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In Python, number datatype is used to store numeric values. Numbers in Python are immutable data type. Being an immutable datatype means that if we change the value of an already allocated number datatype then that would result in newly allocated object. In this module we will delve deeper into number datatype. Following is the list of the topics that will be covering in this module, in case you want to jump to a specific one:

Without any further ado, let’s get started.

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The number datatype is further categorized based on the type of numeric value that can be stored in it. If a variable contains a numeric value, the data type of that variable will under one of the categories of the number datatype based on the type of value assigned to that variable.

The number datatype is divided into the following five datatypes.

- integer
- long integer
- octal and hexadecimal
- floating-point
- complex numbers

Let’s understand each of the category of number datatype separately

Python integers are nothing but whole numbers, whose range dependent on the hardware on which python is run. Integers can be of different types such as positive, negative, zero, and long.

Example:

I = 123 #Positive Integer

J = -20 #Negative Integer

K = 0 #Zero Integer

J = -20 #Negative Integer

K = 0 #Zero Integer

**L** suffix is used for the representation of long integers in python. Long integers are is used to store large numbers without losing precision.

I = 99999999999L

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In python, we also have another number datatype called octal and a hexadecimal number.

To represent the octal which has base 8 in python add a preceding 0 (zero), so that Python interpreter can recognize that you want the value to be in base 8 and not in base 10.

Example:

I = 11

#Then in Octal you will write –

I = 011

print (I)

#Then in Octal you will write –

I = 011

print (I)

Output:

9

To represent the hexadecimal numbers (base 16) in Python, add a preceding 0x, so that the interpreter can recognize that you want the value to be in base 16 and not in base 10.

Example:

I = 0x11

print (I)

print (I)

Output:

17

Floating point numbers symbolize the real numbers that are written with a decimal point dividing the integer and fractional parts.

Floating point numbers may also be in scientific notation with E or e, indicating the power of 10

Example:

5.6e2 that means 5.6 * 102.

I = 2.5

J = 7.5e4

J = 7.5e4

Complex numbers are of the form of a + bj, where a is real part floating value and b is the imaginary part floating value and j represents the square root of -1.

Example:

2.5 + 2j

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Python offers a few built-in functions that let us convert numbers explicitly from one type to another. This process is called coercion. The conversion of one type of number to another becomes essential when performing certain operations that require parameters of same type. For example, programmers may need to perform mathematical operations like addition and subtraction between values of different number types such as integer and float.

We can use the following built in functions to convert one number type into another:

- int(x), to convert x into an integer value
- long(x), to convert x into a long integer value
- float(x), to convert x into a floating-point number
- complex(x), to convert x into a complex number where the imaginary part stays 0 and x becomes the real part.
- complex(x,y), to convert x and y to a complex number where x becomes the real part and y becomes the imaginary part.

Example:

a = 3.5

b = 2

c = -3.5

a = int(a)

print (a)

b = float(b)

print (b)

c = int(c)

print (c)

b = 2

c = -3.5

a = int(a)

print (a)

b = float(b)

print (b)

c = int(c)

print (c)

Output:

3

2.0

-3

2.0

-3

When converting a float datatype into integer datatype, the value gets converted into an integer value closest to zero.

This brings us to the end of this module, the next module highlights the String Datatype in Python. See you there!

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