Welcome back, review and standard distribution

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!

Using NumPy

Once you've installed NumPy you can import it as a library:

import numpy as np

my_list = [1,2,3]

np.array(my_list)

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

np.array(my_matrix)

np.arange(0,10)

np.arange(0,11,2)

np.zeros(3)

np.zeros((5,5))

np.ones((3,3))

np.linspace(0,10,50)

Creates an identity matrix

np.eye(4)

Create an array of the given shape and populate it with random samples from a uniform distribution over [0, 1).

np.random.rand(2)

np.random.rand(5,5)

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

np.random.randn(2)

np.random.randn(5,5)

Return random integers from low (inclusive) to high (exclusive).

np.random.randint(1,100)

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

Let's discuss some useful attributes and methods or an array:

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

arr

ranarr

Reshape Returns an array containing the same data with a new shape.

arr.reshape(5,5)

max,min,argmax,argmin

These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax

ranarr

ranarr.max()

ranarr.argmax()

ranarr.min()

ranarr.argmin()

Shape

Shape is an attribute that arrays have (not a method):

# 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

dtype

You can also grab the data type of the object in the array:

arr.dtype