OdinMorningstar / awesome-notebooks

Ready to use data science templates, organized by tools to jumpstart your projects and data products in minutes. 😎 published by the Naas community.

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Naas Templates Awesome

(aka the "awesome-notebooks")

What is Naas ?

Naas is an all-in-one data platform that enable anyone with minimal technical knowledge to turn Jupyter Notebooks into powerful automation, analytical and AI data products thanks to low-code formulas and microservices.

The platform is based on 3 low-code layers:

  • 😎 Templates: enable anyone to use data engines on all kind of subjects in minutes.
  • 🏎 Drivers: connectors to facilitate access to tools, and complex libraries (database, API, ML algorithm...)
  • πŸͺ Features: production microservices on top of Jupyter like scheduling, asset sharing, notifications and more.

Naas Cloud is free to use with 100 credits/month.
Open your account

>> More information

What is the objective of this repository ?

The objective of this repository is to create the largest catalog of production-ready Jupyter Notebooks templates. With those templates, it becomes easy to create data products (analytical dashboards, automation/AI engines and more). Check out the data-product-template repository to learn more.
The repository is organized by source/tools for easy discovery. You can also use our "Google-like" search to find templates by keywords

To ensure the quality of the templates, we have defined a framework. Each notebook shall be organized with the following sections:

  • Naas logo
  • # Title: "Tool - Action of the notebook", as h1 (an "Open in Naas" button will be automatically added by the CI/CD when a notebook is merged to the master branch)
  • Tags: hastags of the topics the notebook is about, as text
  • Author: name and social profile link of the author(s), as text
  • Description: a one-liner explaining the benefits of the notebooks for the user, as text
  • ## Input: list of all the variables, credentials, that needs to be setup, as h2
  • ## Model: list the functions applied to the data, as h2
  • ## Output: list the assets to be used by the user and its distribution channels if any, as h2

How to contribute ?

Pre-requisites:

  • Open free account on Naas Cloud so we can test the templates in a similar environment
  • Register to the Contributor Program so we can add you to team of contributors in the Naas GitHub organization
  • Join our Slack Community so you can present yourself and #chat with us

Step by step process:

  • Step 1: Find or propose an issue you want to work on
    • The Backlog of the Product Roadmap is where we put all the priorities
    • The Issues section is where we gather all the needs
  • Step 2: Prepare the issue before you start working on it
    • Make sure the description is clear
    • Tag yourself in Assignees section
    • Change the status to 'In Progress' in Projects section/Community Roadmap
    • Create a branch in Development section
  • Step 3: Clone the awesome-notebooks repository on your Naas Cloud account and switch to the branch you created
  • Step 4: Create folder named with the source tool (if it's not already created)
  • Step 5: Copy/Paste template.ipynb at the root of the folder inside the folder you are working on, and start working on your notebook
  • Step 6: Once you are happy with the result, commit to the branch by using Git extension or command line (make sure you use a GitHub personal access token and not password, otherwise it wont work)
  • Step 7: Open a Pull Request and add a member of the core team as Reviewer (Florent,Maxime or Jeremy)
  • Step 8: Change status of this Issue to β€œReview” in Projects section and comment the Pull Request with a brief on what you have done
  • Step 9: Expect a feedback and merge in the next 48h-72h
  • Step 10: Once merged, promote your work on LinkedIn, Twitter and other social media channels! (Optional, but people need to know you are awesome πŸ˜‰)

>> More information

Support on social media

We are committed to sharing templates and giving shout outs to the contributors on our social media platforms, you can support us on:

Template analytics

Templates_monthly

Templates list

AWS

Abstract API

Affinity

Agicap

Airtable

AlphaVantage

Azure Blob Storage

Bazimo

BeautifulSoup

BigQuery

Bitly

Boursorama

Bubble

CCXT

CSV

Canny

Celestrak

Cityfalcon

Clockify

Cloud Mercato

Creditsafe

D-Tale

Dash

Dask

Data.gouv.fr

Draft Kings

EM-DAT

Elasticsearch

Excel

FAO

FEC

FED

FTP

Faker

GitHub

Gmail

Google Analytics

Google Calendar

Google Drive

Google Search

Google Sheets

Google Slides

HTML

Healthchecks

HubSpot

Hugging Face

IFTTT

IMDB

INPI

IPyWidgets

IUCN

Insee

Instagram

Integromat

Johns Hopkins

Jupyter Notebooks

Jupyter

Kaggle

Knative

LeFigaro

LinkedIn Sales Navigator

LinkedIn

Matplotlib

Metrics Store

Microsoft Teams

Microsoft Word

MongoDB

MySQL

NASA

Naas Auth

Naas Dashboard

Naas

Neo

Newsapi

Notion

OpenAI

OpenBB

OpenPIV

OpenWeatherMap

OwnCloud

PDF

Pandas

Pandasql

Panel

Pillow

Pipedrive

Plaid

Plotly

Polars

PostgresSQL

PowerPoint

PyCaret

PyGWalker

PyPI

Python

Pyvis

Qonto

Quandl

Reddit

Redshift

Remoteok

Remotive

SAP-HANA

SEON

SQLite

SWIFT

SendGrid

Sendinblue

SharePoint

Slack

Snowflake

Societe.com

Spotify

Stabilty AI

Streamlit

Stripe

Supabase

Telegram

Text

Thinkific

TikTok

Trello

Twilio

Twitter

Typeform

US Bureau of Labor Statistics

Vizzu

WAQI

WSR

Wikipedia

WindsorAI

WorldBank

Worldometer

XGBoost

XML

YahooFinance

YouTube

ZIP

Zapier

ZeroBounce

gTTS

spaCy


Contact us on support@naas.ai if you need any help or join our [Slack community](https://join.slack.com/t/naas-club/shared_invite/zt-1970s5rie-dXXkigAdEJYc~LPdQIEaLA)

About

Ready to use data science templates, organized by tools to jumpstart your projects and data products in minutes. 😎 published by the Naas community.

License:BSD 3-Clause "New" or "Revised" License


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Language:Jupyter Notebook 99.9%Language:Python 0.1%Language:Makefile 0.0%