Python 2.x Training for Scientists and Engineers Training Class | Webucator Programming Classes

Overview

This Python 2.x Training for Scientists and Engineers class 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 toward 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.

Goals

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.

Outline

The Python Environment
1. Starting Python
2. If the Interpreter Is Not in Your PATHs
3. Using the Interpreter
4. Trying Out a Few Commands
5. The help() Function
6. Running a Python Script
7. Python Scripts on Unix
8. Python Scripts on Windows
9. Python Editors and IDEs
Getting Started
1. Using Variables
2. Keywords
3. Built-in Functions
4. Variable Typing
5. Strings
6. Single-quoted String Literals
7. Triple-quoted String Literals
8. Raw String Literals
9. Unicode Literals
10. String Operators and Methods
11. Numeric Literals
12. Math Operators and Expressions
13. Converting among Types
14. Writing to the Screen
15. String Formatting
16. Legacy String Formatting
17. Command Line Parameters
18. Reading from the keyboard
Flow Control
1. About Flow Control
2. What's with the White Space?
3. if and elif
4. Conditional Expressions
5. Relational Operators
6. Boolean Operators
7. while Loops
8. Alternate Ways to Exit a Loop
Lists and Tuples
1. About Sequences
2. Lists
3. Tuples
4. Indexing and Slicing
5. Iterating through a Sequence
6. Functions for All Sequences
7. Using enumerate()
8. Operators and Keywords for Sequences
9. The xrange() Function
10. Nested Sequences
11. List Comprehensions
12. Generator Expressions
Working with Files
1. Text file I/O
2. Opening a Text File
3. The with Block
4. Reading a Text File
5. Writing to a Text File
6. "Binary" (Raw, or Non-delimited) Data
Dictionaries and Sets
1. About Dictionaries
2. When to Use Dictionaries
3. Creating Dictionaries
4. Getting Dictionary Values
5. Iterating through a Dictionary
6. Reading File Data into a Dictionary
7. Counting with Dictionaries
8. About Sets
9. Creating Sets
10. Working with Sets
Functions
1. Defining a Function
2. Function Parameters
3. Global Variables
4. Variable Scope
5. Returning Values
Exception Handling
1. Syntax Errors
2. Exceptions
3. Handling Exceptions with Try
4. Handling Multiple Exceptions
5. Handling Generic Exceptions
6. Ignoring Exceptions
7. Using else
8. Cleaning Up with finally
9. Re-raising Exceptions
10. Raising a New Exception
11. The Standard Exception Hierarchy
OS Services
1. The os Module
2. Environment Variables
3. Launching External Processes
4. Paths, Directories, and Filenames
5. Walking Directory Trees
6. Dates and Times
7. Sending email
Pythonic Idioms
1. Pythonic Idioms
2. The Zen of Python
3. Common Python Idioms
4. Packing and Unpacking
5. Lambda Functions
6. List Comprehensions
7. Generators vs Iterators
8. Generator Expressions
9. String Tricks
Modules and Packages
1. What Is a Module?
2. The Import Statement
3. Where Did that pyc File Come from?
4. Module Search Path
5. Zipped Libraries
6. Creating Modules
7. Packages
8. Module Aliases
9. When the Batteries Aren't Included
Classes
1. Defining Classes
2. Instance Objects
3. Instance Attributes
4. Methods
5. init
6. Properties
7. Class Data
8. Inheritance
9. Multiple INHERITANCE
10. Base Classes
11. Special Methods
12. Pseudo-private Variables
13. Static Methods
Developer Tools
1. Program Development
2. Comments
3. pylint
4. Customizing pylint
5. Unit Testing
6. The unittest Module
7. Creating a Test Class
8. Establishing Success or Failure
9. Startup and Cleanup
10. Running the Tests
11. The Python Debugger
12. Starting Debug Mode
13. Stepping through a Program
14. Setting Breakpoints
15. Debugging Command Reference
16. Benchmarking
Chapter 14 – XML and JSON
1. About XML
2. Normal Approaches to XML
3. Which Module to Use?
4. Getting Started with ElementTree
5. How ElementTree Works
6. Creating a New XML Document
7. Parsing an XML Document
8. Navigating the XML Document
9. Using XPath
10. Advanced XPath
iPython
1. About iPython
2. Features of iPython
3. Starting iPython
4. Tab Completion
5. Magic Commands
6. Benchmarking
7. External Commands
8. Enhanced Help
9. Notebooks
numpy
1. Python's scientific Stack
2. numpy Overview
3. Creating Arrays
4. Creating Ranges
5. Working with Arrays
6. Shapes
7. Slicing and Indexing
8. Indexing with booleans
9. Stacking
10. Iterating
11. Tricks with Arrays
12. Matrices
13. Data Types
14. numpy Functions
scipy
1. About scipy
2. Polynomials
3. Vectorizing Functions
4. Subpackages
5. Getting Help
6. Weave
A Tour of scipy subpackages
1. cluster
2. constants
3. fftpack
4. integrate
5. interpolate
6. io
7. linalg
8. ndimage
9. odr
10. optimize
11. signal
12. sparse
13. spatial
14. special
15. stats
pandas
1. About pandas
2. Pandas Architecture
3. Series
4. DataFrames
5. Data Alignment
6. Index Objects
7. Basic Indexing
8. Broadcasting
9. Removing Entries
10. Time Series
11. Reading Data
matplotlib
1. About matplotlib
2. matplotlib Architecture
3. matplotlib Terminology
4. matplotlib Keeps State
5. What Else Can You Do?
Python Imaging Library
1. The PIL
2. Supported Image File Types
3. The Image Class
4. Reading and Writing
5. Creating Thumbnails
6. Coordinate System
7. Cropping and Pasting
8. Rotating, Resizing, and Flipping
9. Enhancing

Class Materials

Each student in our Live Online and our Onsite classes receives a comprehensive set of materials, including course notes and all the class examples.

Class Prerequisites

Experience in the following would be useful for this Python class:

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.

Preparing for Class

Onsite: Course Setup

Customized Onsite Training