PYTHON

Course Number:

This course leads the student from the basics of writing and running Python scripts to more advanced features such as file operations, regular expressions, working with binary data, and using the extensive functionality of Python modules. Extra emphasis is placed on features unique to Python, such as tuples, array slices, and output formatting.

This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by graduated lab exercises.

Python Intro is a practical introduction to a working programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world.

Note: This course can be taught for either Python 2.x or Python 3.x. Version-specific course materials and exercises will be provided.

Audience:

Prerequisites:

Students should already have a working, user-level knowledge of Unix/Linux, Mac, or Windows. While not required, basic skills with at least one other programming language will be helpful.

• Create working Python scripts following best practices
• Use python data types appropriately
• Read and write files with both text and binary data
• Search and replace text with regular expressions
• Get familiar with the standard library and its work-saving modules
• Use lesser-known but powerful Python data types
• Create “real-world”, professional Python applications
• Work with dates, times, and calendars
• Know when to use collections such as lists, dictionaries, and sets
• Understand Pythonic features such as comprehensions and iterators
• Write robust code using exception handling

• An overview of Python
  • What is python?
  • Python Timeline
  • Advantages/Disadvantages of Python
  • Getting help with pydoc

• The Python Environmens
  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on Unix/Windows
  • Editors and IDEs

• Getting Started
  • Using variables
  • Built-in function
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • Command line parameters

• Flow Control
  • About flow control
  • White space
  • Conditional expressions
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

• Sequences
  • About sequences
  • Lists and list methods
  • Tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions,keywords, and operators
  • List comprehensions
  • Generator Expressions
  • Nested sequences

• Working with files
  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Reading and writing raw (binary) data
  • Converting binary data with struct

• Dictionaries and Sets
  • About dictionaries
  • Creating dictionaries
  • Iterating through a dictionary
  • About sets
  • Creating sets
  • Working with sets

• Functions
  • Defining functions
  • Parameters
  • Global and local scope
  • Nested functions
  • Returning values

• Sorting
  • The sorted() function
  • Alternate keys
  • Lambda functions
  • Sorting collections
  • Using operator.itemgetter()
  • Reverse sorting

• Errors and Exception Handling
  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions

• Modules and Packages
  • The import statement
  • Module search path
  • Creating Modules
  • Using packages
  • Function and Module aliases

• Classes
  • About OO programming
  • Defining classes
  • Constructors
  • Methods
  • Instance data
  • Properties
  • Class methods and data

• Regular Expressions
  • RE syntax overview
  • RE Objects
  • Searching and matching
  • Compilation flags
  • Groups and special groups
  • Replacing text
  • Splitting strings

• The Standard library
  • The sys module
  • Launching external programs
  • Math functions
  • Random numbers
  • The string module
  • Reading CSV data

• Dates and times
  • Working with dates and times
  • Translating timestamps
  • Parsing dates from text
  • Formatting dates
  • Calendar data

• Working with the file system
  • Paths, directories, and filenames
  • Checking for existence
  • Permissions and other file attributes
  • Walking directory trees
  • Creating filters with fileinput
  • Using shutil for file operations

• Advanced data handling
  • Defaultdict and Counter
  • Prettyprinting data structures
  • Compressed archives (zip, gzip, tar, etc.)
  • Persistent data

• Network services
  • Grabbing web content
  • Sending email
  • Using SSH for remote access
  • Using FTP

• Writing real-life applications
  • Parsing command-line options
  • Detecting the current platform
  • Trapping signals
  • Implementing logging

Course Number:

This course provides a ramp-up to using Python for scientific and mathematical computing. Starting with the basics, it progresses to the most important Python modules for working with data, from arrays, to statistics, to plotting results. The material is geared towards scientists and engineers.

This is a hands-on programming class. All concepts are reinforced by informal practice during the lecture followed by lab exercises. Many labs build on earlier labs, which helps students retain the earlier material.

Applied Python is 35 percent hands-on, 65 percent lecture, with the longest lecture segments lasting for around 45 minutes. Students “learn by doing,” with immediate opportunities to apply the material they learn to real-world problems.

Audience:

Prerequisites:

While there are no programming prerequisites, programming experience is helpful. Students should be comfortable working with files and folders, and should not be afraid of the command line.

• Create and run basic programs
• Design and code modules and classes
• Implement and run unit tests
• Use benchmarks and profiling to speed up programs
• Process XML and JSON
• Manipulate arrays with numpy
• Get a grasp of the diversity of subpackages that make up scipy
• Use iPython notebooks for ad hoc calculations, plots, and what-if?
• Manipulate images with PIL
• Solve equations with sympy

• The Python Environment
  • About Python
  • Starting Python
  • Using the interpreter
  • Running a Python script
  • Python scripts on Unix/Windows
  • Using the Spyder editor

• Getting Started
  • Using variables
  • Built-in functions
  • Strings
  • Numbers
  • Converting among types
  • Writing to the screen
  • String formatting
  • Command line parameters

• Flow Control
  • About flow control
  • White space
  • Conditional expressions (if,else)
  • Relational and Boolean operators
  • While loops
  • Alternate loop exits

• Sequences
  • About sequences
  • Lists and tuples
  • Indexing and slicing
  • Iterating through a sequence
  • Sequence functions,keywords, and operators
  • List comprehensions
  • Generator expressions
  • Nested sequences

• Working with Files
  • File overview
  • Opening a text file
  • Reading a text file
  • Writing to a text file
  • Raw (binary) data

• Dictionaries and Sets
  • Creating dictionaries
  • Iterating through a dictionary
  • Creating sets
  • Working with sets

• Functions
  • Defining functions
  • Parameters
  • Variable scope
  • Returning values
  • Lambda functions

• Errors and Exception Handling
  • Syntax errors
  • Exceptions
  • Using try/catch/else/finally
  • Handling multiple exceptions
  • Ignoring exceptions

• OS Services
  • The OS module
  • Environment variables
  • Launching external commands
  • Walking directory trees
  • Paths, directories, and filenames
  • Working with file systems
  • Dates and times

• Pythonic idioms
  • Small Pythonisms
  • Lambda functions
  • Packing and unpacking sequences
  • List Comprehensions
  • Generator Expressions

• Modules and Packages
  • Initialization code
  • Namespaces
  • Executing modules as scripts
  • Documentation
  • Packages and name resolution
  • Naming conventions
  • Using imports

• Classes
  • Defining classes
  • Constructors
  • Instance methods and data
  • Attributes
  • Inheritance
  • Multiple inheritance

• Developer Tools
  • Analyzing programs with pylint
  • Creating and running unit tests
  • Debugging applications
  • Benchmarking code
  • Profiling applications

• XML and JSON
  • Using ElementTree
  • Creating a new XML document
  • Parsing XML
  • Finding by tags and XPath
  • Parsing JSON into Python
  • Parsing Python into JSON

• iPython
  • iPython basics
  • Terminal and GUI shells
  • Creating and using notebooks
  • Saving and loading notebooks
  • Ad hoc data visualization

• numpy
  • numpy basics
  • Creating arrays
  • Indexing and slicing
  • Large number sets
  • Transforming data
  • Advanced tricks

• scipy
  • What can scipy do?
  • Most useful functions
  • Curve fitting
  • Modeling
  • Data visualization
  • Statistics

• A Tour of scipy Subpackages
  • Clustering
  • Physical and mathematical Constants
  • FFTs
  • Integral and differential solvers
  • Interpolation and smoothing
  • Input and Output
  • Linear Algebra
  • Image Processing
  • Distance Regression
  • Root-finding
  • Signal Processing
  • Sparse Matrices
  • Spatial data and algorithms
  • Statistical distributions and functions
  • C/C++ Integration

• pandas
  • pandas overview
  • Dataframes
  • Reading and writing data
  • Data alignment and reshaping
  • Fancy indexing and slicing
  • Merging and joining data sets

• matplotlib
  • Creating a basic plot
  • Commonly used plots
  • Ad hoc data visualization
  • Advanced usage
  • Exporting images

• The Python Imaging Library (PIL)
  • PIL overview
  • Core image library
  • Image processing
  • Displaying images