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Python Data Structures
course content

Course Content

Python Data Structures

Python Data Structures

1. List
Creating a ListList IndexingNested ListList LengthModifying a ListThe append() MethodThe insert() MethodDeleting ElementsThe remove() MethodList ComprehensionsList Comprehansions with Conditions
2. Dictionary
Creating a DictionaryAccessing Dictionary ValuesAccessing Dictionary KeysAdding ItemsChallenge: Expand the DictionaryThe del KeywordThe pop() MethodThe popitem() MethodThe clear() MethodDictionary ComprehensionsDictionary Comprehension with Condition
3. Tuple
Creating a Tuple Accessing Elements in a TupleConcatenating TuplesDeleting a TupleUpdating a TupleAdding Items to a TupleCounting Elements in a TupleFinding Elements in a Tuple
4. Set
Creating a SetThe add() MethodThe update() MethodAccessing the Elements of a SetThe remove() and discard() MethodsThe clear() Method

bookList Comprehansions with Conditions

You can also use conditions:

For example, suppose you want to create a list of cities located in Japan.


              
1234567891011121314

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = []
for city in travel_wishlist:
    if city[1] == "Japan":
        japanese_cities.append(city[0])

print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy

Which is equivalent to:


              
12345678910

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = [city[0] for city in travel_wishlist if city[1] == "Japan"]
print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy
Task
test

Swipe to show code editor

Create a new list that contains the names of the cities from your travel_wishlist, but only if the estimated cost is less than $2500. Use a list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 11
toggle bottom row

bookList Comprehansions with Conditions

You can also use conditions:

For example, suppose you want to create a list of cities located in Japan.


              
1234567891011121314

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = []
for city in travel_wishlist:
    if city[1] == "Japan":
        japanese_cities.append(city[0])

print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy

Which is equivalent to:


              
12345678910

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = [city[0] for city in travel_wishlist if city[1] == "Japan"]
print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy
Task
test

Swipe to show code editor

Create a new list that contains the names of the cities from your travel_wishlist, but only if the estimated cost is less than $2500. Use a list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 1. Chapter 11
toggle bottom row

bookList Comprehansions with Conditions

You can also use conditions:

For example, suppose you want to create a list of cities located in Japan.


              
1234567891011121314

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = []
for city in travel_wishlist:
    if city[1] == "Japan":
        japanese_cities.append(city[0])

print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy

Which is equivalent to:


              
12345678910

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = [city[0] for city in travel_wishlist if city[1] == "Japan"]
print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy
Task
test

Swipe to show code editor

Create a new list that contains the names of the cities from your travel_wishlist, but only if the estimated cost is less than $2500. Use a list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

You can also use conditions:

For example, suppose you want to create a list of cities located in Japan.


              
1234567891011121314

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = []
for city in travel_wishlist:
    if city[1] == "Japan":
        japanese_cities.append(city[0])

print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy

Which is equivalent to:


              
12345678910

            
travel_wishlist = [
    ["Paris", "France", 2000],
    ["Tokyo", "Japan", 3000],
    ["New York", "USA", 2500],
    ["Kyoto", "Japan", 1500],
    ["Sydney", "Australia", 4000]
]

japanese_cities = [city[0] for city in travel_wishlist if city[1] == "Japan"]
print(japanese_cities)  # Output: ['Tokyo', 'Kyoto']
copy
Task
test

Swipe to show code editor

Create a new list that contains the names of the cities from your travel_wishlist, but only if the estimated cost is less than $2500. Use a list comprehension.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 11
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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