Скачать или смотреть Unpack list of dictionaries in Python

In Python, it's common to work with lists of dictionaries, where each dictionary represents a set of key-value pairs. Unpacking such lists can be quite useful when you need to extract specific information from these dictionaries or perform operations on them. In this tutorial, we'll explore how to unpack a list of dictionaries in Python with code examples.
A list of dictionaries is a data structure where each dictionary in the list may have different keys and values. You may encounter such structures when dealing with data from sources like JSON, databases, or API responses. Unpacking these dictionaries allows you to access and work with the data more efficiently.
The simplest way to unpack a list of dictionaries is by using a for loop. Here's an example:
This code iterates over each dictionary in the data list and extracts the values associated with the "name" and "age" keys.
List comprehensions provide a more concise way to unpack a list of dictionaries and collect the results in a new list. Here's an example:
In this example, we've used list comprehensions to create separate lists for names and ages.
If you want to extract only specific keys from the dictionaries, you can use dictionary unpacking with the ** operator. Here's an example:
This code unpacks only the "name" and "age" keys from each dictionary.
You can filter dictionaries within the list based on certain conditions. For instance, if you want to extract dictionaries with people older than 30, you can use a list comprehension:
This code creates a new list containing only dictionaries with an "age" greater than 30.
Unpacking a list of dictionaries in Python is a fundamental skill when working with data. Whether you're parsing JSON responses, reading data from a file, or processing database results, these techniques will help you efficiently access and manipulate the data within the dictionaries. The choice between a for loop and list comprehensions depends on your specific use case and coding style preferences.
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