8. Operators
By Bernd Klein. Last modified: 29 Jun 2022.
Introduction
This chapter covers the various built-in operators, which Python has to offer.
Live Python training
Operators
These operations (operators) can be applied to all numeric types:
Operator | Description | Example |
---|---|---|
+, - | Addition, Subtraction | 10 -3 |
*, % | Multiplication, Modulo | 27 % 7 Result: 6 |
/ | Division This operation brings about different results for Python 2.x (like floor division) and Python 3.x |
Python3:
10 / 3 3.3333333333333335and in Python 2.x: 10 / 3 3 |
// | Truncation Division (also known as floordivision or floor division) The result of this division is the integral part of the result, i.e. the fractional part is truncated, if there is any. It works both for integers and floating-point numbers, but there is a difference between the type of the results: If both the dividend and the divisor are integers, the result will also be an integer. If either the divident or the divisor is a float, the result will be the truncated result as a float. |
10 // 3 3If at least one of the operands is a float value, we get a truncated float value as the result. 10.0 // 3 3.0 >>>A note about efficiency: The results of int(10 / 3) and 10 // 3 are equal. But the "//" division is more than two times as fast! You can see this here: In [9]: %%timeit for x in range(1, 100): y = int(100 / x) : 100000 loops, best of 3: 11.1 μs per loop In [10]: %%timeit for x in range(1, 100): y = 100 // x : 100000 loops, best of 3: 4.48 μs per loop |
+x, -x | Unary minus and Unary plus (Algebraic signs) | -3 |
~x | Bitwise negation | ~3 - 4 Result: -8 |
** | Exponentiation | 10 ** 3 Result: 1000 |
or, and, not | Boolean Or, Boolean And, Boolean Not | (a or b) and c |
in | "Element of" | 1 in [3, 2, 1] |
<, ≤, >, ≥, !=, == | The usual comparison operators | 2 ≤ 3 |
|, &, ^ | Bitwise Or, Bitwise And, Bitwise XOR | 6 ^ 3 |
<, > | Shift Operators | 6 < 3 |
Live Python training
Upcoming online Courses
24 Feb 2025 to 28 Feb 2025
31 Mar 2025 to 04 Apr 2025
07 Apr 2025 to 11 Apr 2025
19 May 2025 to 23 May 2025
02 Jun 2025 to 06 Jun 2025
30 Jun 2025 to 04 Jul 2025
11 Aug 2025 to 15 Aug 2025
10 Mar 2025 to 14 Mar 2025
07 Apr 2025 to 11 Apr 2025
23 Jun 2025 to 27 Jun 2025
28 Jul 2025 to 01 Aug 2025
Efficient Data Analysis with Pandas
10 Mar 2025 to 11 Mar 2025
07 Apr 2025 to 08 Apr 2025
02 Jun 2025 to 03 Jun 2025
23 Jun 2025 to 24 Jun 2025
28 Jul 2025 to 29 Jul 2025
Machine Learning from Data Preparation to Deep Learning
10 Mar 2025 to 14 Mar 2025
07 Apr 2025 to 11 Apr 2025
02 Jun 2025 to 06 Jun 2025
28 Jul 2025 to 01 Aug 2025