Python type() Function

Filed Under: Python
Python Type Function

Python type() Function

Python has a lot of buit-in function. The type() function is used to get the type of an object.

Python type() function syntax is:


type(object)

type(name, bases, dict)
  • When a single argument is passed to the type() function, it returns the type of the object. Its value is the same as the object.__class__ instance variable.
  • When three arguments are passed, it returns a new type object. It’s used to create a class dynamically on the fly.
    1. The “name” string becomes the class name. Its same as the __name__ attribute of a class.
    2. The “bases” tuple specifies the base classes. Its same as the __bases__ attribute of the class.
    3. The “dict” dictionary is used to create the class body. Its same as the __dict__ attribute of the class.

Python type() Examples

Let’s look into some examples of using the type() function.

1. Checking the type of an object


x = 10
print(type(x))

s = 'abc'
print(type(s))

from collections import OrderedDict

od = OrderedDict()
print(type(od))


class Data:
    pass


d = Data()
print(type(d))

Output:


<class 'int'>
<class 'str'>
<class 'collections.OrderedDict'>
<class '__main__.Data'>

Notice that the type() function returns the type of the object with the module name. Since our Python script doesn’t have a module, it’s module becomes __main__.

2. Creating Dynamic Classes

Let’s say we have following classes.


class Data:
    """Data Class"""
    d_id = 10


class SubData(Data):
    """SubData Class"""
    sd_id = 20

Let’s print some of the properties of these classes.


print(Data.__class__)
print(Data.__bases__)
print(Data.__dict__)
print(Data.__doc__)

print(SubData.__class__)
print(SubData.__bases__)
print(SubData.__dict__)
print(SubData.__doc__)

Output:


<class 'type'>
(<class 'object'>,)
{'__module__': '__main__', '__doc__': 'Data Class', 'd_id': 10, '__dict__': <attribute '__dict__' of 'Data' objects>, '__weakref__': <attribute '__weakref__' of 'Data' objects>}
Data Class

<class 'type'>
(<class '__main__.Data'>,)
{'__module__': '__main__', '__doc__': 'SubData Class', 'sd_id': 20}
SubData Class

We can create similar classes using the type() function.


Data1 = type('Data1', (object,), {'__doc__': 'Data1 Class', 'd_id': 10})
SubData1 = type('SubData1', (Data1,), {'__doc__': 'SubData1 Class', 'sd_id': 20})

print(Data1.__class__)
print(Data1.__bases__)
print(Data1.__dict__)
print(Data1.__doc__)

print(SubData1.__class__)
print(SubData1.__bases__)
print(SubData1.__dict__)
print(SubData1.__doc__)

Output:


<class 'type'>
(<class 'object'>,)
{'__doc__': 'Data1 Class', 'd_id': 10, '__module__': '__main__', '__dict__': <attribute '__dict__' of 'Data1' objects>, '__weakref__': <attribute '__weakref__' of 'Data1' objects>}
Data1 Class

<class 'type'>
(<class '__main__.Data1'>,)
{'__doc__': 'SubData1 Class', 'sd_id': 20, '__module__': '__main__'}
SubData1 Class

Note that we can’t create functions in the dynamic class using the type() function.

Real-Life Usage of type() function

Python is a dynamically-typed language. So, if we want to know the type of the arguments, we can use the type() function. If you want to make sure that your function works only on the specific types of objects, use isinstance() function.

Let’s say we want to create a function to calculate something on two integers. We can implement it in the following way.


def calculate(x, y, op='sum'):
    if not(isinstance(x, int) and isinstance(y, int)):
        print(f'Invalid Types of Arguments - x:{type(x)}, y:{type(y)}')
        raise TypeError('Incompatible types of arguments, must be integers')
    
    if op == 'difference':
        return x - y
    if op == 'multiply':
        return x * y
    # default is sum
    return x + y

The isinstance() function is used to validate the input argument type. The type() function is used to print the type of the parameters when validation fails.

References

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