![]() ![]() If you don’t want to clutter your inbox but you’d still like to support the site, check out the 5 Ways to Support The Renegade Coder article. We can create a new Python dictionary using only one iterable if we choose to generate either the keys or the values on the fly. Otherwise, help this collection grow by becoming a subscriber today or hopping on the mailing list! In this section, lets use dictionary comprehension to create a Python dictionary from an iterable, say, a list or a tuple. ![]() ![]() For instance, I have a huge list of Python code snippets for you to check out. If you found this article helpful, there’s plenty more where that came from. Pass # Insert logic for handling duplicate keysĪs we can see, there are several ways to map two lists into a dictionary. Name_value_tuples = zip(column_names, column_values) # Convert two lists into a dictionary with a loop For simplicity though, let’s imagine we have two lists: column_names = Īnd, we want to map the names to the values: name_to_value_dict = To ask me to stop using them would be like asking Angus Young to stand up while he plays guitar. This can be really annoying to traverse and maintain, so I sometimes prefer to map the column names to each row of data. I use Python list and dict comprehension heavily these days. In my personal experience, I’ve found that database libraries often return data in a huge matrix or list of lists. That way, we can eliminate traversals and focus on maintaining a single dictionary. If these lists need to be scanned often, it may just make more sense to convert them into a dictionary. Sometimes iterating over two lists at the same time can be confusing and complicated. Otherwise, jump down below! Problem Introduction Video can’t be loaded because JavaScript is disabled: 3 Ways to Map Two Lists to a Dict in Python ()Īs always, I like to include a video which covers all of the content discussed in this article. List and dictionary in Python Programming, which help us to store the data, perform operations and analyze the data as well. ![]()
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