Question

Cities: Make a dictionary called cities. Use the names of three cities as keys in your dictionary. Create a dictionary of information about each city and include the country that the city is in, its approximate population, and one fact about that city. The keys for each city's dictionary should be something like country, population, and fact. Print the name of each city and all of the information you have stored about it.

Short answer

Create a nested dictionary, assign city details, and print each with info using a loop.

Step-by-step solution

Create the Dictionary Structure

First, create a dictionary called `cities`. This dictionary will hold three key-value pairs, where each key is the name of a city and the value is another dictionary containing information about the city.

Populate City Information

Inside the `cities` dictionary, for each city, create another dictionary with keys such as `country`, `population`, and `fact`. Assign appropriate values to these keys. For example: `{'country': 'France', 'population': 2148327, 'fact': "Notable for its art, fashion, and culture."}` for Paris.

Assign City Data

Assign the populated dictionaries to the corresponding city keys. For illustration, let's assume our cities are Paris, Cairo, and Tokyo: - Paris: `{'country': 'France', 'population': 2148327, 'fact': "Notable for its art, fashion, and culture."}` - Cairo: `{'country': 'Egypt', 'population': 20000000, 'fact': "Home to the Pyramids of Giza."}` - Tokyo: `{'country': 'Japan', 'population': 13929286, 'fact': "World's most populous metropolitan area."}`.

Print Each City and its Information

Iterate through the `cities` dictionary. For each city, print its name followed by its stored information. Use a for loop to access each city and its dictionary, and format the output to display neatly.

Key concepts

Data Structures

In Python, **data structures** allow you to organize and store data efficiently so that it can be accessed and modified easily. Among these, dictionaries are one of the most versatile structures. A dictionary consists of keys and values. Each key is linked to a value, and together they form a key-value pair.

A dictionary is particularly useful when you need to pair unique identifiers with related information. For instance, in a dictionary named `cities`, each city's name serves as a unique key, and the value might include another dictionary with detailed information about the city. This can include the country it's located in, its population, and unique facts about the city.

Using dictionaries for such hierarchical data follows a nested structure, where a dictionary contains other dictionaries as values. This nesting is particularly useful because it allows grouping of related data under a single key, thereby ensuring that you can manage and retrieve information in a structured manner.

To create a dictionary in Python, you use curly braces `{}` and define key-value pairs within them. Here's how you can define a dictionary with information about cities:

• Paris: `{'country': 'France', 'population': 2148327, 'fact': "Known for art, fashion, and culture."}`
• Cairo: `{'country': 'Egypt', 'population': 20000000, 'fact': "Home to the Pyramids of Giza."}`
• Tokyo: `{'country': 'Japan', 'population': 13929286, 'fact': "The most populous metropolitan area."}`

Dictionary Comprehension

A powerful feature in Python called **dictionary comprehension** allows you to create new dictionaries from existing ones in a concise way. It works similarly to list comprehensions, providing a compact and expressive way to create dictionaries by specifying a kind of loop structure in a single line.

Using dictionary comprehension can make your code more readable and efficient, especially when dealing with transformations or filtering of data. For example, if you have a dictionary of cities with their population and want to extract cities with populations over a certain threshold, dictionary comprehension can be handy.

Suppose you have a dictionary called `cities` with nested dictionaries containing city information. You could use a dictionary comprehension to pull out only those cities with populations greater than 10 million like this:

• `large_cities = {city: info for city, info in cities.items() if info['population'] > 10000000}`

This would result in a dictionary that includes only 'Cairo' and 'Tokyo' based on the population data provided.

Overall, dictionary comprehension is a versatile tool when you need to efficiently create dictionaries without the need for lengthy loops, making your Python programming more Pythonic and efficient.

Python Programming

**Python programming** is renowned for its ease of use, readability, and versatility, making it a preferred choice for beginners and experts alike. Its clarity allows programmers to express concepts without writing excessive boilerplate code, and it's particularly advantageous when dealing with data structures like dictionaries.

As with any programming language, understanding how to utilize its features effectively can greatly enhance your coding efficiency. When working with dictionary data structures in Python, leveraging Python's built-in functions, like `items()`, `keys()`, and `values()`, can simplify data manipulation.

For instance, iterating over the `cities` dictionary to print out all its contents can be done with a simple `for` loop:

• `for city, info in cities.items():`
• `print(f"City: {city}")`
• `print(f"Country: {info['country']}, Population: {info['population']}, Fact: {info['fact']}")`

This demonstrates how Python deftly combines readability with functionality, allowing developers to write clear, understandable code. Such simplicity of expression in Python makes it especially beneficial for newcomers seeking to grasp complex concepts in programming.