Python NumPy For Your Grandma - 2.4 Indexing 1-D Arrays

In this section, we’ll look at how to index a 1d array to access and modify its elements.

We’ll start by making a 1d array called foo with five elements.

import numpy as np
foo = np.array([10, 20, 30, 40, 50])
## [10 20 30 40 50]

We can access the ith element just like a python list using square bracket notation where the first element starts at index 0, the second element starts at index 1, and so on.

foo[0]  # 1st element
## 10
foo[1]  # 2nd element
## 20

If we want to modify an element, say we want to change the 2nd element to 99, then we can just do

foo[1] = 99
## [10 99 30 40 50]

Since we know foo has five elements, if we wanted to access the last element, we can do

## 50

If we want to make that more dynamic, we can replace the index, 4, with len(foo) - 1

foo[len(foo) - 1]
## 50

But we can make that even simpler with negative indexing. Just like python lists, the index -1 returns the last element in the array, -2 returns the second to last element, and so on.

## 50

And if we try to access an element outside the bounds of the array, we’ll get an “out of bounds” error.

foo[999]  # error: out of bounds

If we want to access multiple elements at once, we can do that too, using a list or numpy array of indices. For example, we could do

foo[[0, 1, 4]]
## array([10, 99, 50])

Or we could do

## array([10, 99, 10, 99])

Notice, the indices don’t need to be unique. We could even pass in something like this.

foo[np.zeros(shape=3)]  # error

Well, maybe not. The problem is that the zeros() function returns an array of floating point zeros by default, and indices need to be integers. So we make a slight tweak and set the dtype argument to int64 and we’re all good.

foo[np.zeros(shape=3, dtype='int64')]
## array([10, 10, 10])

We can also use slicing just like python lists. The signature here is basically foo[start index : end index : step size]. And there are a lot of shorthands to this, so let’s look at some examples.

If we do foo[:2], we get every element from the beginning of the array to index 2 exclusive.

## array([10, 99])

If we do foo[2:], we get every element from index 2 to the end of the array.

## array([30, 40, 50])

And if we do foo[::2], we get every other element from the beginning to the end. In other words we get the elements at indices 0, 2, 4, ….

## array([10, 30, 50])

Another thing we can do is modify multiple elements at once. For example,

foo[[0, 1, 4]] = [100, 200, 400]
## [100 200  30  40 400]

In this case, our list of values needs to be the same size as our list of indices, unless we assign to a single value in which case it gets applied to every index. For example, if we do foo[[0,1,4]] = [1,2], we’ll get a “shape mismatch” error.

But if we do foo[[0,1,4]] = 77, it gets assigned to each of those indices.

foo[[0,1,4]] = 77
## [77 77 30 40 77]

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