Compare mutable data types with immutable data types in Python ? List the
immutable and mutable data types, separately. Discuss mutable and immutable data type, with
their respective syntax in Python.
Mutable data types in Python are those that can be changed or modified after they are created. This means they allow in-place modifications without changing their identity. On the other hand, immutable data types cannot be changed once they are created and any operation that appears to modify the value actually creates a new object with a new identity.
Examples of mutable data types in Python include:
- Lists [ ]
- Dictionaries { }
- Sets { }
- Bytearrays bytearray( )
- User-defined classes (if they are implemented as mutable)
Examples of immutable data types in Python include:
- Numeric data types (int, float, complex)
- Strings ' '
- Tuples ( )
Mutable data types can be modified using various methods or functions available to perform operations such as adding or removing elements, changing the value of elements, or resizing the object.
For example, let's consider a list:
```
my_list = [1, 2, 3]
my_list.append(4) # Adding an element
my_list[0] = 10 # Modifying an element
```
Immutable data types cannot be modified directly but can be operated upon to create a new object with a new value. Any operation that seems to modify the value actually creates a new object, leaving the original object unchanged.
For example, let's consider a string:
```
my_string = "Hello"
new_string = my_string + " World" # Concatenating strings
```
In the above example, the original string remains unchanged, and a new string is created with the concatenated value.
Immutable objects are generally considered safer in terms of data integrity, as they cannot be modified accidentally. They also allow for efficient caching, hashing, and usage as keys in dictionaries. Mutable objects are more flexible and allow for dynamic changes, but they require additional care to ensure consistency.
It is important to note that operations such as assignment, slicing, or copying of mutable objects will not create a new object. The new object will still refer to the same memory location or identity as the original object. To create a new object, you need to use methods or functions that explicitly create a copy.
For example, creating a copy of a list:
```
my_list = [1, 2, 3]
new_list = my_list.copy() # Creating a shallow copy
```
In the example above, `new_list` will have the same values as `my_list`, but modifying one will not affect the other.