Advanced Python

Overview

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.

Audience

Students already familiar with Python programming.

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 duration

4 Days

Course outline

1. IPython Notebook

Getting Started with IPython Notebook

Creating Your First IPython Notebook

IPython Notebook Modes

Useful Shortcut Keys

Markdown

Magic Commands

Getting Help

2. Advanced Python Concepts

Advanced List Comprehensions
Collections Module
Mapping and Filtering
Lambda Functions
Advanced Sorting
Unpacking Sequences in Function Calls
Modules and Packages

3. Working with Data

Databases
CSV
Getting Data from the Web
HTML
XML
JSON

4. Classes and Objects

Creating Classes
Attributes, Methods and Properties
Extending Classes
Documenting Classes
Static, Class, Abstract Methods
Decorator

5. Testing and Debugging

Creating Simulations
Testing for Performance
The unittest Module

6. 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

7. 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
Combining Row and Column Selection
Scalar Data: at[] and iat[]
Boolean Selection
Plotting with matplotlib

8. Regular Expressions

Regular Expression Syntax
Python's Handling of Regular Expressions

9. Unicode and Encoding

Encoding and Decoding Files in Python
Converting a File from cp1252 to UTF-8