To summarize the three options we’ve seen, as well as a streaming ijson-based solution: Package Performance may differ between CPUs, Python versions and library versions.$ /usr/bin/time -f "RAM: %M KB, Elapsed: %E" python with_msgspec.py Make sure to compare modules on a sample of your own data and in your own environment.If you really love speed and don’t mind writing some pandas conversions yourself from time to time, `msgspec` may be the right answer.If working in pandas you may choose pandas.read_json to easily convert json to pandas dataframe.If speed is a concern and you don’t need to work with pandas, you may choose `orjson` or `msgspec`.It may not always be the right choice and the factors mentioned above, will help you choose or switch to a library that best fits your needs. `json` isn’t always the right answer: Most Python tutorials use the `json` module by default.Open Detailed Analysis in Noteable Performance Benchmark Results Python JSON Loads - 4 Faster Alternatives to json.loads in Python | Ojrson Ujson Pandas Msgspec 2 Key Takeaways Import json import orjson import ujson import pandas as pd import msgspec import timeit # Create a VERY LARGE JSON file by repeating the same data x 100000 times very_large_json_data = " [ " + str.
#Python json how to#
Here is the example code of how to use ujson.loads:
It is designed to parse JSON data efficiently and quickly, making it a good choice for situations where you need to process a large volume of JSON data in real-time. Ujson is a fast JSON library for Python that is optimized for speed and uses less memory compared to the built-in json library. Here are a few of the most popular alternatives, along with examples of how to use them: ujson module How to speed up loading and reading json files in pythonĪlternatives to json.loads: There are several alternative JSON parsing libraries for Python that can provide better performance when parsing large amounts of JSON data. However, this strict adherence to the JSON specification also means that the library is not optimized for speed and can be slow when parsing large amounts of JSON data. This means that it provides robust error handling and can handle non-UTF8 encoded characters, ensuring that the resulting Python object is a valid representation of the JSON data. The json module is part of the Python Standard Library and follows the JSON specification strictly. Here is a quick 2 minute overview of why `json.loads` is slow for a large json file and the alternatives you can use based on your speed and performance priorities.
If you are looking for a quick fix, simply replace the json in json.loads to orjson or ujson.īut it may not work for all types of JSON files. json loads too slow? Use orjson to speed up loading large JSON file In this article, we will discuss why json.loads can be slow and explore alternative Python libraries that can be faster when parsing JSON. However, when working with large JSON files, the performance of json loads can be a concern.
It is part of the built-in json library and provides a simple and convenient way to parse JSON data into a Python object. The json.loads function is a popular tool for working with JSON data in Python.
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