All Projects → rsokl → Learning_python

rsokl / Learning_python

Licence: cc-by-sa-4.0
Source material for Python Like You Mean it

Programming Languages

python
139335 projects - #7 most used programming language

Projects that are alternatives of or similar to Learning python

Thesemicolon
This repository contains Ipython notebooks and datasets for the data analytics youtube tutorials on The Semicolon.
Stars: ✭ 345 (+342.31%)
Mutual labels:  jupyter-notebook, data-science, tutorial, numpy
Machine learning refined
Notes, examples, and Python demos for the textbook "Machine Learning Refined" (published by Cambridge University Press).
Stars: ✭ 750 (+861.54%)
Mutual labels:  jupyter-notebook, data-science, numpy
Intro To Python
An intro to Python & programming for wanna-be data scientists
Stars: ✭ 536 (+587.18%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Python Introducing Pandas
Introduction to pandas Treehouse course
Stars: ✭ 24 (-69.23%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Code search
Code For Medium Article: "How To Create Natural Language Semantic Search for Arbitrary Objects With Deep Learning"
Stars: ✭ 436 (+458.97%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Dsp Theory
Theory of digital signal processing (DSP): signals, filtration (IIR, FIR, CIC, MAF), transforms (FFT, DFT, Hilbert, Z-transform) etc.
Stars: ✭ 437 (+460.26%)
Mutual labels:  jupyter-notebook, tutorial, numpy
Allstate capstone
Allstate Kaggle Competition ML Capstone Project
Stars: ✭ 72 (-7.69%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Stats Maths With Python
General statistics, mathematical programming, and numerical/scientific computing scripts and notebooks in Python
Stars: ✭ 381 (+388.46%)
Mutual labels:  jupyter-notebook, data-science, numpy
Machinelearningcourse
A collection of notebooks of my Machine Learning class written in python 3
Stars: ✭ 35 (-55.13%)
Mutual labels:  jupyter-notebook, data-science, numpy
Computervision Recipes
Best Practices, code samples, and documentation for Computer Vision.
Stars: ✭ 8,214 (+10430.77%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Ncar Python Tutorial
Numerical & Scientific Computing with Python Tutorial
Stars: ✭ 50 (-35.9%)
Mutual labels:  jupyter-notebook, tutorial, numpy
Pycon 2019 Tutorial
Data Science Best Practices with pandas
Stars: ✭ 410 (+425.64%)
Mutual labels:  jupyter-notebook, data-science, tutorial
User Machine Learning Tutorial
useR! 2016 Tutorial: Machine Learning Algorithmic Deep Dive http://user2016.org/tutorials/10.html
Stars: ✭ 393 (+403.85%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Data Science Your Way
Ways of doing Data Science Engineering and Machine Learning in R and Python
Stars: ✭ 530 (+579.49%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Open source demos
A collection of demos showcasing automated feature engineering and machine learning in diverse use cases
Stars: ✭ 391 (+401.28%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Har Keras Coreml
Human Activity Recognition (HAR) with Keras and CoreML
Stars: ✭ 23 (-70.51%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Scikit Learn Videos
Jupyter notebooks from the scikit-learn video series
Stars: ✭ 3,254 (+4071.79%)
Mutual labels:  jupyter-notebook, data-science, tutorial
25daysinmachinelearning
I will update this repository to learn Machine learning with python with statistics content and materials
Stars: ✭ 53 (-32.05%)
Mutual labels:  jupyter-notebook, data-science, numpy
Awesome Google Colab
Google Colaboratory Notebooks and Repositories (by @firmai)
Stars: ✭ 863 (+1006.41%)
Mutual labels:  jupyter-notebook, data-science, tutorial
Numerical Linear Algebra
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Stars: ✭ 8,263 (+10493.59%)
Mutual labels:  jupyter-notebook, data-science, numpy

Python Like You Mean It

View this content as hosted on Python Like You Mean It

This repository contains the source material for the website Python Like You Mean It. The site is written primarily in Jupytext-markdown (which are eventually transformed into html using nbsphinx). A huge perk of this of this is that you simply need to be familiar with Jupyter notebooks and some markdown syntax in order to contribute to this project!

Asking Questions

Please feel free to post questions (or point out mistakes) about the reading by opening a GitHub issue. Someone from the PLYMI team will respond ASAP! Refer to this reference to see how to include python-codeblocks in your post. This will make it much easier for us to discuss code with each other.

How To Contribute

Contributions to this project are very welcome! I will be sure to credit any/all contributions (unless your want to remain anonymous). Some great ways to help out are to:

  • proofread
  • add reading comprehension exercises to existing sections
  • provide general feedback about the organization of the website, the consistency of the material, etc.
  • create lengthier standalone problems to serve as holistic examples of how to apply the concepts presented in the reading
  • recommend new sections to be added to PLYMI

You can either open an issue to provide feedback or point out errors, or you can create a pull request if you want to submit new/modified materials. The other maintainers of PLYMI and I are happy to help you refine your issues and PRs, so please do not be discouraged if you are new to Git/GitHub.

I have posted a number of "To-Do" tasks in this project's issues page, and I will be adding to these as this project progresses. Included are requests for proofreading and exercises. Please post within an issue if you are working on a to-do item, so multiple people don't end up working on the same task. General feedback on content is also hugely valuable, so feel free to open a new issue to provide your feedback on any of the material.

Here is a nice Markdown "cheat sheet" for looking up how to make tables, code-blocks, etc., in Markdown.

Making a Pull Request

If you want to submit a change to some of the content (e.g. correcting typos), do the following:

  1. Fork this repository
  2. Create a new branch, appropriately named for whatever task you are performing: git checkout -b your_branch_name
  3. In your new branch, make the relevant changes and commit them.
  4. Push your branch to your fork: git push origin your_branch_name
  5. Create a Pull Request from your fork branch into the master branch of this repo.

Building the Site

Important Note: it is strongly preferred that pull requests do not contain changes to the HTML of this site. Rather, it is better if PRs simply contain changes to text files (.rst or .md). A site administrator (@rsokl, @davidmascharka) will be responsible for publishing the actual site-HTML. Thus the following instructions are useful for you to view your changes as they will appear in the site, but you likely need not go through the process of committing the changes to the HTML.

Creating a Conda Environment From Scratch

First, create a miniconda environment. We'll call it plymi and will use Python 3.8

conda create -n plymi python=3.8

It is important that we activate the environment before proceeding

conda activate plymi

Next, we will install ipython, Jupyter, numpy, and matplotlib

conda install ipython jupyter notebook numpy matplotlib 

Next, we'll use the conda-forge package channel to install some critical packages for building the HTML

conda install -c conda-forge sphinx==3.4.3 nbsphinx==0.8.1 pandoc==2.1.3 jupytext=1.9.1 nbformat=5.0.8

Finally, we will use PyPi to install jupytext and our website's stylistic theme

pip install sphinx-rtd-theme==0.5.1

and install the plymi code base from this repo. Clone the present repository and run:

pip install .

Using this environment, you should now be able to run sphinx to build the html for this site from the source-code. To do this, run the following commands in your Python terminal:

import plymi
plymi.convert_src_to_html("./Python") # point to the dir containing `conf.py`

This will convert all of the "restructured text" (.rst) files to html via sphinx. jupytext is responsible for converting the markdown (.md) files to jupyter notebooks (.ipynb) and then nbsphinx converts these notebooks to html. These html files will be located in Python/_build. You can open the index.html page in your browser to view how the locally-built site looks on your computer.

Note that, if you are introducing a new page to the site or are doing anything that would affect the site's navigation-bar, it is a good idea to delete the _build directory before building the html. This will make sure that sphinx fully generates the pages from scratch.

Publishing HTML for this site

Once you have built the html and have verified that it looks good to you, navigate to the top level of the repository and run:

import plymi
plymi.build_to_doc(".") # point to the top-level dir (contains both `docs/` and `docs_backup`)

This will back-up your current docs directory, and will move the html from _builds to docs. It will also ensure some essential "meta" files, .nojekyll and CNAME are present. The former is required for githubpages to build the site correctly, the latter ensures that the canonical name for the site is pythonlikeyoumeanit.com.

The only directories in this repository that contain html should be docs and docs_backup. Do not commit the _build directory

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].