How to Convert a Nested Dictionary in a List to a DataFrame in Python

A comprehensive guide on transforming nested dictionaries in Python's DataFrame using `json_normalize` and lambda functions for effective data manipulation. --- This video is based on the question https://stackoverflow.com/q/66614072/ asked by the user 'Brandon Wong' ( https://stackoverflow.com/u/15388518/ ) and on the answer https://stackoverflow.com/a/66614458/ provided by the user 'Divyessh' ( https://stackoverflow.com/u/13810872/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: nested dictionary in list to dataframe python Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Unpacking Nested Dictionaries in Python: Transforming JSON to DataFrame When working with APIs in Python, you often encounter data that returns in a complex structure, such as nested dictionaries and lists. This can lead to a challenge when you want to convert your data into a format that is easy to work with, like a pandas DataFrame. In this guide, we will unravel how to convert a nested dictionary in a list to a DataFrame using the json_normalize method from the pandas library. The Challenge: Understanding Nested Data Let's say you have a JSON response from an API that looks something like this: [[See Video to Reveal this Text or Code Snippet]] You need to extract stock_data, which is itself a list nested within each item and reshape it into a DataFrame that presents each attribute (like description, industry, ticker) in a more accessible form. Step 1: Initial Data Import To start, retrieve the raw data from your API using the requests library. Here's how you can do it: [[See Video to Reveal this Text or Code Snippet]] Step 2: Using json_normalize As the next step, we use the json_normalize method which is designed to flatten semi-structured data into a flat table. Here's the code: [[See Video to Reveal this Text or Code Snippet]] At this point, your DataFrame will include a column stock_data, but it will contain lists instead of flattened attributes. Step 3: Extracting Nested Values To transform the nested structures into individual columns, we need to use the powerful apply function combined with lambda functions. Here’s how you can achieve this: [[See Video to Reveal this Text or Code Snippet]] In the code above: Each line pulls out the respective attribute from the nested dictionary within the stock_data list. We access the first (and only) item in the list since our JSON seems to suggest that stock_data always contains only one item. Step 4: Cleaning Up the DataFrame Now that we have the desired columns, we can drop the original stock_data column to tidy up our DataFrame: [[See Video to Reveal this Text or Code Snippet]] Result: A Clean DataFrame After completing these steps, your DataFrame will now have the following structure: descriptionindustrytickerISINupdate_datetime'zzz''C'xxx123timeConclusion Working with nested dictionaries in Python can feel overwhelming at first, but with the right methods such as json_normalize and effective use of lambda functions, you can efficiently reshape your data into a usable format. Do not hesitate to dive into the documentation of pandas for more complex manipulations as you grow in your data handling skills! Happy coding!