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.
highs = np.array([13.6 , 14.387, 14.585, 15.164])
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.
Function 
Description 


Add all elements together 

Multiply all elements together 

Test whether all elements are true values (nonzero numbers are true) 

Test whether any elements are true values (nonzero numbers are true) 

Count the number of nonzero elements 

Calculate the mean (sum divided by number of elements) 

The minimum over the array 

The maximum over the array 
Each of these functions takes an array as an argument and returns an array of values.
Function 
Description 


Difference between adjacent elements 

Round each number to the nearest integer (whole number) 

Absolute value of each number (remove minus signs 

A cumulative product: for each element, multiply all elements so far 

A cumulative sum: for each element, add all elements so far 

Exponentiate each element 

Take the natural logarithm of each element 

Take the square root of each element 

Sort the elements 
Each of these functions takes an array of strings and returns an array.
Function 
Description 


Lowercase each element 

Uppercase each element 

Remove spaces at the beginning or end of each element 

Whether each element is only letters (no numbers or symbols) 

Whether each element is only numeric (no letters) 
Each of these functions takes both an array of strings and a search string; each returns an array.
Function 
Description 


Count the number of times a search string appears among the elements of an array 

The position within each element that a search string is found first 

The position within each element that a search string is found last 

Whether each element starts with the search string 
Note
This page has content from the Arrays notebook of an older version of the UC Berkeley data science course. See the Berkeley course section of the license file.