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Python NumPy For Your Grandma - 2.3 Creating NumPy Arrays

In this secion, we’ll look at different ways to create a NumPy array.

As we saw earlier, you can make an array by typing np dot array and then passing in a list of values.

import numpy as np
np.array(['a', 'b', 'c'])
## array(['a', 'b', 'c'], dtype='<U1')

You can also make a 2-dimensional array from a list of lists like this.

np.array([
    ['a', 'b'],
    ['c', 'd'],
    ['e', 'f']
])
## array([['a', 'b'],
##        ['c', 'd'],
##        ['e', 'f']], dtype='<U1')

You can also make a three dimensional array from a list of lists of lists.

np.array([
    [
        ['a', 'b'],
        ['c', 'd'],
        ['e', 'f']
    ],
    [
        ['g', 'h'],
        ['i', 'j'],
        ['k', 'l']
    ]
])
## array([[['a', 'b'],
##         ['c', 'd'],
##         ['e', 'f']],
## 
##        [['g', 'h'],
##         ['i', 'j'],
##         ['k', 'l']]], dtype='<U1')

… and so on.

Now let’s say you want to make a three by five array filled with zeros. This’ll be a good time so check out the NumPy documentation. It turns out, there’s a function called zeros() that does exactly what we want. The only argument that doesn’t have a default value is shape, so we’ll need to pass in the shape as an int or tuple of ints.

If we call np.zeros(shape = 3) we’ll get back a 1d array with three zeros.

np.zeros(shape = 3)
## array([0., 0., 0.])

But, our goal was to get a three by five array so I’m gonna set the shape to a tuple like this.

np.zeros(shape = (3,5))
## array([[0., 0., 0., 0., 0.],
##        [0., 0., 0., 0., 0.],
##        [0., 0., 0., 0., 0.]])

Now, what about the generic case? Say, instead of zeros you want a three by five array filled with the word ‘cat’. For this, we can use a function called full(). We just need to pass in a shape and fill_value. I’ll do that here.

np.full(shape = (3,5), fill_value = 'cat')
## array([['cat', 'cat', 'cat', 'cat', 'cat'],
##        ['cat', 'cat', 'cat', 'cat', 'cat'],
##        ['cat', 'cat', 'cat', 'cat', 'cat']], dtype='<U3')

Another very common array to build is a sequence array like [1,2,3,4]. The function for this is np.arange() where you pass in start, stop, and step values. For example,

np.arange(start = 1, stop = 5, step = 1)
## array([1, 2, 3, 4])

(Note that start is inclusive while stop is exclusive.)

Alternatively, if you just do np.arange() and pass in an integer like 10, you’ll get back a sequence of ten integers starting from zero.

np.arange(10)
## array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

By the way, the name “arange” stands for “array range” because it’s like the array version of a python range.

Another useful function is np.random.randint() which let’s you build an array of random integers selected with replacement. Here we’ll make a two by three array of random ints between one (inclusive) and seven (exclusive). You can think of this as two iterations of rolling three dice.

np.random.randint(low = 1, high = 7, size = (2, 3))
## array([[2, 3, 1],
##        [5, 5, 2]])

Obviously there’s a bunch of other methods to build arrays, and we’ll see a lot of them later the course. But hopefully this was enough to get your feet wet and introduce you to the NumPy documentation.


Course Curriculum

  1. Introduction
    1.1 Introduction
  2. Basic Array Stuff
    2.1 NumPy Array Motivation
    2.2 NumPy Array Basics
    2.3 Creating NumPy Arrays
    2.4 Indexing 1-D Arrays
    2.5 Indexing Multidimensional Arrays
    2.6 Basic Math On Arrays
    2.7 Challenge: High School Reunion
    2.8 Challenge: Gold Miner
    2.9 Challenge: Chic-fil-A
  3. Intermediate Array Stuff
    3.1 Broadcasting
    3.2 newaxis
    3.3 reshape()
    3.4 Boolean Indexing
    3.5 nan
    3.6 infinity
    3.7 random
    3.8 Challenge: Love Distance
    3.9 Challenge: Professor Prick
    3.10 Challenge: Psycho Parent
  4. Common Operations
    4.1 where()
    4.2 Math Functions
    4.3 all() and any()
    4.4 concatenate()
    4.5 Stacking
    4.6 Sorting
    4.7 unique()
    4.8 Challenge: Movie Ratings
    4.9 Challenge: Big Fish
    4.10 Challenge: Taco Truck
  5. Advanced Array Stuff
    5.1 Advanced Array Indexing
    5.2 View vs Copy
    5.3 Challenge: Population Verification
    5.4 Challenge: Prime Locations
    5.5 Challenge: The Game of Doors
    5.6 Challenge: Peanut Butter
  6. Final Boss
    6.1 as_strided()
    6.2 einsum()
    6.3 Challenge: One-Hot-Encoding
    6.4 Challenge: Cumulative Rainfall
    6.5 Challenge: Table Tennis
    6.6 Challenge: Where’s Waldo
    6.7 Challenge: Outer Product

Additional Content

  1. Python Pandas For Your Grandpa
  2. Neural Networks For Your Dog
  3. Introduction To Google Colab