Instructor-led training

Python for Applied Engineers Training in Crowley, Texas

Python Programming training from Hartmann Software Group, delivered onsite in Crowley, Texas or live online.

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What you'll learn

This is a 4 - day course that 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 result s. The material is geared towards scientists and engineers. This is an intense, 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 student s retain the earlier material. Python for Programming 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.

Prerequisites

Must have some experience coding in Python.

Course Outline

Python Refresher

Variables

Python Data Types

Sequences

Mapping Types

Program Structure

Files an Console I/O

Conditionals

Loops

Defining a Function

Function Parameters

Builtins

Python 2 vs 3 differences

Pythonic Programming

Common Python Idioms

Slicing and Dicing

Unpacking Function Arguments

Lambda Functions

Nested Functions

List Comprehensions

Iterables

Generator Expressions

Writing Generators

Python Time Travel

Three Python Easter Eggs

A String Trick

String Formatting

Mapping and Filtering

Modules and Packages

Modules

Using import

Initialization Code

Namespaces

Executing Modules as Scripts

Packages

Configuring Import with

__init__.py

Name Resolution (AKA scope)

Nested Functions

Python Style

Classes

About OO Programming

Defining Classes

Constructors

Instance Methods

Properties

Class Methods and Data

Static Methods

"Private" Methods

Inheritance

Untangling the Nomenclature

Collections Module

Metaprogramming

Special Attributes

globals() and locals()

Working with Attributes

The inspect module

Decorators

Decorator Functions

Decorator Classes

Decorating Classes

Creating Classes at Runtime

Monkey Patching

Regular Expressions

Database Access

The DB API

Available Interfaces

Connecting to a Server

Creating a Cursor

Executing a Statement

Fetching Data

Tip: Making an iterator for large queries

Parameterized Statements

Dictionary Cursors

Metadata

Transactions

Object-relational Mappers

qt GUI Programming with PyQt4

What is PyQt4?

Event Driven Applications

GUI Application Flow Chart

External Anatomy of a PyQt4

Application

Internal Anatomy of a PyQt4

Application

Using designer

Anatomy of a designer-based application

Naming Conventions

Common Widgets

Layouts

Selectable Buttons

Making your application stretch

Actions and Events

Menu Bar

Status Bar

Using predefined dialogs

Creating Custom Dialogs

Tabs

Niceties

Working with Images

Network Programming

Sockets

Socket options

Client Concepts

Server Concepts

Application Protocols

Forking Servers

Grabbing HTML from the Web

Consuming Web Services

Web Data the Easier Way

Sending email

Binary Data

The struct module

Multiprogramming

What are Threads?

The Python Thread Manager

The threading module

Threads for the impatient

Creating a thread class

Variable Sharing

Using Queues

Debugging threaded programs

The multiprocessing module

Alternatives to multiprogramming

XML and JSON

About XML

Normal approaches to XML

Which module to use?

Getting Started with ElementTree

How ElementTree works

Creating a new XML Document

Parsing an XML Document

Navigating the XML Document

Using XPath

About JSON

Reading JSON

Writing JSON

Extending Python

Why extend Python?

Ways to extend Python with C

Hand-coded C

Overview

The C Program

Methods

The Method Table

The init function

Handling errors

Custom exception objects

Putting it all together

Using SWIG

The interface file

Generating the Wrappers

Building and installing the extension

cytpes

cffi

numpy

Python’s scientific stack

numpy overview

Creating arrays

Creating ranges

Working with arrays

Shapes

Slicing and indexing

Indexing with Booleans

Stacking

Iterating

Tricks with arrays

Matrices

Data types

numpy functions

scipy

About scipy

Polynomials

Vectorizing functions

Subpackages

Getting help

Weave

A Tour of scipy subpackages

cluster

constants

fftpack

integrate

interpolate

linalg

ndimage

odr

optimize

signal

sparse

spatial

special

stats

pandas

About

pandas

Pandas architecture

Series

DataFrames

Data Alignment

Index Objects

Basic Indexing

Broadcasting

Removing entries

Time series

Reading Data

matplotlib

About matplotlib

matplotlib architecture

matplotlib Terminology

matplotlib keeps state

What else can you do?

Training locations

Python for Applied Engineers training across the U.S. and beyond.

We provide Python for Applied Engineers training onsite and live online for organizations throughout the United States and internationally.

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