import numpy as np

"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>

NumPy
NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Data Science with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks.
Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great StackOverflow post.
We will only learn the basics of NumPy, to get started we need to install it!
Installation Instructions
It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:
conda install numpy

If you do not have Anaconda and can not install it, please refer to Numpy's official documentation on various installation instructions.
Using NumPy
Once you've installed NumPy you can import it as a library:
End of explanation
"""


my_list = [1,2,3]
my_list

np.array(my_list)

my_matrix = [[1,2,3],[4,5,6],[7,8,9]]
my_matrix

np.array(my_matrix)

"""
Explanation: Numpy has many built-in functions and capabilities. We won't cover them all but instead we will focus on some of the most important aspects of Numpy: vectors,arrays,matrices, and number generation. Let's start by discussing arrays.
Numpy Arrays
NumPy arrays are the main way we will use Numpy throughout the course. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).
Let's begin our introduction by exploring how to create NumPy arrays.
Creating NumPy Arrays
From a Python List
We can create an array by directly converting a list or list of lists:
End of explanation
"""


np.arange(0,10)

np.arange(0,11,2)

"""
Explanation: Built-in Methods
There are lots of built-in ways to generate Arrays
arange
Return evenly spaced values within a given interval.
End of explanation
"""


np.zeros(3)

np.zeros((5,5))

np.ones(3)

np.ones((3,3))

"""
Explanation: zeros and ones
Generate arrays of zeros or ones
End of explanation
"""


np.linspace(0,10,3)

np.linspace(0,10,50)

"""
Explanation: linspace
Return evenly spaced numbers over a specified interval.
End of explanation
"""


np.eye(4)

"""
Explanation: eye
Creates an identity matrix
End of explanation
"""


np.random.rand(2)

np.random.rand(5,5)

"""
Explanation: Random
Numpy also has lots of ways to create random number arrays:
rand
Create an array of the given shape and populate it with
random samples from a uniform distribution
over [0, 1).
End of explanation
"""


np.random.randn(2)

np.random.randn(5,5)

"""
Explanation: randn
Return a sample (or samples) from the "standard normal" distribution. Unlike rand which is uniform:
End of explanation
"""


np.random.randint(1,100)

np.random.randint(1,100,10)

"""
Explanation: randint
Return random integers from low (inclusive) to high (exclusive).
End of explanation
"""


arr = np.arange(25)
ranarr = np.random.randint(0,50,10)

arr

ranarr

"""
Explanation: Array Attributes and Methods
Let's discuss some useful attributes and methods or an array:
End of explanation
"""


arr.reshape(5,5)

"""
Explanation: Reshape
Returns an array containing the same data with a new shape.
End of explanation
"""


ranarr

ranarr.max()

ranarr.argmax()

ranarr.min()

ranarr.argmin()

"""
Explanation: max,min,argmax,argmin
These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax
End of explanation
"""


# Vector
arr.shape

# Notice the two sets of brackets
arr.reshape(1,25)

arr.reshape(1,25).shape

arr.reshape(25,1)

arr.reshape(25,1).shape

"""
Explanation: Shape
Shape is an attribute that arrays have (not a method):
End of explanation
"""


arr.dtype

"""
Explanation: dtype
You can also grab the data type of the object in the array:
End of explanation
"""


