Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Data Science Ipython Notebooks | 25,668 | 7 months ago | 34 | other | Python | |||||
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines. | ||||||||||
Koalas | 3,291 | 1 | 16 | 7 months ago | 47 | October 19, 2021 | 112 | apache-2.0 | Python | |
Koalas: pandas API on Apache Spark | ||||||||||
Arcticdb | 1,071 | 3 | 17 days ago | 35 | December 07, 2023 | 260 | other | C++ | ||
ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem. | ||||||||||
Visualpython | 748 | 5 months ago | 88 | November 18, 2023 | 20 | other | JavaScript | |||
GUI-based Python code generator for data science, extension to Jupyter Lab, Jupyter Notebook and Google Colab. | ||||||||||
Delta Sharing | 654 | 7 | 3 months ago | 33 | December 02, 2023 | 74 | apache-2.0 | Scala | ||
An open protocol for secure data sharing | ||||||||||
Sdc | 645 | 6 months ago | 54 | bsd-2-clause | Python | |||||
Numba extension for compiling Pandas data frames, Intel® Scalable Dataframe Compiler | ||||||||||
Eland | 588 | 3 | 3 months ago | 30 | November 22, 2023 | 88 | apache-2.0 | Python | ||
Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch | ||||||||||
Ustore | 435 | 8 months ago | 57 | September 01, 2023 | 29 | apache-2.0 | C++ | |||
Multi-Modal Database replacing MongoDB, Neo4J, and Elastic with 1 faster ACID solution, with NetworkX and Pandas interfaces, and bindings for C 99, C++ 17, Python 3, Java, GoLang 🗄️ | ||||||||||
Notebook | 140 | 2 years ago | 4 | mit | C++ | |||||
✍ 记录一路走来学习的计算机专业知识 ,力求构建 AI & CS & SE 知识体系 | ||||||||||
Zhihu_bigdata | 138 | 7 years ago | HTML | |||||||
使用scrapy和pandas完成对知乎300w用户的数据分析。首先使用scrapy爬取知乎网的300w,用户资料,最后使用pandas对数据进行过滤,找出想要的知乎大牛,并用图表的形式可视化。 |