Course Content
Python Data Structures
Python Data Structures
Dictionary Comprehensions
Dictionary comprehensions offer a succinct way to create dictionaries in Python. They are built in the same way as list comprehensions but with some exceptions.
Basic Dictionary Comprehension
At its heart, a basic dictionary comprehension lets you construct a new dictionary by applying an expression to each key-value pair in an iterable variable.
Syntax:
What it does: For every item
in iterable
, it evaluates both key_expression
and value_expression
to create a new key-value pair in the dictionary.
Note
In contrast to lists, dictionaries require curly braces instead of square brackets. Additionally, in a dictionary comprehension, you specify both a key and a value separated by a colon, as in
key:value
, rather than just a single value.
Here, for each number x
in the range 0
through 4
, we create a key-value pair where the key is the number and the value is its square.
It's equivalent to:
squared_dict = {} for x in (0, 1, 2, 3, 4): squared_dict[x] = x * x print(squared_dict)
Dictionary Comprehension with Condition
This variation allows you to introduce a condition to your dictionary comprehension, functioning as a filter. Only items that meet the condition are processed and added to the new dictionary.
Syntax: {key_expression: value_expression for item in iterable if condition}
What it does: For every item
in iterable
, if the condition is True
, it evaluates both key_expression
and value_expression
and adds the resulting key-value pair to the dictionary.
In this instance, we only construct key-value pairs for numbers from the range 0
through 5
if they're even. The value represents the square of the key.
This example is equivalent to:
even_dict = {} for x in (0, 1, 2, 3, 4): if x % 2 == 0: even_dict[x] = x * x print(even_dict)
Dictionary comprehensions, like list comprehentions, are a more efficient and "Pythonic" way to craft dictionaries, often proving quicker in execution when compared to traditional loop methods.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Everything was clear?
Dictionary Comprehensions
Dictionary comprehensions offer a succinct way to create dictionaries in Python. They are built in the same way as list comprehensions but with some exceptions.
Basic Dictionary Comprehension
At its heart, a basic dictionary comprehension lets you construct a new dictionary by applying an expression to each key-value pair in an iterable variable.
Syntax:
What it does: For every item
in iterable
, it evaluates both key_expression
and value_expression
to create a new key-value pair in the dictionary.
Note
In contrast to lists, dictionaries require curly braces instead of square brackets. Additionally, in a dictionary comprehension, you specify both a key and a value separated by a colon, as in
key:value
, rather than just a single value.
Here, for each number x
in the range 0
through 4
, we create a key-value pair where the key is the number and the value is its square.
It's equivalent to:
squared_dict = {} for x in (0, 1, 2, 3, 4): squared_dict[x] = x * x print(squared_dict)
Dictionary Comprehension with Condition
This variation allows you to introduce a condition to your dictionary comprehension, functioning as a filter. Only items that meet the condition are processed and added to the new dictionary.
Syntax: {key_expression: value_expression for item in iterable if condition}
What it does: For every item
in iterable
, if the condition is True
, it evaluates both key_expression
and value_expression
and adds the resulting key-value pair to the dictionary.
In this instance, we only construct key-value pairs for numbers from the range 0
through 5
if they're even. The value represents the square of the key.
This example is equivalent to:
even_dict = {} for x in (0, 1, 2, 3, 4): if x % 2 == 0: even_dict[x] = x * x print(even_dict)
Dictionary comprehensions, like list comprehentions, are a more efficient and "Pythonic" way to craft dictionaries, often proving quicker in execution when compared to traditional loop methods.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Everything was clear?
Dictionary Comprehensions
Dictionary comprehensions offer a succinct way to create dictionaries in Python. They are built in the same way as list comprehensions but with some exceptions.
Basic Dictionary Comprehension
At its heart, a basic dictionary comprehension lets you construct a new dictionary by applying an expression to each key-value pair in an iterable variable.
Syntax:
What it does: For every item
in iterable
, it evaluates both key_expression
and value_expression
to create a new key-value pair in the dictionary.
Note
In contrast to lists, dictionaries require curly braces instead of square brackets. Additionally, in a dictionary comprehension, you specify both a key and a value separated by a colon, as in
key:value
, rather than just a single value.
Here, for each number x
in the range 0
through 4
, we create a key-value pair where the key is the number and the value is its square.
It's equivalent to:
squared_dict = {} for x in (0, 1, 2, 3, 4): squared_dict[x] = x * x print(squared_dict)
Dictionary Comprehension with Condition
This variation allows you to introduce a condition to your dictionary comprehension, functioning as a filter. Only items that meet the condition are processed and added to the new dictionary.
Syntax: {key_expression: value_expression for item in iterable if condition}
What it does: For every item
in iterable
, if the condition is True
, it evaluates both key_expression
and value_expression
and adds the resulting key-value pair to the dictionary.
In this instance, we only construct key-value pairs for numbers from the range 0
through 5
if they're even. The value represents the square of the key.
This example is equivalent to:
even_dict = {} for x in (0, 1, 2, 3, 4): if x % 2 == 0: even_dict[x] = x * x print(even_dict)
Dictionary comprehensions, like list comprehentions, are a more efficient and "Pythonic" way to craft dictionaries, often proving quicker in execution when compared to traditional loop methods.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.
Everything was clear?
Dictionary comprehensions offer a succinct way to create dictionaries in Python. They are built in the same way as list comprehensions but with some exceptions.
Basic Dictionary Comprehension
At its heart, a basic dictionary comprehension lets you construct a new dictionary by applying an expression to each key-value pair in an iterable variable.
Syntax:
What it does: For every item
in iterable
, it evaluates both key_expression
and value_expression
to create a new key-value pair in the dictionary.
Note
In contrast to lists, dictionaries require curly braces instead of square brackets. Additionally, in a dictionary comprehension, you specify both a key and a value separated by a colon, as in
key:value
, rather than just a single value.
Here, for each number x
in the range 0
through 4
, we create a key-value pair where the key is the number and the value is its square.
It's equivalent to:
squared_dict = {} for x in (0, 1, 2, 3, 4): squared_dict[x] = x * x print(squared_dict)
Dictionary Comprehension with Condition
This variation allows you to introduce a condition to your dictionary comprehension, functioning as a filter. Only items that meet the condition are processed and added to the new dictionary.
Syntax: {key_expression: value_expression for item in iterable if condition}
What it does: For every item
in iterable
, if the condition is True
, it evaluates both key_expression
and value_expression
and adds the resulting key-value pair to the dictionary.
In this instance, we only construct key-value pairs for numbers from the range 0
through 5
if they're even. The value represents the square of the key.
This example is equivalent to:
even_dict = {} for x in (0, 1, 2, 3, 4): if x % 2 == 0: even_dict[x] = x * x print(even_dict)
Dictionary comprehensions, like list comprehentions, are a more efficient and "Pythonic" way to craft dictionaries, often proving quicker in execution when compared to traditional loop methods.
Task
Given a dictionary with cities and their respective populations, use dictionary comprehension to create a new dictionary that only contains cities with populations greater than a specified number.
Note
The expression
for city, population in cities_popul.items()
iterates over each key-value pair from the dictionary. During each loop,city
holds the name of a city from the dictionary andpopulation
captures its associated population value.