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# Python Numbers (int, float, and complex type)

Numeric types in Python are used to store numeric values. The three numeric types in Python are:

**Note**: The type() function is used to find the type of a value or variable. For example:

x = 45 y = 34.54 z = 12j print(type(x)) print(type(y)) print(type(z))

The output is:

<class 'int'> <class 'float'> <class 'complex'>

## Python int type

Any whole number is considered of the "int" type. For example, 12, 1, 0, -1345431234, 23456765432134565432, etc.

### Advantages of the "int" type in Python

- Python arithmetic operations performed on "int" values are faster than those performed on other numeric data types.
- Memory efficiency: "int" values consume less memory than other numeric data types such as "float."
- Integral division: When performing division with the "int" type, the resulting value is an integer, which can be advantageous in certain situations.
- Supports bitwise operations

### Disadvantages of the "int" type in Python

- The range of values that the "int" type can represent is limited, so large numbers cannot be represented by this data type.
- Not suitable for non-integer values: The "int" data type can only represent whole numbers, so it cannot be used to store values that are not integers.
- No decimal support: "int" values cannot store decimal values; other data types, such as "float," must be used for this purpose.
- Memory consumption can be high: Memory usage can be high for very large "int" values, which can be detrimental in memory-constrained environments.

## Python float type

Any number with a decimal (except the complex number) is classified as float.For example, 1.9, 2.0, -4253464.2433, etc.

**Note**: Numbers like 781E4, 23E3, -3542.6E10, etc. are also considered floating-point numbers, where e or E indicates the power of 10. Therefore, these numbers are of the
"float" type.

The number 781E4 is equal to 7810000.0. Similarly, the number -3542.6e5 is equal to -354260000.0.

**Note**: A number in the form of 123E32 can also be called a scientific number.

### Advantages of the "float" type in Python

- Supports decimal numbers: The "float" type can be used to store decimal numbers, which the "int" type cannot.
- Can show a wide range of values: The "float" type can show a wide range of values, from very small to very large.
- Arithmetic operations: "Float" values can be used to do arithmetic operations quickly.
- Widely used: The "float" type is used a lot in science and engineering when decimal accuracy is important.

### Disadvantages of the "float" type in Python

- Precision problems: The way decimal numbers are stored in binary format can cause precision problems with the "float" type. This can lead to rounding errors and inaccurate calculations.
- Not suitable for exact decimal values: Some decimal values, like 0.1, can't be represented exactly with the "float" type, which can cause calculations to come up with unexpected results.
- More use of memory: The "float" type takes up more memory than the "int" type, which can be bad in situations where memory is limited.
- Not as fast as "int" type: Adding and subtracting with "float" values is slower than with "int" values.

## Python complex type

Any number with an imaginary part is considered complex. For example, 23+6J, 10J, -32J, etc.

### Advantages of the "complex" type in Python

- The "complex" type is appropriate for representing complex numbers and performing arithmetic operations on them.
- Using the "complex" type, complex numbers can be created and manipulated easily in Python.
- Supports complex arithmetic operations The "complex" type supports arithmetic operations on complex numbers, including addition, subtraction, multiplication, and division.
- Complex numbers are utilized in numerous disciplines, including mathematics, engineering, and physics, and the "complex" type in Python is a useful tool for representing and manipulating them.

### Disadvantages of the "complex" type in Python

- Complex numbers are rarely used in everyday programming tasks, and their application is limited to specific fields.
- Certain mathematical functions do not support complex numbers, limiting their applicability in certain circumstances.
- Performing arithmetic operations on complex numbers may be slower than with other data types, such as "int" and "float."
- Complex numbers require more memory than other data types, which can be disadvantageous in environments with limited memory.

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