You can use the following syntax to convert a column in a pandas DataFrame from a float to an integer:

df['float_column'] = df['float_column'].astype(int)

The following examples show how to use this syntax in practice.

Example 1: Convert One Column from Float to Integer

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'],
                   'points': [25.2, 27.0, 14.5, 17.6, 20.7],
                   'assists': [5.1, 7.7, 10.3, 8.6, 9.5]})

#view data types for each column
df.dtypes

player      object
points     float64
assists    float64
dtype: object

We can see that the points and assists columns both have a data type of float.

The following code shows how to convert the points column from a float to an integer:

#convert 'points' column to integer
df['points'] = df['points'].astype(int)

#view data types of each column
df.dtypes

player      object
points       int32
assists    float64
dtype: object

We can see that the points column is now an integer, while all other columns remained unchanged.

Example 2: Convert Multiple Columns to Integer

The following code shows how to convert multiple columns in a DataFrame from a float to an integer:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'],
                   'points': [25.2, 27.0, 14.5, 17.6, 20.7],
                   'assists': [5.1, 7.7, 10.3, 8.6, 9.5]})

#convert 'points' and 'assists' columns to integer
df[['points', 'assists']] = df[['points', 'assists']].astype(int)

#view data types for each column
df.dtypes

player     object
points      int32
assists     int32
dtype: object

We can see that the points and assists columns have both been converted from floats to integers.

Additional Resources

The following tutorials explain how to perform other common conversions in Python: