Advanced Python 3 (4 Day Course) Training

Answers to Popular Questions:

 
Yes, this class can be tailored to meet your specific training needs.
Yes, we provide Python consulting services.
Yes, group discounts are provided.

Course Description

 
In this Python training course, students already familiar with Python programming will learn advanced Python techniques such as IPython Notebook, the Collections module, mapping and filtering, lamba functions, advanced sorting, writing object-oriented code, testing and debugging, NumPy, pandas, matplotlib, regular expressions, Unicode, text encoding and working with databases, CSV files, JSON and XML. This advanced Python course is taught using Python 3, however, differences between Python 2 and Python 3 are noted.
Course Length: 4 Days
Course Tuition: $1290 (US)

Prerequisites

Basic Python programming experience. In particular, you should be very comfortable with: working with strings; working with lists, tuples and dictionaries; loops and conditionals; and writing your own functions. Experience in the following areas would be beneficial: some exposure to HTML, XML, JSON, and SQL.

Course Outline

 
IPython Notebook
Getting Started with IPython Notebook
Creating Your First IPython Notebook
IPython Notebook Modes
Useful Shortcut Keys
Markdown
Magic Commands
Getting Help
 
Advanced Python Concepts
Advanced List Comprehensions
Collections Module
Mapping and Filtering
Lambda Functions
Advanced Sorting
Unpacking Sequences in Function Calls
Modules and Packages
 
Working with Data
Databases
CSV
Getting Data from the Web
HTML
XML
JSON
 
Classes and Objects
Creating Classes
Attributes, Methods and Properties
Extending Classes
Documenting Classes
Static, Class, Abstract Methods
Decorator
 
Testing and Debugging
Creating Simulations
Testing for Performance
The unittest Module
 
NumPy
One-dimensional Arrays
Multi-dimensional Arrays
Getting Basic Information about an Array
NumPy Arrays Compared to Python Lists
Universal Functions
Modifying Parts of an Array
Adding a Row Vector to All Rows
Random Sampling
 
pandas
Series and DataFrames
Accessing Elements from a Series
Series Alignment
Comparing One Series with Another
Element-wise Operations
Creating a DataFrame from NumPy Array
Creating a DataFrame from Series
Creating a DataFrame from a CSVl
Getting Columns and Rows
Cleaning Data
Advanced Python
Combining Row and Column Selection
Scalar Data: at[] and iat[]
Boolean Selection
Plotting with matplotlib
 
Regular Expressions
Regular Expression Syntax
Python's Handling of Regular Expressions
 
Unicode and Encoding
Encoding and Decoding Files in Python
Converting a File from cp1252 to UTF-8

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