avatarAlina Zhang

Summary

This article discusses an iterative approach to flatten deeply nested JSON objects in Python, which is safer and more performant than recursive methods.

Abstract

The article "How to Flatten Deeply Nested JSON Objects in Non-Recursive Elegant Python" emphasizes the dangers of using recursive solutions for flattening deeply nested JSON objects in Python, such as stack overflows and slower performance. The author introduces an iterative approach to solve this problem, which involves scanning each element in the JSON file and unpacking just one level if the element is nested. The iterative solution is demonstrated using the chain.from_iterable() and starmap() functions from the itertools library. The article concludes by inviting readers to use the provided source code and share their ideas for optimizing the iterative solution.

Opinions

  • Recursive solutions for flattening JSON objects can lead to stack overflows and slower performance.
  • Iterative solutions are safer and more performant for flattening deeply nested JSON objects.
  • The iterative approach involves scanning each element in the JSON file and unpacking just one level if the element is nested.
  • The iterative solution is demonstrated using the chain.from_iterable() and starmap() functions from the itertools library.
  • The article encourages readers to use the provided source code and share their ideas for optimizing the iterative solution.
  • The author uses the metaphor of "bringing all nested matryoshka dolls outside for some fresh air iteratively" to explain the iterative approach.
  • The article concludes by inviting readers to try out an AI service that offers the same performance and functions as ChatGPT Plus (GPT-4) but at a more cost-effective price.

How to Flatten Deeply Nested JSON Objects in Non-Recursive Elegant Python

It is dangerous to flatten deeply nested JSON objects with a recursive python solution. Because the python interpreter limits the depth of stack to avoid infinite recursions which could result in stack overflows. And from performance standpoint, recursion is usually slower than an iterative solution.

Deeply Nested “JSON”. Photo credit to wikipedia.

The purpose of this article is to share an iterative approach for flattening deeply nested JSON objects with python source code and examples provided, which is similar to bring all nested matryoshka dolls outside for some fresh air iteratively.

Photo credit to MagiDeal

Traditional recursive python solution for flattening JSON

The following function is an example of flattening JSON recursively. Code at line 16 and 20 calls function “flatten” to keep unpacking items in JSON object until all values are atomic elements (no dictionary or list).

In the following example, “pets” is 2-level nested. The value for key “dolphin” is a list of dictionary.

Loading the flattened results to a pandas data frame, we can get

Using an iterative approach to flatten deeply nested JSON

The function “flatten_json_iterative_solution” solved the nested JSON problem with an iterative approach. The idea is that we scan each element in the JSON file and unpack just one level if the element is nested. We keep iterating until all values are atomic elements (no dictionary or list).

  • chain.from_iterable() at line 27 is used to get chained inputs from a single iterable argument, i.e. chain.from_iterable([‘ABC’, ‘DEF’]) → A B C D E F
  • starmap() at line 27 is used to make an iterator that computes the function “unpack” using arguments obtained from dictionary.items()

Using the new iterative solution “flatten_json_iterative_solution” for example “pets”:

Unit test with another example “pets2” which is 3-level nested at key “dolphin”. A list [{“bird”:”bluejay”},{“fish”:”dolphin”}] is packed as the value of key “tiger”.

In this article, I describe an iterative solution for flattening deeply nested JSON object. Feel free to use the source code in practice. Please send me a message if you have any idea of optimizing the iterative solution.

Congratulations! You just finished reading an article and emancipated some matryoshka dolls at the meaning time.

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Artificial Intelligence
Python
Data Science
Machine Learning
JavaScript
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