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Full Description
Doing Science With Python introduces readers to the most popular coding tools for scientific research, such as Anaconda, Spyder, Jupyter Notebooks, and JupyterLab, as well as dozens of important Python libraries for working with data, including NumPy, matplotlib, and pandas. No prior programming experience is required! You'll be guided through setting up a professional coding environment, then get a crash course on programming with Python, and explore the many tools and libraries ideal for working with data, designing visualisations, simulating natural events, and more.
Contents
Introduction
Part 1: Setting up for Science
Chapter 1: Installing Anaconda and Launching Navigator
Chapter 2: Keeping Organized with Conda Environments
Chapter 3: Simple Scripting in Jupyter Qt Console
Chapter 4: Serious Scripting with Spyder
Chapter 5: Jupyter Notebook: An Interactive Journal for Computational Research
Chapter 6: JupyterLab: Your Center for Science
Part 2: Python Primer
Chapter 7: Integers, Floats, and Strings
Chapter 8: Variables
Chapter 9: The Container Data Types
Chapter 10: Flow Control
Chapter 11: Functions and Modules
Chapter 12: Files and Folders
Chapter 13: Object Oriented Programming
Chapter 14: Documenting your Work
Part 3: The Scientific and Visualization Libraries
Chapter 15: The Scientific Libraries
Chapter 16: The InfoVis and SciVis Visualization Libraries
Chapter 17: The GeoVis Libraries
Part 4: The Essential Libraries
Chapter 18: Numpy: Numerical Python
Chapter 19: Demystifying Matplotlib
Chapter 20: Pandas, Seaborn, and Scikit-learn
Chapter 21: Managing Dates and Times with Python and Pandas
Appendix A: Answers to the "Test your Knowledge" Challenges
