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Steps to create a good project base for developing in a conda environment

Step-by-step (uncomplete) tutorial for setting up a base Python library given the following requirements:

• Use conda (instead of pipenv or others) because this is both a package manager and an environment manager, and installing the Python scientific stack (Numpy, Pandas, Scipy, Matplotlib, etc.) is straightforward
• Use Visual Studio Code (instead of PyCharm, Spyder or others) because it's free, runs on Windows and is one of the mostly used IDE
• Document and automate as many production steps as possible including linting (flake8), formatting (black), packaging (setup.py, setup.cfg), versionning (git), testing (pytest, pytest-cov, tox), documenting (sphinx, readthedocs), building (setuptools) and distributing (twine, keyring)
• Include IPython Notebooks and have them tested (pytest-nbval)

Table of content

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Make conda easily available on Windows

The goal is to lower the burden of running a script with Python when that Python executable is encapsulated within a conda environment. There are several ways to do that.

Add Anaconda Prompt to Windows right click context menu

Adapted from jiewpeng's gist (original gist here)

This describes how to add Anaconda Prompt to the Windows right click context menu. In doing so, the user can open a Command Prompt in any folder from the File Explorer, which is going to be activated

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name: holoviews

channels:
  - conda-forge

dependencies:
  - pyviz::holoviews=1.13*
  # Install requires
  - pyviz::param>=1.8.0,<2.0
  - pyviz::pyviz_comms>=0.7.2

maximlt

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

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from PyInstaller.utils.hooks import collect_data_files

datas = collect_data_files('panel', include_py_files=True) + \
        collect_data_files('pyviz_comms', include_py_files=True) + \
        collect_data_files('bokeh')  # See https://github.com/pyinstaller/pyinstaller/pull/4746

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@echo OFF
rem How to run a Python script in a given conda environment from a batch file.

rem It doesn't require:
rem - conda to be in the PATH
rem - cmd.exe to be initialized with conda init

rem Define here the path to your conda installation
set CONDAPATH=C:\ProgramData\Miniconda3
rem Define here the name of the environment

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name: panel_last_env

channels:
  - pyviz
  - conda-forge

dependencies:
  - panel
  - jupyterlab

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1. Install ptvsd using the same Python interpreter Qgis uses. E.g. on Windows:

C:\OSGeo4W64\apps\Python37\python.exe -m pip install ptvsd==4.3.2 

And on Linux:

pip3 install --user ptvsd

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#!/usr/bin/env python3

from datetime import date
import pathlib
import subprocess
import sys

def create_nb(nb_dir: str, extra_title: str = None) -> pathlib.Path:
    today = date.today()
    title = today.strftime("%B %d, %Y")

maximlt

Add this debug configuration that is going to serve the current file, i.e. the one with .servable() calls for Panel or a YAML file for Lumen:

  {
      "name": "Python: Panel",
      "type": "python",
      "request": "launch",
      "module": "panel",
 "args": [