Looking for inspiration? datacamp_facebook_live_titanic [1] by datacamp [2].
DataCamp Facebook Live Code Along Session 2: Learn how to complete a Kaggle competition using exploratory data analysis, data munging, data cleaning and machine leaning. Enjoy.
References:
[1]: https://github.com/datacamp/datacamp_facebook_live_titanic
[2]: https://github.com/datacamp
Posts tagged: github-stars
All posts with the tag "github-stars"
820 posts
latest post 2026-03-22
Publishing rhythm
I’m really excited about standard-readme [1], an amazing project by RichardLitt [2]. It’s worth exploring!
A standard style for README files
References:
[1]: https://github.com/RichardLitt/standard-readme
[2]: https://github.com/RichardLitt
Check out lepture [1] and their project python-livereload [2].
livereload server in python
References:
[1]: https://github.com/lepture
[2]: https://github.com/lepture/python-livereload
I recently discovered tqdm [1] by tqdm [2], and it’s truly impressive.
⚡ A Fast, Extensible Progress Bar for Python and CLI
References:
[1]: https://github.com/tqdm/tqdm
[2]: https://github.com/tqdm
I’m really excited about cmder [1], an amazing project by cmderdev [2]. It’s worth exploring!
Lovely console emulator package for Windows
References:
[1]: https://github.com/cmderdev/cmder
[2]: https://github.com/cmderdev
I recently discovered setup.py [1] by navdeep-G [2], and it’s truly impressive.
📦 A Human’s Ultimate Guide to setup.py.
References:
[1]: https://github.com/navdeep-G/setup.py
[2]: https://github.com/navdeep-G
I like WaylonWalker’s [1] project pyDataVizDay [2].
A python implementation of the Data Viz Day visualization.
References:
[1]: https://github.com/WaylonWalker
[2]: https://github.com/WaylonWalker/pyDataVizDay
If you’re into interesting projects, don’t miss out on iplotter [1], created by niloch [2].
JavaScript charting in ipython/jupyter notebooks -
References:
[1]: https://github.com/niloch/iplotter
[2]: https://github.com/niloch
Just starred apistar [1] by encode [2]. It’s an exciting project with a lot to offer.
The Web API toolkit. 🛠
References:
[1]: https://github.com/encode/apistar
[2]: https://github.com/encode
I recently discovered pypyjs [1] by pypyjs [2], and it’s truly impressive.
PyPy compiled to JavaScript
References:
[1]: https://github.com/pypyjs/pypyjs
[2]: https://github.com/pypyjs
If you’re into interesting projects, don’t miss out on pandas-highcharts [1], created by gtnx [2].
Beautiful charting of pandas.DataFrame with Highcharts
References:
[1]: https://github.com/gtnx/pandas-highcharts
[2]: https://github.com/gtnx
Check out PythonDataScienceHandbook [1] by jakevdp [2]. It’s a well-crafted project with great potential.
Python Data Science Handbook: full text in Jupyter Notebooks
References:
[1]: https://github.com/jakevdp/PythonDataScienceHandbook
[2]: https://github.com/jakevdp
I like timofurrer’s [1] project colorful [2].
Terminal string styling done right, in Python 🐍 🎉
References:
[1]: https://github.com/timofurrer
[2]: https://github.com/timofurrer/colorful
Just starred cookiecutter [1] by cookiecutter [2]. It’s an exciting project with a lot to offer.
A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.
References:
[1]: https://github.com/cookiecutter/cookiecutter
[2]: https://github.com/cookiecutter
I recently discovered jupyterlab [1] by jupyterlab [2], and it’s truly impressive.
JupyterLab computational environment.
References:
[1]: https://github.com/jupyterlab/jupyterlab
[2]: https://github.com/jupyterlab
Just starred tidy-data-python [1] by nickhould [2]. It’s an exciting project with a lot to offer.
Tidy Data in Python Jupyter Notebook
References:
[1]: https://github.com/nickhould/tidy-data-python
[2]: https://github.com/nickhould
The work on write-pythonic-code-demos [1] by mikeckennedy [2].
Write Pythonic Code Like a Seasoned Developer video course demo materials.
References:
[1]: https://github.com/mikeckennedy/write-pythonic-code-demos
[2]: https://github.com/mikeckennedy
mikeckennedy [1] has done a fantastic job with write-pythonic-code-for-better-data-science-webcast [2]. Highly recommend taking a look.
No description available.
References:
[1]: https://github.com/mikeckennedy
[2]: https://github.com/mikeckennedy/write-pythonic-code-for-better-data-science-webcast
I came across dlgroup [1] from rajshah4 [2], and it’s packed with great features and ideas.
Deep Learning Group
References:
[1]: https://github.com/rajshah4/dlgroup
[2]: https://github.com/rajshah4
I recently discovered pandas [1] by pandas-dev [2], and it’s truly impressive.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
References:
[1]: https://github.com/pandas-dev/pandas
[2]: https://github.com/pandas-dev