Python has four built-in collection data types:
- Lists are ordered and mutable and allow duplicate items.
- Tuples are ordered and immutable and allow duplicate items.
- Sets are unordered and do not allow duplicate items.
- Dictionaries are mutable mappings from keys to values.

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Values can be classified into different classes, known as data types:
- Integer (int) - 1, 4, 7, 24, 578, etc.
- Float (float) - 2.2, 2.59, 3.14159, etc.
- String (str) - *Any combination of characters in quotes
- List (list) - An ordered, mutable collection of items
- Tuple (tuple) - An ordered, immutable collection of items
- Dictionary (dict) - An unordered collection of data with keys and values
- Boolean (bool) - True or False
 
The terms "data type" and "class" are often used interchangeably.

Data Types in Python

Use the ```type()``` function to get the type of an object in Python. 
For example, 
```print(type(4))``` 
outputs ```<class 'int'>```.

Getting the type of an object

- Expressions are any combination of data types to be evaluated. Evaluating an expression results in values/objects. Expressions can also be evaluated into or set to values. 
- Asking Python to print an expression will result in the interpreter attempting to evaluate the expression and display the result

Expressions in Python 

In Python, a sequence refers to any ordered set. This includes strings, lists and tuples. All sequences can be sliced, indexed into, and are iterable.

Sequences

An object is mutable if its data can be changed after initialization. Inversely, an object is immutable if its data cannot be changed after creation. Lists are mutable, and tuples are immutable. This makes lists better suited for data which we anticipate will be modified. However, tuples (but not lists) can serve as dictionary keys. In addition to lists, dictionary objects are also mutable.

In Python, most data types are immutable, such as integers, floats, strings, tuples and booleans. Immutable data types are faster to use in code, but their values can only be changed by creating a copy of the object to be altered. 

Mutability

In Python, the type() function is used to return the class (data type) of an object. For example:
```python
print(type(66))
print(type("hello there"))
print(type(["General", "Kenobi"]))
```
will print:
<class 'int'>
<class 'str'>
<class 'list'>

type() Function

In Python, lists are indexed collections, so they can contain multiple items with the same value; duplicates are allowed. For example, a list can hold the string "cherry" twice without raising an error. Permitting duplicate members is a defining property of lists (and tuples), unlike sets, which store only unique members.

Duplicate Values in a List

Data types in a program are likely to be allocated into memory when encountered in a program. Python's interpreter includes the Python memory manager, which handles how data types are stored and tracked in memory. The user has no control over this process. Memory allocation for each new variable is performed on demand by the Python memory manager automatically.

Memory Allocation

While Python natively provides built-in data types such as booleans, floats, and integers, NumPy offers additional specialized data types. These are often referred to by single-character codes:
- `i`: integer
- `b`: boolean
- `u`: unsigned integer
- `f`: float
- `c`: complex float
- `m`: timedelta
- `M`: datetime
- `O`: object
- `S`: string
- `U`: unicode string
- `V`: void (fixed chunk of memory for other types)

Data Types in NumPy

Python constructor functions such as `str()`, `float()`, `list()`, and `tuple()` can be used to convert a compatible value to a specified data type. For example, `x = list(("apple", "banana", "cherry"))` converts the supplied sequence into a list.

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