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.
Method 1: Using DataFrame.astype() method
First of all we will create a DataFrame:
# 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()
# 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()
# 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 :
Method 2: Using DataFrame.apply() method
First of all we will create a DataFrame.
# 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)
# 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)
# 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) |
Output :
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