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Python 3 Advanced

Number of Days: 4


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.



Students already familiar with Python programming.

This course will help you prepare for the new certification by Python Institute Open Education & Development PCPP – Certified Professional in Python Programming



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 for Performance
  • The unittest Module


  • 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


  • 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
  • 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


Schedule your class   Schedule >