Analysis of public Datasets
- Disclaimer
- Versions
- V2 - Python, Google Sheets AND DataStudio
- V1 - Notebook Description (deprecated)
- Tecnical Notes
- How to contribute
The present notebook is for educational purposes
- You can send your contributions with a pull request or on twitter
@eocode
- Python, Googhe Sheets AND DataStudio
- Jupyter notebook AND Google COLAB
Create a Google Cloud Project here: https://console.developers.google.com/
- Enable Drive API and generate json
- Enable Google Sheets API
Copy *.json to root app
On file main.py edit the name api.json for your file name and GSheets data
run this command for install dependences
python -m venv env
pip install -r requirements.txt
Open your Google Sheets and Share with client_email inside on your file .json
run
python main.py
- Connect Dataset in Google Sheets to DataStudio
This repo contains a notebook with analisys of COVIT-19 propagation in Mexico and other countries
- Deaths in México start analysis
- Deaths start day 0
- Impact = Deaths / Cases
This detection dependence by country tests
This repo is build with Jupyter Notebook and Anaconda/Python 3, for run open next file:
COVIT-19 Analisys.ipynb
The notebook make this:
- Download the data
- Clean and Filter the data
- Analisys the data
- Visualize the data
https://colab.research.google.com/drive/1KsGxBwe0cNkQVemaM5HRax11025qNSmn#scrollTo=AIo7aJ2h1iS3
Relationship cases and deaths
The best of cases is Japan tendency
Compare China and Italy
- Excecute commands
! pwd
- Cells Support markdown
- Export to LATEX, HTML, PDF, etc
- Cells operations, merge, add, edit, update, delete
- View metadata
- Find and replace data
- Kernel operations (Instance of python) interrupt, stop, restart ...
- Snippets
- Execute Python and JS
- Dinamic Variables with forms
- Connect to local python kernel
- temporal code
Send me a pull request with your changes or catact me on Twitter or Linkedin as EOCode