python_koans VS cheatsheets

- python

The learn to code in 2 months boot camps are largely rubbish but self teaching the basics in something like Python (I like the Koans https://github.com/gregmalcolm/python_koans as they will reach you coding, Google skills, testing, and source control) will at least tell you if you like coding.

This reminds me of the Python Koans .. I loved doing those :) https://github.com/gregmalcolm/python_koans

After that, I'd work through something interactive. Lots off choices here, but maybe something like the Python koans: https://github.com/gregmalcolm/python_koans

When I started my current job I was given a couple of weeks to learn python. (I was a ruby dev previously). I really enjoyed the Python koans (https://github.com/gregmalcolm/python_koans) as a change from just reading docs, and something that gave me some small sense of achievement

I'm learning a new language right now and I'm finding its "koans" helpful. "Koans" are a format for tutorials that take the form of a script that runs on your computer, that you gradually fix up like Stardew Valley. There are Python koans here - https://github.com/gregmalcolm/python_koans

Not exactly what you asked for but a good addition to learning. https://github.com/gregmalcolm/python_koans

github.com/gregmalcolm - Python Koans

When I was learning Ruby for a job ruby koans was helpful. Python has a version here: https://github.com/gregmalcolm/python_koans not sure of the quality though.

Matplotlib - A Python 2D plotting library.

Data visualization: utilizing Python's Matplotlib for visualizing order book information.

Name: matplotlib Version: 3.7.1 Summary: Python plotting package Home-page: https://matplotlib.org Author: John D. Hunter, Michael Droettboom Author-email: [email protected] License: PSFLocation: /home/huinker/.local/lib/python3.10/site-packages

Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses without relying on expensive or proprietary software.

programming language: basic python, pandas, matplotlib -- you'll probably do these in school, but if not https://cs50.harvard.edu/python/2022/ https://matplotlib.org/

Analysis was done in Jupyter Notebook with Python 3.10, Pandas, Matplotlib, wordcloud and Mercury framework.

Analysis was done in Jupyter Notebook with Python 3.10 kernel, Pandas, Matplotlib, wordcloud and Mercury framework to share notebook as a web application with widgets and code hidden. Gif created in Canva.