numpy-100-master
numpy 

所属分类:图形图象
开发工具:Python
文件大小:48KB
下载次数:1
上传日期:2020-08-20 14:22:19
上 传 者6927061
说明:  numpy使用的100个道练习题,用来检测巩固知识
(Numpy uses 100 exercises to test and consolidate knowledge)

文件列表:
100_Numpy_exercises.ipynb (31003, 2020-07-01)
100_Numpy_exercises.md (10493, 2020-07-01)
100_Numpy_exercises_with_hints.md (12895, 2020-07-01)
100_Numpy_exercises_with_hints_with_solutions.md (30203, 2020-07-01)
100_Numpy_exercises_with_solutions.md (27801, 2020-07-01)
100_Numpy_random.ipynb (1424, 2020-07-01)
LICENSE.txt (1075, 2020-07-01)
generators.py (4394, 2020-07-01)
initialise.py (251, 2020-07-01)
requirements.txt (81, 2020-07-01)
source (0, 2020-07-01)
source\exercises100.ktx (29130, 2020-07-01)
source\headers.ktx (974, 2020-07-01)

## 100 numpy exercises [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org:/repo/rougier/numpy-100/notebooks/100%20Numpy%20exercises.ipynb) This is a collection of numpy exercises from numpy mailing list, stack overflow, and numpy documentation. I've also created some problems myself to reach the 100 limit. The goal of this collection is to offer a quick reference for both old and new users but also to provide a set of exercises for those who teach. For extended exercises, make sure to read [From Python to NumPy](http://www.labri.fr/perso/nrougier/from-python-to-numpy/). → [Test them on Binder](http://mybinder.org:/repo/rougier/numpy-100/notebooks/100_Numpy_exercises.ipynb) → [Read them on GitHub](100_Numpy_exercises.md) Note: markdown and ipython notebook are created programmatically from the source data in `source/exercises.ktx`. To modify the content of these files, please change the text in the source and run the `generators.py` module with a python interpreter with the libraries under `requirements.txt` installed. The keyed text format (`ktx`) is a minimal human readable key-values to store text (markdown or others) indexed by keys. This work is licensed under the MIT license. [![DOI](https://zenodo.org/badge/10173/rougier/numpy-100.svg)](https://zenodo.org/badge/latestdoi/10173/rougier/numpy-100)

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