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Practical Data Science in Python

Setting Up Your Environment

Setting Up Python and conda
Setting Up VS Code
Augmenting Your Command Line
Jupyter Notebooks
VS Code, R, and Jupyter

Command Line

Command Line Basics
Advanced Command Line

Numpy

Vectors
Subsetting Vectors
- Modifying Subsets of Vectors
- Recap
Views and Copies
Matrices
Arrays
Random numbers

Pandas

Welcome to Pandas
Indexes
Views and Copies in Pandas
- Views and Copies in pandas
- The View/Copy Headache in pandas without Copy on Write
Reading Data
- Binary Data
Data Cleaning
Data Manipulations

Data Visualization

Plotting in Python

Big Data & Performance

What is Big Data?
Big Data Strategies
Parquet
Object dtypes
Solving Performance Issues
Parallel Computing
Distributed Computing with dask

Practical Skills and Concepts

Debugging
- Debugging Python in VS Code
Defensive Programming
- The Iceberg Principle
Workflow Management
Getting Help Online
Python: Variables vs. Objects
- Python vs. R: Important Differences To Be Aware Of
Backwards Design
Numbers in Computers
Writing Good Jupyter Notebooks
Reviewing Code on Github
conda Environments

Professional Advice

How To Buy A Computer
chatGPT and You
So You’re Thinking About a PhD

Miscellaneous

Running Python in the VS Code Interactive Window
Understanding and Managing Python Packages
Combining numbers and text into strings: f-strings

Repository
Open issue

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Practical Data Science in Python

Practical Data Science in Python#

Welcome to Practical Data Science, the beginning of a textbook by Kyle Bradbury and Nick Eubank.

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Installing Python and Miniconda

By Kyle Bradbury & Nick Eubank

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