Description

The Python Bootcamp provides an overview of the fundamental concepts necessary to work within the Python programming language. Through a series of annotated, hands-on lessons, students will learn to understand the basic principles of the Python language including:

1. installing Python and working with Jupyter
2. understanding Python objects and data types
3. working with base Python structures (e.g., lists, dictionaries)
4. importing packages and additional data structures (e.g., NumPy arrays; pandas DataFrames)
5. utilizing control flow statements
6. reading and writing data
7. manipulating data
8. plotting (e.g., Matplotlib, seaborn, Plotly)
9. developing functions

No prior programming experience is necessary to benefit from this course.

Note: For the R version of this course, visit the r-bootcamp.

Table of Contents

Unit 1

• Installing Python
  • Running Python
• Installing Jupyter
  • Google Colaboratory and Deepnote
• Working with Jupyter Notebooks
  • The Basics
  • Side Notes

Unit 2

• Arithmetic Operators
• Syntax Considerations
• Multiple Operations
• Errors and Warnings
• Functions
• Importing Functions
  • Installing Packages
  • Updating Packages
  • More on Importing: as and from

Unit 3

• Data Types
  • Numeric
  • Boolean
    • Comparison Operators
    • Boolean Operators
  • Text Sequence
• Explicit Type Conversion
• Date and Time Types

Unit 4

• Variables
  • Variable Naming
  • Augmented Assignment
  • Multiple Assignment
  • Deleting Variables
• Data Structures
  • Lists
    • Accessing List Elements
    • Slicing
    • In and Not In
    • Updating List Elements
    • Adding List Elements
    • Deleting List Elements
    • Joining Lists
    • Other List Methods
    • dir
  • Ranges
  • Tuples
    • Accessing Tuple Elements
    • Updating, Adding, and Deleting Tuple Elements
    • Joining Tuples
    • Other Tuple Methods
    • zip
  • Sets
    • Accessing Set Elements
    • Updating Set Elements
    • Adding Set Elements
    • Deleting Set Elements
    • Joining Sets
    • Other Set Methods
  • Dictionaries
• References vs. Copies
  • Comparison Operators: Identity

Unit 5

• Importable Data Structures
  • NumPy Arrays
    • Accessing Array Elements
    • Updating Array Elements
    • Adding Array Elements
    • Deleting Array Elements
    • Joining Arrays
    • Other Array Methods
    • Multidimensional Arrays
  • pandas DataFrames
    • Reading and Viewing Data
    • Accessing DataFrame Elements
    • Bitwise Operators and Querying
    • Updating DataFrame Elements
    • Adding DataFrame Elements
    • Deleting DataFrame Elements
    • Renaming Indexes
    • Joining DataFrames
    • Grouping and Aggregate Functions
    • Writing Data
    • Other DataFrame Methods
  • pandas Series

Unit 6

• Control Flow
  • if
    • elif, else
    • pass
    • Nesting
    • Input
  • for
    • break
    • continue
    • enumerated
    • List Comprehension
  • while
    • break and continue
• Defining Functions
  • lambda and map

Unit 7

• Plotting Overview
• Getting Started
• Point Plots
  • seaborn
  • pandas
• Line Plots
• Importing R Data Sets
• Bar Plots
• Density Plots and Histograms
• Box Plots
• Sizing and Styling seaborn Plots
• Saving Plots
• Interactive Visualizations

To-Do (Work-in-Progress)

• Environments
• BLAS
• Add more on pandas, dict
• extend discussion of dicts (and JSON), including Python 3.9 features such as dict union
• Jupyter Lab spellchecker plugin
• mention unofficial Windows binaries in Unit 1