the string.

Convert the Data Type of Column Values of a DataFrame to String Using the apply() Method

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df["Age"]=employees_df["Age"].apply(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

Output:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name  Score Age
0   Ayush     31  33
1  Bikram     38  34
2   Ceela     33  38
3   Kusal     39  45
4  Shanty     35  37

Datatype of columns after conversion:
Name     object
Score     int64
Age      object
dtype: object

It changes the data type of the Age column from int64 to object type representing the string.

Convert the Data Type of All DataFrame Columns to string Using the applymap() Method

If we want to change the data type of all column values in the DataFrame to the string type, we can use the applymap() method.

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df=employees_df.applymap(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

Output:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37
zeppy@zeppy-G7-7588:~/test/Week-01/taddaa$ python3 1.py
DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name Score Age
0   Ayush    31  33
1  Bikram    38  34
2   Ceela    33  38
3   Kusal    39  45
4  Shanty    35  37

Datatype of columns after conversion:
Name     object
Score    object
Age      object
dtype: object

It converts the datatype of all DataFrame columns to the string type denoted by object in the output.

Convert the Data Type of Column Values of a DataFrame to string Using the astype() Method

import pandas as pd

employees_df = pd.DataFrame({
    'Name': ["Ayush","Bikram","Ceela","Kusal","Shanty"],
    'Score': [31, 38, 33, 39,35],
    'Age':  [33,34,38,45,37],

})
print("DataFrame before Conversion:")
print(employees_df,"\n")
print("Datatype of columns before conversion:")
print(employees_df.dtypes,"\n")

employees_df["Score"]=employees_df["Score"].astype(str)

print("DataFrame after conversion:")
print(employees_df,"\n")
print("Datatype of columns after conversion:")
print(employees_df.dtypes)

Output:

DataFrame before Conversion:
     Name  Score  Age
0   Ayush     31   33
1  Bikram     38   34
2   Ceela     33   38
3   Kusal     39   45
4  Shanty     35   37

Datatype of columns before conversion:
Name     object
Score     int64
Age       int64
dtype: object

DataFrame after conversion:
     Name Score  Age
0   Ayush    31   33
1  Bikram    38   34
2   Ceela    33   38
3   Kusal    39   45
4  Shanty    35   37

Datatype of columns after conversion:
Name     object
Score    object
Age       int64
dtype: object

It converts the data type of the Score column in the employees_df Dataframe to the string type.

Related Article - Pandas DataFrame Column

  • Get Pandas DataFrame Column Headers as a List
  • Delete Pandas DataFrame Column
  • Convert Pandas Column to Datetime
  • Get the Sum of Pandas Column
  • Change the Order of Pandas DataFrame Columns
  • Convert DataFrame Column to String in Pandas