| donnemartin/data-science-ipython-notebooks |
25,668 |
|
0 |
0 |
over 2 years ago |
0 |
|
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. |
| databricks/koalas |
3,291 |
|
1 |
16 |
over 2 years ago |
47 |
October 19, 2021 |
112 |
apache-2.0 |
Python |
| Koalas: pandas API on Apache Spark |
| man-group/ArcticDB |
920 |
|
0 |
3 |
about 2 years ago |
35 |
December 07, 2023 |
260 |
other |
C++ |
| ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem. |
| visualpython/visualpython |
748 |
|
0 |
0 |
over 2 years 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-io/delta-sharing |
654 |
|
0 |
7 |
over 2 years ago |
33 |
December 02, 2023 |
74 |
apache-2.0 |
Scala |
| An open protocol for secure data sharing |
| IntelPython/sdc |
646 |
|
0 |
0 |
about 3 years ago |
0 |
|
54 |
bsd-2-clause |
Python |
| Numba extension for compiling Pandas data frames, Intel® Scalable Dataframe Compiler |
| elastic/eland |
588 |
|
0 |
3 |
about 2 years ago |
30 |
November 22, 2023 |
88 |
apache-2.0 |
Python |
| Python Client and Toolkit for DataFrames, Big Data, Machine Learning and ETL in Elasticsearch |
| unum-cloud/ustore |
435 |
|
0 |
0 |
over 2 years 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 🗄️ |
| wugenqiang/NoteBook |
140 |
|
0 |
0 |
over 3 years ago |
0 |
|
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 |
| yoghurtjia/Zhihu_bigdata |
138 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
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。 |
