x = 1         # x is an integer
x = 'hello'   # now x is a string
x = [1, 2, 3] # now x is a list

"""
Explanation: Variables and Objects in Python

Now we move to the semantics of a language which involve the meaning of the statements.
We will start with variables and objects, which are the main ways you store, reference, and operate on data within a Python script.

Python Variables Are Pointers
Assigning variables in Python is as easy as putting a variable name to the left of the equals (=) sign:
```python
assign 4 to the variable x
x = 4
```
Note that you don't have to declare a variable type before the assignment.


In many programming languages, variables = "buckets" of data


So in FORTRAN, for example, when you write


fortran
! FORTRAN code
integer x
x = 4
you are essentially defining a "memory bucket" named x, and putting the value 4 into it.
In Python, by contrast, variables $\approx$ pointers.
So in Python, when you write
python
x = 4
you are essentially defining a pointer named x that points to some other bucket containing the value 4.
Therefore, there is no need to "declare" the variable.
This is the sense in which people say Python is dynamically-typed: variable names can point to objects of any type.
So in Python, you can do things like this:
End of explanation
"""


x = [1, 2, 3]
y = x

"""
Explanation: While users of statically-typed languages might miss the type-safety that comes with declarations like those found in FORTRAN or C, this dynamic typing is one of the pieces that makes Python so quick to write and easy to read.
Dangers of "variable as pointer" model
If we have two variable names pointing to the same mutable object, then changing one will change the other as well!
For example, let's create and modify a list:
End of explanation
"""


print(y)

x.append(4) # append 4 to the list pointed to by x
print(y) # y's list is modified as well!

"""
Explanation: We've created two variables x and y which both point to the same object.
Because of this, if we modify the list via one of its names, we'll see that the "other" list will be modified as well:
End of explanation
"""


x = 'something else'
print(y)  # y is unchanged

"""
Explanation: This behavior might seem confusing if you're wrongly thinking of variables as buckets that contain data.
But if you're correctly thinking of variables as pointers to objects, then this behavior makes sense.
Note also that if we use "=" to assign another value to x, this will not affect the value of y - assignment is simply a change of what object the variable points to:
End of explanation
"""


x = 10
y = x
x += 5  # add 5 to x's value, and assign it to x
print("x =", x)
print("y =", y)

"""
Explanation: Again, this makes perfect sense if you think of x and y as pointers, and the "=" operator as an operation that changes what the name points to.
You might wonder whether this pointer idea makes arithmetic operations in Python difficult to track, but Python is set up so that this is not an issue. Numbers, strings, and other simple types are immutable: you can't change their value – you can only change what values the variables point to.
So, for example, it's perfectly safe to do operations like the following:
End of explanation
"""


L = [1, 2, 3]
L.append(100)
print(L)

"""
Explanation: When we call x += 5, we are not modifying the value of the 5 object pointed to by x, but rather we are changing the object to which x points.
For this reason, the value of y is not affected by the operation.
Everything Is an Object
Python is an object-oriented programming language, and in Python everything is an object.
In OOP languages, an object is an entity that contains data along with associated metadata and/or functionality.
In Python everything is an object, which means every entity has some metadata (called attributes) and associated functionality (called methods).
These attributes and methods are accessed via the dot syntax.
For example, before we saw that lists have an append method, which adds an item to the list, and is accessed via the dot (".") syntax:
End of explanation
"""


x = 4.5
print(x.real, "+", x.imag, 'i')

"""
Explanation: While it might be expected for compound objects like lists to have attributes and methods, what is sometimes unexpected is that in Python even simple types have attached attributes and methods.
For example, numerical types have a real and imag attribute that returns the real and imaginary part of the value, if viewed as a complex number:
End of explanation
"""


x = 4.5
x.is_integer()

x = 4.0
x.is_integer()

"""
Explanation: Methods are like attributes, except they are functions that you can call using opening and closing parentheses.
For example, floating point numbers have a method called is_integer that checks whether the value is an integer:
End of explanation
"""


type(x.is_integer)

"""
Explanation: When we say that everything in Python is an object, we really mean that everything is an object – even the attributes and methods of objects are themselves objects with their own type information:
End of explanation
"""


