gyleodhis/My-Data-Science-Devops

This repository contains code snipe sets of what I consider best practices in the field of data science. This however is a work in continuous progress and its not final. I make every effort to update this repo at least once a week.

Project Directory

. ├── LICENSE ├── Numpy │   ├── NumpyCheatSheet(Draft).ipynb │   └── NumpyCheatSheet(Draft).py ├── Pandas │   ├── PandasCheetSheet.ipynb │   └── PandasCheetSheet.py └── README.md

2 directories, 6 files

This work is done in Jupyter Notebooks however any installation of ipython should comfortably compile the notebooks.

Within each directory I have tried to include normal python3 scripts (.py) for those who would like to run the scripts that way.

Prequisites:

To successfully run this codes make sure you have the following installed in your machine: 1. Python 3.5 and above 2. ipython (This can be installed using: pip3 install ipython) 3. Jupyter Notebook. (pip3 install jupyter) 4. Numpy. (pip install numpy) 5. Pandas. (pip install pandas) Any error incurred during the above installations can be corrected by starting the commands with (sudo pip ...) or by running the terminal as an ADMIn if you are using windows.

Testing And Running

The python (.py) scripts can be run AS IS

To run the notebooks make sure you reload the whole kernel in jupyter; (Restert the kernel, then re-run the whole notebook(with dialog)).

Contributing:

Submit a pull request.

Many thanks and I hope it helps.