Reformulate docs and curate environment.yml

CHANGELOG.rst

@@ -2,6 +2,12 @@
 Changelog
 =========
 
+Version 0.6
+===========
+
+* Curated the list of packages in ``environment.yml``
+* Editable install of package direclty in ``environment.yml``
+
 Version 0.5.1
 =============
 

src/pyscaffoldext/dsproject/templates/environment_yml.template

@@ -6,26 +6,44 @@ channels:
 dependencies:
   - python>=3.6
   - pip
+  # BASIC
   - numpy
   - scipy
   - pandas
-  - dask
-  - ipython
   - tqdm
   - click
+  # VISUALIZATION
   - matplotlib
   - seaborn
-  - altair
-  - statsmodels
-  - scikit-learn
-  - pytorch
-  - yellowbrick
+  # - altair
+  # - yellowbrick
+  # ML, STATS & DEEP LEARNING
+  # - statsmodels
+  # - scikit-learn
+  # - sktime
+  # - tslearn
+  # - xgboost
+  # - catboost
+  # - lightgbm
+  # - pytorch
+  # - fastai
+  # - tensorflow
+  # - keras
+  # - spacy
+  # OTHER TOOLS
+  # - optuna
+  # - dask
   - pip:
      - -e .  # install git checkout of ${name} in editable mode
      # add here only pip-packages that are not available in conda/conda-forge! E.g.:
-     - optuna
+     # - icecream
+     # - jax
+     # - numpyro
+     # - funsor
+     # - neptune-client
+     # - neptune-contrib
 
-  # for development only (could also be kept in a separate environment file)
+  # DEVELOPMENT ONLY PACKAGES (could also be kept in a separate environment file)
   - jupyterlab
   - pytest
   - pytest-cov

src/pyscaffoldext/dsproject/templates/readme_md.template

@@ -10,7 +10,7 @@ A longer description of your project goes here...
 
 In order to set up the necessary environment:
 
-1. create an environment `${name}` with the help of [conda],
+1. review and uncomment what you need in `environment.yml` and create an environment `${name}` with the help of [conda],
    ```
    conda env create -f environment.yml
    ```
@@ -21,8 +21,6 @@ In order to set up the necessary environment:
 
 > **_NOTE:_**  The conda environment will have ${name} installed in editable mode.
 > Some changes, e.g. in `setup.cfg`, might require you to run `pip install -e .` again.
-> Be aware that `environment.yml` contains quite many data science related packages, thus
-> commenting all those which are not needed before creating the environment saves you some time and space.
 
 
 Optional and needed only once after `git clone`: