Introduction
Numbers are a fundamental aspect of computer programming. Python provides robust support for numeric operations and data types. This lesson covers the three built-in numeric types available in Python: integers, floating-point numbers, and complex numbers.
Integers
An integer is a whole number that can be written without a fractional or decimal component. They can be positive, negative, or zero, such as -2, -1, 0, 1, 2.
In Python, integers are represented by the int type. Python int literals are written then same way as integers are in common language, i.e. a sequence of digits, with a minus sign on the left to represent a negative number.
Example:
positive_integer = 10 negative_integer = -5 zero = 0
Python’s int type can handle arbitrarily large integers. Unlike some other programming languages that have a fixed maximum size for integers, Python’s integers grow as needed to accommodate the value.
Example:
very_large_integer = 500000000000000000000000000000000000000000000000000
Floating Point Numbers
Floating-point numbers, often abbreviated as floats, are numbers that contain a decimal point. The floating-point type in Python is float.
Float literals are written in Python as either a decimal fraction, a scientific notation, or both.
Example:
approx_pi = 3.14 some_float = 10e4 another_float = 6.022e23
Python implements floating-point numbers according to the IEEE 754 standard, which defines the format for representing both floating-point numbers and their arithmetic operations. This standard allows Python to maintain consistency in floating-point operations across different platforms and architectures.
Precision Problems With Floats
Although floating-point numbers are essential for representing a wide range of real numbers, they have limitations due to their finite precision. This limitation can lead to problems where calculations may not produce the expected results due to rounding errors.
Floating point numbers are represented in computer memory with a finite sequence of digits. Consequently, they cannot precisely represent numbers that require an infinite sequence. The numbers that floats can’t represent not only include irrational numbers (that require infinite non-repeating sequences of digits) such as Pi, but also rational numbers that require infinite repeating sequences, such as 1/3, which is represented in decimal by 0.333…
However, since digital computers are based on the binary system (1’s and 0’s), the rational numbers that require an infinite repeating sequence of digits are not necessarily the same ones we are used to. For example, multiples of one tenth in decimal (0.1) need an infinite sequences of digits in binary, which leads to unexpected behaviors.
Example:
print(0.1 + 0.2)
Output:
0.30000000000000004
In the example above, the floating-point numbers 0.1 and 0.2 are added, and seemingly the result of this addition is a small amount more than 0.3. This happens because it’s not really 0.1 and 0.2 that are added, but the imprecise, truncated versions of their binary representations. When the truncated numbers are added in binary and converted to decimal for printing, the result is not exactly 0.3 due to rounding errors.
Complex Numbers
A complex number is a number that has both a real part and an imaginary part, typically expressed in the form a + bj, where a is the real part, b is the imaginary part, and j (or i in mathematics) is the imaginary unit with the property that j2 = -1.
Complex numbers are represented in Python by the complex type. Python uses j to denote the imaginary part of a complex number literal.
Declaring and Using Complex Numbers
Declaring a complex number in Python is straightforward. You can use the complex() function or the literal notation with j.
Here is the basic syntax for declaring a complex number using the complex() function:
z = complex(real, imag)
And this is how you can declare a complex number literal with j:
z = real + imagj
In this syntax:
- real: The real part of the complex number. This part must be an integer or float literal. It can be omitted, in which case the real part is set to 0.
- imag: The imaginary part of the complex number. This part must also be an integer or float literal.
Example:
z1 = complex(2, 3) z2 = 2 + 3j z3 = 0.5 + 2.1j
Underscores In Numbers
Sequences of digits in numeric literals can have underscores in between the digits. Python ignores those underscores, and they function as an optional way to improve the readability of long numbers in source code. They are similar in purpose to commas that are placed after every third digit in written language.
The Python print() function and terminal clear out the underscores.
Example:
print(50_000_000_000_000)
Output:
50000000000000
Arithmetic Operations
Python provides a variety of arithmetic operations that can be performed on numeric types using operators. Here are some of the most commonly used operators:
- Addition (
+): Adds two numbers. - Subtraction (
-): Subtracts one number from another. - Multiplication (
*): Multiplies two numbers. - Division (
/): Divides one number by another and returns a float. - Floor Division (
//): Divides one number by another and returns an integer. - Modulus (
%): Returns the remainder of the division. - Exponentiation (
**): Raises a number to the power of another.
Examples:
a = 10 b = 3 print(a + b) # Output: 13 print(a - b) # Output: 7 print(a * b) # Output: 30 print(a / b) # Output: 3.3333333333333335 print(a // b) # Output: 3 print(a % b) # Output: 1 print(a ** b) # Output: 1000
All the arithmetic operators work on int and float types. They also work on complex numbers except for // and %.
If you use an operator with two of the same numeric type, the result is of that type, except for /, which results in a float when two ints are divided. // division will result in an int.
You can use operators with two different numeric types. If one of the operands is a float the result is a float, unless one is complex, which results in a complex type.
Summary & Reference for Python Numbers
Python provides robust support for numeric operations with floating-point, and complex numbers data types.
Integers such as -2, -1, 0, 1, and 2 are represented in Python by the int type.
positive_integer = 10 negative_integer = -5 zero = 0
Python’s int type can handle arbitrarily large integers.
very_large_integer = 500000000000000000000000000000000000000000000000000
Floating-point numbers (floats), are numbers that contain a decimal point, and are represented in Python by the float type.
approx_pi = 3.14 some_float = 10e4 another_float = 6.022e23
Floats hold a limited number of binary digits and therefore cannot represent all real numbers precisely, leading to unexpected results.
0.1 + 0.2 # --> 0.30000000000000004
Complex numbers have both a real and an imaginary part, and are represented in Python by the complex type. Python literals are written with the j notation for the imaginary part.
z = 2 + 3.5j
You can also create a complex number using the complex() function.
complex(2, 3.5) # --> 2+3.5j
Sequences of digits in numeric literals can have underscores in between the digits. Those can be used to improve the readability of long numbers in source code.
print(50_000_000_000_000) # --> 50000000000000
Python provides a variety of arithmetic operators that can be used on numeric types. Those include addition (+), subtraction (-), multiplication (*), division (/), floor division (//), modulus (%), and exponentiation (**).
1.1 + 2.2 # --> 3.3
All the arithmetic operators work on int and float types. They also work on complex numbers except for // and %.