Everything in Python is an object.
Even values of basic primitive types (integer, string, float..) are objects. Lists are objects, tuples, dictionaries, everything.
Objects have attributes and methods that can be accessed using the dot syntax.
For example, try defining a new variable of type
age = 8
age now has access to the properties and methods defined for all
This includes, for example, access to the real and imaginary part of that number:
print(age.real) # 8 print(age.imag) # 0 print(age.bit_length()) #4 # the bit_length() method returns the number of bits necessary to represent this number in binary notation
A variable holding a list value has access to a different set of methods:
items = [1, 2] items.append(3) items.pop()
The methods depend on the type of value.
id() global function provided by Python lets you inspect the location in memory for a particular object.
id(age) # 140170065725376
Your memory value will change, I am only showing it as an example
If you assign a different value to the variable, its address will change, because the content of the variable has been replaced with another value stored in another location in memory:
age = 8 print(id(age)) # 140535918671808 age = 9 print(id(age)) # 140535918671840
But if you modify the object using its methods, the address stays the same:
items = [1, 2] print(id(items)) # 140093713593920 items.append(3) print(items) # [1, 2, 3] print(id(items)) # 140093713593920
The address only changes if you reassign a variable to another value.
Some objects are mutable, some are immutable. This depends on the object itself. If the object provides methods to change its content, then it’s mutable. Otherwise it’s immutable. Most types defined by Python are immutable. For example an
int is immutable. There are no methods to change its value. If you increment the value using
age = 8 age = age + 1 #or age += 1
and you check with
id(age) you will find that
age points to a different memory location. The original value has not mutated, we switched to another value.
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