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Python Pandas DataFrame.astype() function changes the data type of an object to the specified data type.

pandas.DataFrame.astype()grammar

DataFrame.astype(dtype, copy=True, errors="raise")

parameter

Return Object

It returns a DataFrame with the data type.

Example code: DataFrame.astype()Method to change a column data type

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                        'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                        'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})

print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)

dataframe1 = dataframe.astype({'Attendance': 'int32'}).dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)

Output:

The Original Data Types of the Data frame are: 

Attendance         int64
Name              object
Obtained Marks     int64
dtype: object
The Modified Data Types of the Data frame are: 

Attendance         int32
Name              object
Obtained Marks     int64
dtype: object

The function returns the converted data type. We use dtypes()the function to display the data type of each column in the DataFrame.

Example Code: DataFrame.astype()Method to change the data type of all columns in a DataFrame

We will try to change the data type of the given DataFrame.

import pandas as pd
dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                        'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                        'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})
print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)

dataframe1 = dataframe.astype('object').dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)

Output:

The Original Data Types of the Data frame are: 

Attendance         int64
Name              object
Obtained Marks     int64
dtype: object
The Modified Data Types of the Data frame are: 

Attendance        object
Name              object
Obtained Marks    object
dtype: object

The function returns the modified DataFrame, which has changed the data types of all columns to object.

Sample code: DataFrame.astype()Exceptions when a method changes data type

Now we objectset the data type to int32. The function will ignore the exception because we will pass the parameter errors= 'ignore'.

import pandas as pd

dataframe=pd.DataFrame({'Attendance': {0: 60, 1: 100, 2: 80,3: 78,4: 95},
                        'Name': {0: 'Olivia', 1: 'John', 2: 'Laura',3: 'Ben',4: 'Kevin'},
                        'Obtained Marks': {0: 90, 1: 75, 2: 82, 3: 64, 4: 45}})

print("The Original Data Types of the Data frame are: \n")
print(dataframe.dtypes)

dataframe1 = dataframe.astype('int32', errors='ignore').dtypes
print("The Modified Data Types of the Data frame are: \n")
print(dataframe1)

Output:

The Original Data Types of the Data frame are: 

Attendance         int64
Name              object
Obtained Marks     int64
dtype: object
The Modified Data Types of the Data frame are: 

Attendance         int32
Name              object
Obtained Marks     int32
dtype: object

Notice that the function did not raise any exceptions. It ignored the error because we objectconverted to int32. It just did not change Namethe data type of the column.