Effective Python Setup Tips for Coding Success
Learn essential setup procedures for Windows, Linux, and Mac systems to establish a productive Python coding environment. Discover the process of setting up Python tools, utilizing the Linux command line interface, installing Anaconda, managing Python environments, and exploring major Python libraries. Enhance your workflow with Jupyter for interactive Python development.
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Presentation Transcript
Topics Windows based setup Work with the Linux command line interface Set up Python tools Coding environment
Windows based setup Anaconda Install python3 and pip
Command line interface (linux/mac terminal) Preferred systems Linux Windows subsystem for linux; or virtual machine + linux (e.g., virtualbox) Mac command line Common linux shell commands Quick start: https://ubuntu.com/tutorials/command-line-for-beginners More comprehensive: https://ryanstutorials.net/linuxtutorial/ Numerous youtube tutorials
Most important commands man show a command s usage File/directory operations Understand how files are organized file paths pwd, ls, cd, rm, mv, cp, mkdir, rmdir cat, wc, less, head/tail, sort Other utilities top, ps https://ryanstutorials.net/linuxtutorial/cheatsheet.php
Python environment Use python3 Install pip or anaconda (or miniconda) a tool for conveniently installing python libraries/tools python3 -m pip install --upgrade pip Python virtual environment is preferred python3 -m pip install --user virtualenv python3 -m venv env_name_chosen_by_you Activate the environment source env_name/bin/activate you will notice the environment name show up in the command prompt. Then, install packages you need with pip Deactivate the environment deactivate
Jupyter A convenient interactive interface for Python (https://jupyter.org/) Installation methods Activate the environment you created, then run pip install notebook Run jupyter notebook or python3 m notebook
Major python libraries numpy - scientific computing library pandas exploratory data analysis (tabular data pre-processing) Scikit learn (sklearn) most machine learning modeling methods (non deep learning) Matplotlib powerful visualization package It s easy to install them: in an activated environment pip install numpy pandas matplotlib sklearn
Other popular coding environments Check out vscode Sublime text
