What it includes:
-
github api v4
-
graphql
-
python3
-
TODO: Presentation slides
-
Project todos are here
To set a project directory:
Navigate to: packages/server/src/config/constants.py
You might need to set it here as well:
packages/server/notebooks/social_network_graph_analysis.py
Make a copy of:
cp packages/server/template.env packages/server/.env
And update the file to include your Personal Access Token from Github
Export from Jupyter Notebooks as python Commits nicely with github
Import back into Jupyter Notebooks:
How it works:
# %%
Python/R code goes here
# %% markdown
#
# Text goes here
Then it can converted back with Atom Hydrogren OR VS Code Python Package
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── TODO: docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── TODO: requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── TODO: setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── TODO: features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── TODO: models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience