Lecture 10: Arrays and lists

Tips for learning a new language (e.g. Python)

• Start with something (small) you know how to do in R
• Figure out the translation to Python
  • Gives you some concrete examples to further explore
  • Some questions to ask:
    • What kinds of objects are available?
    • How is data stored?
    • How does iteration work? etc.
• Investigate similarities and differences

Recap: vectors in R

x <- c(1, 2, 3)

sqrt(x)

[1] 1.000000 1.414214 1.732051

x + 1

[1] 2 3 4

x + c(2, 3, 4)

[1] 3 5 7

• Vectors only contain one type
• Many functions are (or can be) vectorized
• Math often works element-wise

NumPy arrays

import numpy as np

x = np.array([1, 2, 3])

np.sqrt(x)

array([1.        , 1.41421356, 1.73205081])

x + 1

array([2, 3, 4])

x + np.array([2, 3, 4])

array([3, 5, 7])

1-dimensional arrays work like R vectors:

• Only store one type
• Many functions and math can be applied element-wise

Indexing vectors and arrays

x <- c(1, 2, 3)
y <- c(2, 4, 8)
x[1:3]

[1] 1 2 3

x = np.array([1, 2, 3])
y = np.array([2, 4, 8])
x[0:2]

array([1, 2])

• Similarity: Square brackets [ ] used for both R and Python
• Difference: R is 1-indexed, Python is 0-indexed
• Similarity: Indexing can be used to select multiple entries

Indexing vectors and arrays

x <- sample(1:100, 10)
x

 [1] 67 92 10  5 80 17 40 61 76 78

Question: How would I select the entries in x which are < 50?

Indexing vectors and arrays

x <- sample(1:100, 10)
x

Question: How would I select the entries in x which are < 50?

x[x < 50]

[1] 10  5 17 40

Indexing vectors and arrays

x <- sample(1:100, 10)

Question: How would I write this code in Python?

Indexing vectors and arrays

x = np.random.choice(np.arange(1, 101), 10)
x

array([74, 87, 32, 59, 91, 69,  5,  6, 79, 70])

x[x < 50]

array([32,  5,  6])

• Similarity: Using booleans to index works similarly in R and Python
• Difference: np.arange includes the start, but not the end

Indexing vectors and arrays

Indexing doesn’t always behave the same:

x <- c(1, 2, 3)
x[-1]

[1] 2 3

x = np.array([1, 2, 3])
x[-1]

3

Recap: lists in R

Question: How are lists different from vectors in R?

Recap: lists in R

x <- list(c("a", "b"), list(1, 2, c(4, 5)))

Question: How would I select just the vector c(4, 5)?

Recap: lists in R

x <- list(c("a", "b"), list(1, 2, c(4, 5)))

Question: How would I select just the vector c(4, 5)?

x[[2]][[3]]

[1] 4 5

Lists in Python

x = np.array(["a", 0, 1])

• Like vectors in R, arrays can only store one type

Lists in Python

In R:

x = list("a", 0, 1)
x[[1]]

[1] "a"

In Python:

x = ["a", 0, 1]
x[0]

'a'

Lists in Python

In R:

x <- list(c("a", "b"), list(1, 2, c(4, 5)))
x[[2]][[3]]

[1] 4 5

In Python:

x = [np.array(["a", "b"]), [1, 2, np.array([4, 5])]]
x[1]

[1, 2, array([4, 5])]

x[1][2]

array([4, 5])

Lists in Python

What will happen if I run the following R code?

x <- list(0, 1, 2)
x + 1
x * 2

Lists in Python

What will happen if I run the following R code?

x <- list(0, 1, 2)
x + 1

Error in x + 1: non-numeric argument to binary operator

x * 2

Error in x * 2: non-numeric argument to binary operator

Lists in Python

What if I run the code in Python?

x = [0, 1, 2]
x + [1]
x * 2

Lists in Python

What if I run the code in Python?

x = [0, 1, 2]
x + [1]

[0, 1, 2, 1]

x * 2

[0, 1, 2, 0, 1, 2]

• R vectors, and NumPy arrays, are built for math and data
• Python lists are a much more general tool

Class activity

https://sta279-f23.github.io/class_activities/ca_lecture_10.html