What are numeric in python?


Python Numbers

There are three numeric types in Python:

  • int
  • float
  • complex

Variables of numeric types are created when you assign a value to them:

Example

x = 1    # int
y = 2.8  # float
z = 1j   # complex

To verify the type of any object in Python, use the type() function:


Int

Int, or integer, is a whole number, positive or negative, without decimals, of unlimited length.

Example

Integers:

x = 1
y = 35656222554887711
z = -3255522

print(type(x))
print(type(y))
print(type(z))

Try it Yourself »


Float

Float, or "floating point number" is a number, positive or negative, containing one or more decimals.

Example

Floats:

x = 1.10
y = 1.0
z = -35.59

print(type(x))
print(type(y))
print(type(z))

Try it Yourself »

Float can also be scientific numbers with an "e" to indicate the power of 10.

Example

Floats:

x = 35e3
y = 12E4
z = -87.7e100

print(type(x))
print(type(y))
print(type(z))

Try it Yourself »



Complex

Complex numbers are written with a "j" as the imaginary part:

Example

Complex:

x = 3+5j
y = 5j
z = -5j

print(type(x))
print(type(y))
print(type(z))

Try it Yourself »


Type Conversion

You can convert from one type to another with the int(), float(), and complex() methods:

Example

Convert from one type to another:

x = 1    # int
y = 2.8  # float
z = 1j   # complex

#convert from int to float:
a = float(x)

#convert from float to int:
b = int(y)

#convert from int to complex:
c = complex(x)

print(a)
print(b)
print(c)

print(type(a))
print(type(b))
print(type(c))

Try it Yourself »

Note: You cannot convert complex numbers into another number type.


Random Number

Python does not have a random() function to make a random number, but Python has a built-in module called random that can be used to make random numbers:

Example

Import the random module, and display a random number between 1 and 9:

import random

print(random.randrange(1, 10))

Try it Yourself »

In our Random Module Reference you will learn more about the Random module.


Test Yourself With Exercises

Exercise:

Insert the correct syntax to convert x into a floating point number.

Start the Exercise




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Number Data Type in Python

Python supports integers, floating-point numbers and complex numbers. They are defined as int, float, and complex classes in Python.

Integers and floating points are separated by the presence or absence of a decimal point. For instance, 5 is an integer whereas 5.0 is a floating-point number.

Complex numbers are written in the form, x + yj, where x is the real part and y is the imaginary part.

We can use the type() function to know which class a variable or a value belongs to and isinstance() function to check if it belongs to a particular class.

Let's look at an example:

a = 5

print(type(a))

print(type(5.0))

c = 5 + 3j
print(c + 3)

print(isinstance(c, complex))

When we run the above program, we get the following output:

<class 'int'>
<class 'float'>
(8+3j)
True

While integers can be of any length, a floating-point number is accurate only up to 15 decimal places (the 16th place is inaccurate).

The numbers we deal with every day are of the decimal (base 10) number system. But computer programmers (generally embedded programmers) need to work with binary (base 2), hexadecimal (base 16) and octal (base 8) number systems.

In Python, we can represent these numbers by appropriately placing a prefix before that number. The following table lists these prefixes.

Number SystemPrefix
Binary '0b' or '0B'
Octal '0o' or '0O'
Hexadecimal '0x' or '0X'

Here are some examples

# Output: 107
print(0b1101011)

# Output: 253 (251 + 2)
print(0xFB + 0b10)

# Output: 13
print(0o15)

When you run the program, the output will be:

107
253
13

Type Conversion

We can convert one type of number into another. This is also known as coercion.

Operations like addition, subtraction coerce integer to float implicitly (automatically), if one of the operands is float.

>>> 1 + 2.0
3.0

We can see above that 1 (integer) is coerced into 1.0 (float) for addition and the result is also a floating point number.

We can also use built-in functions like int(), float() and complex() to convert between types explicitly. These functions can even convert from strings.

>>> int(2.3)
2
>>> int(-2.8)
-2
>>> float(5)
5.0
>>> complex('3+5j')
(3+5j)

When converting from float to integer, the number gets truncated (decimal parts are removed).


Python Decimal

Python built-in class float performs some calculations that might amaze us. We all know that the sum of 1.1 and 2.2 is 3.3, but Python seems to disagree.

>>> (1.1 + 2.2) == 3.3
False

What is going on?

It turns out that floating-point numbers are implemented in computer hardware as binary fractions as the computer only understands binary (0 and 1). Due to this reason, most of the decimal fractions we know, cannot be accurately stored in our computer.

Let's take an example. We cannot represent the fraction 1/3 as a decimal number. This will give 0.33333333... which is infinitely long, and we can only approximate it.

It turns out that the decimal fraction 0.1 will result in an infinitely long binary fraction of 0.000110011001100110011... and our computer only stores a finite number of it.

This will only approximate 0.1 but never be equal. Hence, it is the limitation of our computer hardware and not an error in Python.

>>> 1.1 + 2.2
3.3000000000000003

To overcome this issue, we can use the decimal module that comes with Python. While floating-point numbers have precision up to 15 decimal places, the decimal module has user-settable precision.

Let's see the difference:

import decimal

print(0.1)

print(decimal.Decimal(0.1))

Output

0.1
0.1000000000000000055511151231257827021181583404541015625

This module is used when we want to carry out decimal calculations as we learned in school.

It also preserves significance. We know 25.50 kg is more accurate than 25.5 kg as it has two significant decimal places compared to one.

from decimal import Decimal as D

print(D('1.1') + D('2.2'))

print(D('1.2') * D('2.50'))

Output

3.3
3.000

Notice the trailing zeroes in the above example.

We might ask, why not implement Decimal every time, instead of float? The main reason is efficiency. Floating point operations are carried out much faster than Decimal operations.

When to use Decimal instead of float?

We generally use Decimal in the following cases.

  • When we are making financial applications that need exact decimal representation.
  • When we want to control the level of precision required.
  • When we want to implement the notion of significant decimal places.

Python Fractions

Python provides operations involving fractional numbers through its fractions module.

A fraction has a numerator and a denominator, both of which are integers. This module has support for rational number arithmetic.

We can create Fraction objects in various ways. Let's have a look at them.

import fractions

print(fractions.Fraction(1.5))

print(fractions.Fraction(5))

print(fractions.Fraction(1,3))

Output

3/2
5
1/3

While creating Fraction from float, we might get some unusual results. This is due to the imperfect binary floating point number representation as discussed in the previous section.

Fortunately, Fraction allows us to instantiate with string as well. This is the preferred option when using decimal numbers.

import fractions

# As float
# Output: 2476979795053773/2251799813685248
print(fractions.Fraction(1.1))

# As string
# Output: 11/10
print(fractions.Fraction('1.1'))

Output

2476979795053773/2251799813685248
11/10

This data type supports all basic operations. Here are a few examples.

from fractions import Fraction as F

print(F(1, 3) + F(1, 3))

print(1 / F(5, 6))

print(F(-3, 10) > 0)

print(F(-3, 10) < 0)

Output

2/3
6/5
False
True

Python Mathematics

Python offers modules like math and random to carry out different mathematics like trigonometry, logarithms, probability and statistics, etc.

import math

print(math.pi)

print(math.cos(math.pi))

print(math.exp(10))

print(math.log10(1000))

print(math.sinh(1))

print(math.factorial(6))

Output

3.141592653589793
-1.0
22026.465794806718
3.0
1.1752011936438014
720

Here is the full list of functions and attributes available in the Python math module.

import random

print(random.randrange(10, 20))

x = ['a', 'b', 'c', 'd', 'e']

# Get random choice
print(random.choice(x))

# Shuffle x
random.shuffle(x)

# Print the shuffled x
print(x)

# Print random element
print(random.random())

When we run the above program we get the output as follows.(Values may be different due to the random behavior)

18
e
['c', 'e', 'd', 'b', 'a']
0.5682821194654443

Here is the full list of functions and attributes available in the Python random module.

What are 4 different numeric types in Python?

Python provides four basic types of numbers: plain integers, long integers, floating point numbers and complex numbers.

What are the two numeric data types in Python?

Python Numeric Data Type int - holds signed integers of non-limited length. long- holds long integers(exists in Python 2.

Is numeric library Python?

Numeric is a Python module for high-performance, numeric computing. It provides much of the functionality and performance of commercial numeric software such as Matlab; in some cases, it provides more functionality than commercial software.

Is string a numeric data type in Python?

Text data type is known as Strings in Python, or Objects in Pandas. Strings can contain numbers and / or characters. For example, a string might be a word, a sentence, or several sentences.

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