Lists and dictionaries

Overview

Questions

• What is the difference between a list and a dictionary?
• Why do we use a list or dictionary instead of lots of separate variables?
• When is one data structure a better choice than another?
• How do I get a value out of a data structure?
• Can I get multiple values out of a data structure?

Objectives

• Understand why data structures are useful for storing multiple values.
• Create, inspect, index, and modify data structures in Python.
• Understand the difference between mutable and immutable objects.
• Understand that data structures can be nested to suit our storage needs.

Python lists

---

We create a list by putting values inside square brackets and separating the values with commas:

PYTHON

odds = [1, 3, 5, 7]
print('odds are:', odds)

OUTPUT

odds are: [1, 3, 5, 7]

We can access elements of a list using indices – numbered positions of elements in the list. These positions are numbered starting at 0, so the first element has an index of 0.

PYTHON

print('first element:', odds[0])
print('last element:', odds[3])
print('"-1" element:', odds[-1])

OUTPUT

first element: 1
last element: 7
"-1" element: 7

Yes, we can use negative numbers as indices in Python. When we do so, the index -1 gives us the last element in the list, -2 the second to last, and so on. Because of this, odds[3] and odds[-1] point to the same element here.

There is one important difference between lists and strings: we can change the values in a list, but we cannot change individual characters in a string. For example:

PYTHON

names = ['Curie', 'Darwing', 'Turing']  # typo in Darwin's name
print('names is originally:', names)
names[1] = 'Darwin'  # correct the name
print('final value of names:', names)

OUTPUT

names is originally: ['Curie', 'Darwing', 'Turing']
final value of names: ['Curie', 'Darwin', 'Turing']

Callout

Ch-Ch-Ch-Ch-Changes

Mutable data (like lists and arrays) can be changed after creation, while immutable data (like strings and numbers) cannot be modified, only replaced. Modifying mutable objects in place can lead to unexpected behaviour if multiple variables reference the same data. To avoid this, you can create a copy so changes do not affect the original. In-place changes are more efficient but can make code harder to understand, so there is a trade-off between clarity and performance.

PYTHON

mild_salsa = ['peppers', 'onions', 'cilantro']
hot_salsa = mild_salsa        # <-- mild_salsa and hot_salsa point to the *same* list data in memory
hot_salsa[0] = 'hot peppers'
print('Ingredients in mild salsa:', mild_salsa)
print('Ingredients in hot salsa:', hot_salsa)

OUTPUT

Ingredients in mild salsa: ['hot peppers', 'onions', 'cilantro']
Ingredients in hot salsa: ['hot peppers', 'onions', 'cilantro']

If you want variables with mutable values to be independent, you must make a copy of the value when you assign it.

PYTHON

mild_salsa = ['peppers', 'onions', 'cilantro']
hot_salsa = mild_salsa.copy()        # <-- hot_salsa is now a copy of the original
hot_salsa[0] = 'hot peppers'
print('Ingredients in mild salsa:', mild_salsa)
print('Ingredients in hot salsa:', hot_salsa)

OUTPUT

Ingredients in mild salsa: ['peppers', 'onions', 'cilantro']
Ingredients in hot salsa: ['hot peppers','onions', 'cilantro']

Callout

Nested Lists

Since a list can contain any Python variables, it can even contain other lists.

For example, you could represent the products on the shelves of a small grocery shop as a nested list called veg:

To store the contents of the shelf in a nested list, you write it this way:

PYTHON

veg = [['lettuce', 'lettuce', 'peppers', 'zucchini'],
     ['lettuce', 'lettuce', 'peppers', 'zucchini'],
     ['lettuce', 'cilantro', 'peppers', 'zucchini']]

Here are some visual examples of how indexing a list of lists veg works. First, you can reference each row on the shelf as a separate list. For example, veg[2] represents the bottom row, which is a list of the baskets in that row.

Index operations using the image would work like this:

PYTHON

print(veg[2])

OUTPUT

['lettuce', 'cilantro', 'peppers', 'zucchini']

PYTHON

print(veg[0])

OUTPUT

['lettuce', 'lettuce', 'peppers', 'zucchini']

To reference a specific basket on a specific shelf, you use two indexes. The first index represents the row (from top to bottom) and the second index represents the specific basket (from left to right).

PYTHON

print(veg[0][0])

OUTPUT

'lettuce'

PYTHON

print(veg[1][2])

OUTPUT

'peppers'

Callout

Heterogeneous Lists

Lists in Python can contain elements of different types. Example:

PYTHON

sample_ages = [10, 12.5, 'Unknown']

There are many ways to change the contents of lists besides assigning new values to individual elements:

Adding to list

To append an item to a list

PYTHON

odds.append(11)
print('odds after adding a value:', odds)

Removing elements

PYTHON

removed_element = odds.pop(0)
print('odds after removing the first element:', odds)
print('removed_element:', removed_element)

List slicing

If you want to take a slice from the beginning of a sequence, you can omit the first index in the range:

PYTHON

date = 'Monday 4 January 2016'
day = date[0:6]
print('Using 0 to begin range:', day)
day = date[:6]
print('Omitting beginning index:', day)

OUTPUT

Using 0 to begin range: Monday
Omitting beginning index: Monday

And similarly, you can omit the ending index in the range to take a slice to the very end of the sequence:

PYTHON

months = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
sond = months[8:12]
print('With known last position:', sond)
sond = months[8:len(months)]
print('Using len() to get last entry:', sond)
sond = months[8:]
print('Omitting ending index:', sond)

OUTPUT

With known last position: ['sep', 'oct', 'nov', 'dec']
Using len() to get last entry: ['sep', 'oct', 'nov', 'dec']
Omitting ending index: ['sep', 'oct', 'nov', 'dec']

Challenge

Slicing From the End

Use slicing to access only the last four characters of a string or entries of a list.

PYTHON

string_for_slicing = 'Observation date: 02-Feb-2013'
list_for_slicing = [['fluorine', 'F'],
                    ['chlorine', 'Cl'],
                    ['bromine', 'Br'],
                    ['iodine', 'I'],
                    ['astatine', 'At']]

OUTPUT

'2013'
[['chlorine', 'Cl'], ['bromine', 'Br'], ['iodine', 'I'], ['astatine', 'At']]

Would your solution work regardless of whether you knew beforehand the length of the string or list (e.g. if you wanted to apply the solution to a set of lists of different lengths)? If not, try to change your approach to make it more robust.

Hint: Remember that indices can be negative as well as positive

Show me the solution

Use negative indices to count elements from the end of a container (such as list or string):

PYTHON

string_for_slicing[-4:]
list_for_slicing[-4:]

Challenge

Non-Continuous Slices

So far we’ve seen how to use slicing to take single blocks of successive entries from a sequence. But what if we want to take a subset of entries that aren’t next to each other in the sequence?

You can achieve this by providing a third argument to the range within the brackets, called the step size. The example below shows how you can take every third entry in a list:

PYTHON

primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
subset = primes[0:12:3]
print('subset', subset)

OUTPUT

subset [2, 7, 17, 29]

Notice that the slice taken begins with the first entry in the range, followed by entries taken at equally-spaced intervals (the steps) thereafter. If you wanted to begin the subset with the third entry, you would need to specify that as the starting point of the sliced range:

PYTHON

primes = [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37]
subset = primes[2:12:3]
print('subset', subset)

OUTPUT

subset [5, 13, 23, 37]

Use the step size argument to create a new string that contains only every other character in the string “In an octopus’s garden in the shade”. Start with creating a variable to hold the string:

PYTHON

beatles = "In an octopus's garden in the shade"

What slice of beatles will produce the following output (i.e., the first character, third character, and every other character through the end of the string)?

OUTPUT

I notpssgre ntesae

Show me the solution

To obtain every other character you need to provide a slice with the step size of 2:

PYTHON

beatles[0:35:2]

You can also leave out the beginning and end of the slice to take the whole string and provide only the step argument to go every second element:

PYTHON

beatles[::2]

Dictionaries

---

• Dictionaries store key-value pairs and are accessed using keys rather than numeric positions.
• They are mutable, and keys are often strings or numbers.
• Dictionaries are created using curly braces {}.

Example of dictionary creation:

PYTHON

my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}

Accessing elements:

• Elements in a dictionary are accessed using square brackets [] and keys.

Example of accessing elements:

PYTHON

person_name = my_dict['name']   # Accessing value corresponding to 'name' key

Manipulating dictionaries:

• Dictionaries support various operations like adding, removing, and updating key-value pairs.

Examples of manipulation:

PYTHON

my_dict['gender'] = 'Male'     # Adds 'gender': 'Male' to the dictionary
del my_dict['age']             # Removes the key 'age' and its value
my_dict['city'] = 'Los Angeles' # Updates the value of 'city' key to 'Los Angeles'

Why do we need different data structures?

---

We need different data structures because data does not always come in the same form.

Sometimes we want to store values in a simple ordered collection. A list is good for this. For example, a list works well for a sequence of numbers, names, or measurements where the position of each item matters.

Sometimes we want to store values with labels. A dictionary is good for this. For example, if we want to store a person’s name, age, and job, it is more useful to label each value than to rely on its position.

So, lists and dictionaries are both ways of storing multiple values, but they are designed for different purposes. A list helps us work with order, while a dictionary helps us work with meaningful labels.

We also need to consider how information is accessed when working with data at scale, particularly when thinking about how efficiently we can search for values within different data structures.

Key Points

• [value1, value2, value3, ...] creates a list, (this process does not have to be manual).
• Lists can contain any Python object, including lists (i.e., list of lists).
• Lists are indexed and sliced with square brackets (e.g., list[0] and list[2:9]), in the same way as strings and arrays.
• Dictionaries are indexed with the key (e.g., dictionary[‘first_entry’])
• Some objects are mutable (e.g., lists).
• Some objects are immutable (e.g., strings).
• Different data structures exist because they support different ways of organising and accessing information.