marshmallow
ultrajson
Our great sponsors
marshmallow | ultrajson | |
---|---|---|
11 | 3 | |
6,888 | 4,244 | |
0.8% | 0.7% | |
8.7 | 7.0 | |
1 day ago | 18 days ago | |
Python | C | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
marshmallow
-
Help making draggable items for Flask app.
Somehow get a serializer going for your database models. I used marshmallow and flask-marshmallow
-
Faster time-to-market with API-first
Uses a robust data validation library: validating payloads is a complex business. Your data validation library must handle optional and required properties, string formats like ISO dates and UUIDs (both dates and UUIDs are string types in OpenAPI), and strict vs loose type validation (should a string pass as an integer if it can be casted?). Also, in the case of Python, you need to make sure 1 and 0 don’t pass for True and False when it comes to boolean properties. In my experience, the best data validation libraries in the Python ecosystem are pydantic and marshmallow. From the above-mentioned libraries, flasgger and flask-smorest work with marshmallow.
-
What's best library for swagger + flask?
I also came across things like Marsmallow and Blueprints, but don't know what these are, still reading about this as I write.
-
pydantic VS marshmallow - a user suggested alternative
2 projects | 21 Sep 2022
Pydantic is a data validation library, marshmallow is a data validation library. None of the other libraries in the list of pydantic alternatives is a data validation library.
-
Yet another object serialization framework!
I have been working on a package that is very similar in concept to marshmallow (https://marshmallow.readthedocs.io), but which adds a versioning mechanism to track changes in object structure across time, allowing you to migrate objects between different versions.
-
How to implement conditional model
Either using meta programming: https://github.com/marshmallow-code/marshmallow/issues/585
-
Should I use SQLAlchemy for a side project?
You might be surprised how much I agree - I recently opened an issue there hoping to discuss something like this (still awaiting response). https://github.com/marshmallow-code/marshmallow/issues/2000
-
The Pocket Guide To API Request Validation You Wish You Had Earlier
Marshmallow
-
Project Althaia - looking for performance/accuracy feedback on my shallow fork of marshmallow
I created a shallow fork of everyone's favourite marshmallow, to work around some performance issues while dumping data. The performance gain I measured is around 45%, but since it's a bad idea to rely on one's own testing, I was hoping that there are some folks here who use marshmallow in their projects, and who would be willing to try it out. Doubly so if your project has some unit tests in it, to confirm that nothing is broken due to my patches.
-
What's the fastest way to parse JSON to output?
I was looking at https://github.com/marshmallow-code/marshmallow That's a nice library to use to parsing?
ultrajson
-
Processing JSON 2.5x faster than simdjson with msgspec
ujson
-
Benchmarking Python JSON serializers - json vs ujson vs orjson
For most cases, you would want to go with python’s standard json library which removes dependencies on other libraries. On other hand you could try out ujsonwhich is simple replacement for python’s json library. If you want more speed and also want dataclass, datetime, numpy, and UUID instances and you are ready to deal with more complex code, then you can try your hands on orjson
-
The fastest tool for querying large JSON files is written in Python (benchmark)
I asked about this on the Github issue regarding these benchmarks as well.
I'm curious as to why libraries like ultrajson[0] and orjson[1] weren't explored. They aren't command line tools, but neither is pandas right? Is it perhaps because the code required to implement the challenges is large enough that they are considered too inconvenient to use through the same way pandas was used (ie, `python -c "..."`)?
What are some alternatives?
Fast JSON schema for Python - Fast JSON schema validator for Python.
greenpass-covid19-qrcode-decoder - An easy tool for decoding Green Pass Covid-19 QrCode
cattrs - Composable custom class converters for attrs.
serpy - ridiculously fast object serialization
python-rapidjson - Python wrapper around rapidjson
WTForms - A flexible forms validation and rendering library for Python.
PyLD - JSON-LD processor written in Python
jsonschema - JSON Schema validation library
pysimdjson - Python bindings for the simdjson project.
Trafaret - Ultimate transformation library that supports validation, contexts and aiohttp.