__lshift__() method implements the built-in
<< operation. So, when you cal
x << y, Python attempts to call
x.__lshift__(y). If the method is not implemented, Python first attempts to call
__rlshift__ on the right operand and if this isn’t implemented either, it raises a
We call this a “Dunder Method” for “Double Underscore Method” (also called “magic method”). To get a list of all dunder methods with explanation, check out our dunder cheat sheet article on this blog.
The Python bitwise left-shift operator
x << n shifts the binary representation of integer
n positions to the left.
For a positive integer, it inserts a
0 bit on the right and shifts all remaining bits by one position to the left. For example, if you left-shift the binary representation
0101 by one position, you’d obtain
01010. Semantically, the bitwise left-shift operator
x << n is the same as multiplying the integer
print(8 << 1) # 16 print(8 << 2) # 32 print(-3 << 1) # -6
To understand this operation in detail, feel free to read over our tutorial or watch the following video:
Example Custom __lshift__()
In the following example, you create a custom class
Data and overwrite the
__lshift__() method so that it returns a dummy string when trying to calculate the bitwise left-shift operation.
class Data: def __lshift__(self, other): return '... my result of lshift...' a = Data() b = Data() print(a << b) # ... my result of lshift...
If you hadn’t defined the
__lshift__() method, Python would’ve raised a
TypeError: unsupported operand type(s) for <<
Consider the following code snippet where you try to calculate the left-shift operation on custom objects without defining the dunder method
class Data: pass a = Data() b = Data() print(a << b)
Running this leads to the following error message on my computer:
Traceback (most recent call last): File "C:\Users\xcent\Desktop\code.py", line 8, in <module> print(a << b) TypeError: unsupported operand type(s) for <<: 'Data' and 'Data'
The reason for this error is that the
__lshift__() method has never been defined—and it is not defined for a custom object by default. So, to resolve the
TypeError: unsupported operand type(s) for <<, you need to provide the
__lshift__(self, other) method in your class definition as shown previously:
class Data: def __lshift__(self, other): return '... my result of lshift...'
Of course, you’d use another return value in practice as explained in the “Background lshift()” section.
Python __lshift__ vs __rlshift__
Say, you want to calculate the left-shift operation on two custom objects
print(x << y)
Python first tries to call the left object’s
x.__lshift__(y). But this may fail for two reasons:
- The method
x.__lshift__()is not implemented in the first place, or
- The method
x.__lshift__()is implemented but returns a
NotImplementedvalue indicating that the data types are incompatible.
If this fails, Python tries to fix it by calling the
y.__rlshift__() for reverse left-shift on the right operand
y. Not that this is not the same as right-shift, it just means that the left-shift operation is called on the second operand
If the reverse left-shift method is implemented, Python knows that it doesn’t run into a potential problem of a non-commutative operation. If it would just execute
y.__lshift__(x) instead of
x.__lshift__(y), the result would be wrong because the operation is non-commutative. That’s why
y.__rlshift__(x) is needed.
So, the difference between
x.__rlshift__(y) is that the former calculates
x << y whereas the latter calculates
y << x — both calling the respective method defined on the object
You can see this in effect here where we attempt to call the operation on the left operand
x—but as it’s not implemented, Python simply calls the reverse operation on the right operand
class Data_1: pass class Data_2: def __rlshift__(self, other): return 'called reverse lshift' x = Data_1() y = Data_2() print(x << y) # called reverse lshift
Where to Go From Here?
Enough theory. Let’s get some practice!
Coders get paid six figures and more because they can solve problems more effectively using machine intelligence and automation.
To become more successful in coding, solve more real problems for real people. That’s how you polish the skills you really need in practice. After all, what’s the use of learning theory that nobody ever needs?
You build high-value coding skills by working on practical coding projects!
Do you want to stop learning with toy projects and focus on practical code projects that earn you money and solve real problems for people?
🚀 If your answer is YES!, consider becoming a Python freelance developer! It’s the best way of approaching the task of improving your Python skills—even if you are a complete beginner.
If you just want to learn about the freelancing opportunity, feel free to watch my free webinar “How to Build Your High-Income Skill Python” and learn how I grew my coding business online and how you can, too—from the comfort of your own home.
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com. He’s author of the popular programming book Python One-Liners (NoStarch 2020), coauthor of the Coffee Break Python series of self-published books, computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.