microblog VS PythonDataScienceHandbook

In this blog, we're going to address the "on any website" part of a Search Solution. Or at least - propose a starting point for it. There are many great tutorials out there for a deep dive on Flask - one of the best from my colleague Miguel.

- https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial...

If you'd like to deploy a web app, Flask is your best friend. It's very user friendly and there's a lot of great tutorials online. The only thing you'd need other than Python knowledge is some basic understanding of HTML/CSS and Jinja notation for variables, both of which are pretty intuitive to learn. Good luck!

I can't speak highly enough of Miguel Grinberg's work with Python/Flask (https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial...) and the community he's created around it, for both beginners and advanced folks.

Racing through his mega tutorial was a great refresher for me on the fundamentals, and it's easy to plug in computer vision & related libraries/extensions/packages.

Use miguel grinberg’s tutorial https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world

I got a recommendation (from a few places) to use Miguel Grinberg's microblog series to help me get up to speed on some flask things. I'm on ch 15 with blueprints, and am running into pylint cyclic import errors, both on my app and in the actual project (https://github.com/miguelgrinberg/microblog/tree/v0.15?search=1)

In the project root directory create a folder called static with one subfolder called js and another called css. The js folder will contain all of the needed local JavaScript files while the css folder will contain all the CSS stylesheets. In the js folder create a file called index.js and in the css folder one called style.css. Just leave them empty for now. If you want to find out more about web development with Flask I suggest you try out this tutorial. Your current project structure should like this:

https://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-i-hello-world is not perfect, but a great start.

Python Data Science Handbook

Python Data Science Handbook — learn to use Python libraries such as NumPy, Pandas, Matplotlib, Scikit-Learn, and related tools to effectively store, manipulate, and gain insight from data

I don't know what tools you will be using but if you will be using Python you can start with Python Data Science Handbook by Jake VanderPlas and Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting DataData Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data which gives a very good outlook on the data science and big data frame work. PS: Jake's book is also available as jupyter notebooks so you can read and run the code at the same time.

Python Data Science Handbook by Jake VanderPlas (https://jakevdp.github.io/PythonDataScienceHandbook/)

Otherwise you can work through courses on Datacamp, Coursera, Udemy, etc, or check out this book for a more general non-spatial perspective.

7. Data Science Handbook Are you looking for a comprehensive guide to data science with Python? Look no further than the Data Science Handbook by Jake VanderPlas. This repository contains the entire book, which introduces essential tools and techniques used in data science, including IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a fantastic resource for anyone looking to deepen their understanding of data science concepts and best practices.

I can recommend https://jakevdp.github.io/PythonDataScienceHandbook/