Streamlit — A faster way to build and share data apps.
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:zap: A Fast, Extensible Progress Bar for Python and CLI
- progressbar
- progressmeter
- progress-bar
- meter
🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation tools
Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
Fast and Accurate ML in 3 Lines of Code
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
- data-science
- python
- machine-learning
- pycaret
Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy
- json
- python
- rust
- serialization
Lightning ⚡️ fast forecasting with statistical and econometric models.
- time-series
- statistics
- forecasting
- arima
Fast data visualization and GUI tools for scientific / engineering applications
napari: a fast, interactive, multi-dimensional image viewer for python
- napari
- numpy
- visualization
- python
A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
- pandas
- pandas-dataframe
- parallel-computing
- parallelization
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
Python+Numpy+OpenGL: fast, scalable and beautiful scientific visualization
Fast NumPy array functions written in C
- c
- python
- numpy
- fast
A fast xgboost feature selection algorithm
- machine-learning
- machine-learning-algorithms
- feature-selection
- xgboost-algorithm
A fast, forgiving GTFS reader built on pandas DataFrames
身材管控期也想吃麦当劳？这个专家会帮你根据每日消耗搭配麦麦套餐。基于 SP 矩阵理论，打通麦当劳官方 MCP Server 的营养选型与点餐闭环（35 个官方工具 · 14 个 REST 接口 · 零第三方前端依赖）。
Python3 script that transfers files in Google Colab 10-50x faster, using multi-threaded parallel copying between Google Drive locations.
- colab-notebook
- multithreading
- parallel-computing
- copy
Blazingly fast data comparison tool for Python, powered by Rust. Compare massive CSV/Parquet datasets instantly.
Store,Version and Run jupyter notebook via ReST api