What will be the result of the following print statements:

Code inspection

Code:

np.arange('2005-02', '2005-03', dtype='datetime64[D]')


Output:
array(['2005-02-01', '2005-02-02', '2005-02-03', '2005-02-04',
       '2005-02-05', '2005-02-06', '2005-02-07', '2005-02-08',
       '2005-02-09', '2005-02-10', '2005-02-11', '2005-02-12',
       '2005-02-13', '2005-02-14', '2005-02-15', '2005-02-16',
       '2005-02-17', '2005-02-18', '2005-02-19', '2005-02-20',
       '2005-02-21', '2005-02-22', '2005-02-23', '2005-02-24',
       '2005-02-25', '2005-02-26', '2005-02-27', '2005-02-28'],
      dtype='datetime64[D]')

Example of a datetime object in NumPy

uint8 --> Unsigned integer (0 to 255)

uint16 --> Unsigned integer (0 to 65535)

uint32 --> Unsigned integer (0 to 4294967295)

uint64 --> Unsigned integer (0 to 18446744073709551615)

NumPy Unassigned Integers

Complex number, represented by two 32-bit floats (real and imaginary components)

NumPy Complex Float

NumPy allows the subtraction of two Datetime values, an operation which produces a number with a time unit. Because NumPy doesn’t have a physical quantities system in its core, the timedelta64 data type was created to complement datetime64. The arguments for timedelta64 are a number, to represent the number of units, and a date/time unit, such as (D)ay, (M)onth, (Y)ear, (h)ours, (m)inutes, or (s)econds. The timedelta64 data type also accepts the string “NAT” in place of the number for a “Not A Time” value.

NumPy Timedeltas

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)

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NumPy is a Python library for scientific computing that contains support for some fundamental data structures used by scikit-learn, such as multi-dimensional arrays. Example import: `import numpy as np`

NumPy

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

NumPy Documentation

Typically, data that's input to scikit-learn will be in the form of a NumPy array.

NumPy vs. Scikit-learn

What does the following code do?

Recall that "np.dot(a,b)" performs a matrix multiplication on a and b, whereas "a*b" performs an element-wise multiplication. Consider the two following random arrays "a" and "b": what is the shape of c?

Consider the following code: what will be c?

Consider the two following random arrays "a" and "b" below: what will be the shape of "c"?

Because the Numpy array is densely packed in memory due to its homogeneous type, Numpy gets around 30 times faster than Python.

Make big difference when handling big data.

Numpy Speed


In NumPy, there are two objects that are crucial to understanding how this library works. Those are:

1. N-dimensional array object (ndarray)

2. Universal function object (ufunc)

However, keep in mind that there are other objects that are built upon these that can be incorporated into whatever you're doing.

Fundamental Objects in NumPy

Within the NumPy library, there are several types of mathematical functions built-in. These functions are typically used across elements of a NumPy array.

Types of Mathematical Functions:
- Trigonometric
- Rounding
- Exponents and Logarithms
- Arithmetic
- Other Special Functions

Mathematical Functions in NumPy

Data Types in NumPy

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

Lists in Python are indexed, which means they can have items with the same value.
For example, the picture below shows that the list called thislist has the value "cherry" twice. This is not an issue will lists.

Duplicate Values in a List

If you want to specify the data type of an object, you can use constructor functions like str(), float(), list(), and tuple().

Example:
x = list(("apple", "banana", "cherry"))

The example above will turn the sequence into a list

Assigning a Python Object to Have a Certain Type

Python has 4 collection data types:
- lists - Ordered, mutable collection of items allowing duplicate members
- tuples - Ordered, immutable collection of items allowing duplicate members
- sets - Unordered, unindexed collection of items not allowing duplicate members
- dictionaries - Ordered, mutable collection of items not allowing duplicate members


Python Collections

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.

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