Arrays

There are several kinds of sequences in Python. A list is one. However, the sequence type that we will use most in the class, is the array.

The numpy package, abbreviated np in programs, provides Python programmers with convenient and powerful functions for creating and manipulating arrays.

# Load the numpy package, and call it "np".
import numpy as np

Creating arrays

The array function from the Numpy package creates an array from single values, or sequences of values.

For example, remember my_list?

my_list = [1, 2, 3]

This is a list:

type(my_list)

list

The array function from Numpy can make an array from this list:

my_array = np.array(my_list)
my_array

array([1, 2, 3])

As you can see from the display above, this is an array. We confirm it with type:

type(my_array)

numpy.ndarray

We can also create the list and then the array in one call, like this:

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

array([1, 2, 3])

Here [1, 2, 3] is an expression that returns a list. np.array then operates on the returned list, to create an array.

Arrays often contain numbers, but, like lists, they can also contain strings or other types of values. However, a single array can only contain a single kind of data. (It usually doesn’t make sense to group together unlike data anyway.)

For example,

english_parts_of_speech = np.array(["noun", "pronoun", "verb", "adverb", "adjective", "conjunction", "preposition", "interjection"])
english_parts_of_speech

array(['noun', 'pronoun', 'verb', 'adverb', 'adjective', 'conjunction',
       'preposition', 'interjection'], dtype='<U12')

We have not seen this yet, but Python allows us to spread expressions between round and square brackets across many lines. It knows that the expression has not finished yet because it is waiting for the closing bracket. For example, this cell works in the exactly the same way as the cell above, and may be easier to read:

# An expression between brackets spread across many lines.
english_parts_of_speech = np.array(
    ["noun",
    "pronoun",
    "verb",
    "adverb",
    "adjective",
    "conjunction",
    "preposition",
    "interjection"]
    )
english_parts_of_speech

array(['noun', 'pronoun', 'verb', 'adverb', 'adjective', 'conjunction',
       'preposition', 'interjection'], dtype='<U12')

Below, we collect four different temperatures into a list called temps. These are the estimated average daily high temperatures over all land on Earth (in degrees Celsius) for the decades surrounding 1850, 1900, 1950, and 2000, respectively, expressed as deviations from the average absolute high temperature between 1951 and 1980, which was 14.48 degrees.

If you are interested, you can get more data from this file of daily high temperatures.

baseline_high = 14.48
highs = np.array([baseline_high - 0.880,
                  baseline_high - 0.093,
                  baseline_high + 0.105,
                  baseline_high + 0.684])
highs

array([13.6  , 14.387, 14.585, 15.164])

Calculations with arrays

Arrays can be used in arithmetic expressions to compute over their contents. When an array is combined with a single number, that number is combined with each element of the array. Therefore, we can convert all of these temperatures to Fahrenheit by writing the familiar conversion formula.

(9/5) * highs + 32

array([56.48  , 57.8966, 58.253 , 59.2952])

As we saw for strings, arrays have methods, which are functions that operate on the array values. The mean of a collection of numbers is its average value: the sum divided by the length. Each pair of parentheses in the examples below is part of a call expression; it’s calling a function with no arguments to perform a computation on the array called highs.

# The number of elements in the array
highs.size

4

highs.sum()

57.736000000000004

highs.mean()

14.434000000000001

Functions on Arrays

Numpy provides various useful functions for operating on arrays.

For example, the diff function computes the difference between each adjacent pair of elements in an array. The first element of the diff is the second element minus the first.

np.diff(highs)

array([0.787, 0.198, 0.579])

The full Numpy reference lists these functions exhaustively, but only a small subset are used commonly for data processing applications. These are grouped into different packages within np. Learning this vocabulary is an important part of learning the Python language, so refer back to this list often as you work through examples and problems.

However, you don’t need to memorize these. Use this as a reference.

Each of these functions takes an array as an argument and returns a single value.

Each of these functions takes an array as an argument and returns an array of values.

Each of these functions takes an array of strings and returns an array.

Each of these functions takes both an array of strings and a search string; each returns an array.