cysimdjson Fast JSON parsing library for Python, 7-12 times faster than standard Python JSON parser. It is Python bindings for thesimdjsonusingCython. StandardPython JSON parser(json.load()etc.) is relatively s
To migrate from the standard library, the largest difference is that orjson.dumps returns bytes and json.dumps returns a str.Users with dict objects using non-str keys should specify option=orjson.OPT_NON_STR_KEYS.sort_keys is replaced by option=orjson.OPT_SORT_KEYS....
orjson Fast Python JSON library. ormsgpack Fast Python msgpack library. polars Fast multi-threaded DataFrame library in Rust | Python | Node.js. pycrdt Python bindings for the Rust CRDT implementation Yrs. pydantic-core Core validation logic for pydantic written in Rust. primp The fastest python...
Python Imaging Library(PIL) 已经成为 Python 事实上的图像处理标准库了,这是由于,PIL 功能非常强大,但API却非常简单易用。但是由于PIL仅支持到 Python 2.7,再加上年久失修,于是一群志愿者在 PIL 的基础上创建了兼容的版本,名字叫 Pillow,支持最新 Python 3.x,又加入了许多新特性,因此,我们可以跳过 PIL,直接...
LibraryCore FeaturesBest Used For Pandas DataFrame operations, data analysis Tabular data processing NumPy Array operations, mathematical functions Scientific computing Dask Parallel processing Large dataset handling Polars Fast DataFrame operations High performance analytics Vaex Out-of-memory processing Big data...
A Python Echarts Plotting Library Superset 类型:开源的 企业级 轻量BI工具 GitHub star :24937 功能: 创建和分享可视化面板 有丰富的可视化方法来分析数据,且具有灵活的扩展能力 具有可扩展的、高粒度的安全模型,可以用复杂规则来控制访问权限。目前支持主要的认证提供商:DB、OpenID、LDAP、OAuth、和Flask App...
sdispater/tomlkit: Style-preserving TOML library for Python (https://github.com/sdispater/tomlkit) samuelcolvin/rtoml: A fast TOML library for python implemented in rust. (https://github.com/samuelcolvin/rtoml) toml-rs/toml-rs: A TOML encoding/decoding library for Rust (https://github.co...
Understand how to develop, validate, and deploy your Python code projects to Azure Functions using the Python library for Azure Functions.
Understand how to develop, validate, and deploy your Python code projects to Azure Functions using the Python library for Azure Functions.
Instead, you can use Python’s json library to handle the data incrementally. For instance, using json.load() to process large files piece by piece: with open('large_data.json', 'r') as file: for line in file: data = json.loads(line) # Process the data chunk by chunk Alternatively...