Introduction to Python and NumPy for ChBE39

         

A Tufts University Data Lab Workshop
Written by Uku-Kaspar Uustalu

 

Python resources: go.tufts.edu/python
Questions: datalab-support@elist.tufts.edu
Feedback: uku-kaspar.uustalu@tufts.edu

Slides: tufts.box.com/v/chbe39-python

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Table of Contents

• Part 1/2: Introduction to Programming with Python
• Part 2/2: Scientific Computing with Python using NumPy
• Additional Resources

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Part 1/2: Introduction to Programming with Python

     

A hands-on introductory notebook that is suitable for self-guided study and covers the following:

• Using Python in a notebook environment
• Working with lists, dictionaries, and tuples
• Basic string manipulation
• Writing your own functions
• Loops and list comprehension
• Control flow with conditionals
• Reading documentation and modifying function behavior
• Difference between functions and methods
• Lambda (anonymous) functions and map functions
• View (pass-by-reference) vs copy (pass-by-value)

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Part 2/2: Scientific Computing with Python using NumPy

     

An interactive introduction to NumPy and Matplotlib that makes comparisons with MATLAB and covers the following:

• Importing Python packages and common aliases
• The NumPy numpy.ndarray a.k.a. np.array data structure
• Different ways of creating, indexing, and slicing NumPy arrays
• Broadcasting and using mathematical operators with np.array
• Universal functions and aggregation functions
• Iterating and mapping over elements in NumPy arrays
• Row-major vs column-major order and array flattening
• Shape manipulation and the difference between copies and views
• Various options for importing and exporting NumPy arrays
• Tweaking the textual representation of numbers using format strings
• Reading files with Python using with and open()
• Quick overview of creating and styling plots with Matplotlib
• Creating a figure with multiple subplots in Matplotlib

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Additional Resources

• Python Basics
  • Kaggle Python Course (contains interactive hands-on exercises with hints and solutions)
  • W3Schools Python Tutorial (contains interactive examples)
  • Google’s Python Class (contains exercises with solutions)
  • Official Python Tutorial
• NumPy
  • University of Helsinki Data Analysis with Python MOOC (contains exercise prompts)
  • NumPy Quickstart Tutorial
  • NumPy Basics for Absolute Beginners
  • NumPy for MATLAB Users
• Matplotlib
  • University of Helsinki Data Analysis with Python MOOC (contains exercise prompts)
  • Official Matplotlib Tutorials
• Package Management with Conda
  • Getting Started with Conda
  • Managing Packages with Conda
  • Managing Environments with Conda
  • Differences Between Conda and Pip