Convert Floats to Integers in a Pandas DataFrame

Last Updated : 01 Dec, 2023

Let us see how to convert float to integer in a Pandas DataFrame. We will be using the astype() method to do this. It can also be done using the apply() method.

Convert Floats to Integers in a Pandas DataFrame

Below are the ways by which we can convert floats to integers in a Pandas DataFrame:

Using DataFrame.astype() method
Using DataFrame.apply() method

Convert Floats to Integers in Pandas DataFrame Using `DataFrame.astype()`

First of all, we will create a DataFrame

Python3

# importing the library
import pandas as pd

# creating a DataFrame
list = [['Anton Yelchin', 36, 75.2, 54280.20],
        ['Yul Brynner', 38, 74.32, 34280.30],
        ['Lev Gorn', 31, 70.56, 84280.50],
        ['Alexander Godunov', 34, 80.30, 44280.80],
        ['Oleg Taktarov', 40, 100.03, 45280.30],
        ['Dmitriy Pevtsov', 33, 72.99, 70280.25],
        ['Alexander Petrov', 42, 85.84, 25280.75]]
df = pd.DataFrame(list, columns=['Name', 'Age', 'Weight', 'Salary'])
display(df)

Output

Example 1: Converting One Column from Float to Int Using DataFrame.astype()

In this example, the code displays the data types of DataFrame 'df.' It then converts the 'Weight' column from float to int and displays the updated data types.

python3

# displaying the datatypes
display(df.dtypes)

# converting 'Weight' from float to int
df['Weight'] = df['Weight'].astype(int)

# displaying the datatypes
display(df.dtypes)

Output :

Example 2: Converting More Than One Column From Float to Int Using DataFrame.astype()

In this example, the code first displays the data types of DataFrame 'df.' It then converts the 'Weight' and 'Salary' columns from float to int and displays the updated data types.

python3

# displaying the datatypes
display(df.dtypes)

# converting 'Weight' and 'Salary' from float to int
df = df.astype({"Weight": 'int', "Salary": 'int'})

# displaying the datatypes
display(df.dtypes)

Output

Pandas Convert Column to Int in DataFrame using `DataFrame.apply()` method

First of all, we will create a DataFrame.

Python3

# importing the module
import pandas as pd

# creating a DataFrame
list = [[15, 2.5, 100.22], [20, 4.5, 50.21],
        [25, 5.2, 80.55], [45, 5.8, 48.86],
        [40, 6.3, 70.99], [41, 6.4, 90.25],
        [51, 2.3, 111.90]]
df = pd.DataFrame(list, columns=['Field_1', 'Field_2', 'Field_3'],
                  index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])
display(df)

Output

Example 1: Converting a Single Column From Float to Int Using DataFrame.apply(np.int64)

In this example, the code begins by importing the NumPy module as 'np.' It then displays the data types of DataFrame 'df.' After that, it converts the 'Field_2' column from float to int using NumPy's int64 data type and displays the updated data types.

python3

# importing the module
import numpy as np

# displaying the datatypes
display(df.dtypes)

# converting 'Field_2' from float to int
df['Field_2'] = df['Field_2'].apply(np.int64)

# displaying the datatypes
display(df.dtypes)

Output

Example 2: Converting Multiple Columns From Float to int Using DataFrame.apply(np.int64)

In this example, the code initially displays the data types of DataFrame 'df.' Subsequently, it converts both the 'Field_2' and 'Field_3' columns from float to int using NumPy's int64 data type and then displays the updated data types.

Python3

# displaying the datatypes
display(df.dtypes)

# converting 'Field_2' and 'Field_3' from float to int
df['Field_2'] = df['Field_2'].apply(np.int64)
df['Field_3'] = df['Field_3'].apply(np.int64)

# displaying the datatypes
display(df.dtypes)