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python-for-scientists's Introduction

Python for scientists

See the full page.

For many scientists, the open-source nature of Python is intimidating. They’d like to use Python and Jupyter notebooks, but they don’t know how to begin. Proprietary software like Matlab and Mathematica comes pre-packaged and ready out-of-the-box, but Python is less straightforward. Our goal is to provide a quick guide to help scientists get started.

4 steps to Python for scientists:

  1. Install Python using Miniconda
  2. Install the Jupyter Notebook
  3. Install core scientific packages
  4. Run the Jupyter Notebook

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Contributing

We'd love to hear from you. If you have questions or ideas for the page, don't hesitate to open an issue.

If you'd like to contribute, open a PR. If you don't know how, check out this one page guide tp contributing!

python-for-scientists's People

Contributors

biophyser avatar harmsm avatar jharman25 avatar lcwheeler avatar zsailer avatar

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python-for-scientists's Issues

scientific packages pages

Add header lists with extremely brief descriptions of each package at the top of page.

Example:

scikitlearn (machine learning)

Is this webpage helpful?

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Please give us a thumbs up if you found this page useful! Click on the smiley face in the top right corner of this box!

If you have another topic that you think we should add, feel free to open a new issue!

numerical core

Libraries to discuss on "Scientific Python's numerical core" page

  • numpy
  • scipy
  • pandas

Install page discussion

For install Python, I'm thinking we actually need three different "Installing Python" pages--one for each OS (windows, mac, and linux).

If this webpage is going to be useful, we need to directly address installing on Windows vs. Linux-core.

What do you think? @lcwheeler @jharman25 @biophyser

environments and kernels

Start with a short explanation why you would want an environment. Then how to make one using conda and finally how to set up a kernel for jupyter for that environment.

Make website more visually appealing

I'd like to discuss ways we can make each webpage visually appealing. Perhaps this means using graphics in places where there is too much text. Or, maybe we need more code-block examples.

I've tried add some visual graphics in PR #12 as an example.

plotting core page

Libraries to discuss on the "Scientific Python's plotting core"

  • Matplotlib
  • Seaborn
  • Altair
  • Plotly?

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