Following on from our Python for Data Analysis - Intermediate course, Python Advanced will build on your knowledge of Python and pandas. The focus of this course is learning to work more efficiently in Python.
You will learn to use control flow structures and loops and write your own custom functions and classes to automate analyses and improve efficiency. Other learning outcomes include the use of method chaining and pipes to perform multiple operations on DataFrames, the creation of interactive visualisations with Bokeh and the writing of code to automate these processes.
Nexacu Public Schedule
With Lumify Group's acquisition of Nexacu, we're pleased to now offer you the largest public schedule of end user applications training in Australia and New Zealand. As we move to consolidate our end user offering with Nexacu, as an interim measure you can now access the schedule of the most closely aligned courses and book, by clicking on the link below.
After completing this course, students will be able to:
Create interactive visualisations with Bokeh
Write code to automate these processes
Create user-defined functions and classes
Use loops and other control structures, plus alternatives
Use method chaining and pipes to perform multiple operations on DataFrames
Python at Lumify Work
Lumify Work's Python offering has been refreshed to include courses which fully align to certifications from the Python Institute. Our courses are delivered by Python programming experts who can structure their topic flow to your goals and functions, whether it's Python for automation or for data science or for finance.
This course is intended for those who want to learn to work more efficiently in Python.
Course subjects
Introduction
Working more efficiently in Python
Automating frequent data analysis operations
Principles of working more efficiently with code
User-Defined Functions
When to create your own functions
Function basics
Parameters
Positional vs keyword arguments
Defining a function
Indentation
Scope
*args and **kwargs
Unpacking operators
Order of arguments in a function
Adding a docstring
Assertions
Loafing functions for reuse
lambda expressions
Loops and Other Control Structures
if…elif...else
for loops
Loop over sequences
Loop over ranges
Enumerate
Loop over pandas groups
Loop over multiple lists while loops
else, break and continue
Saving results from a loop
Combining loops and functions
Loop and if-else alternatives
np.where() and np.select()
Conditional expressions
List comprehensions
Python built-in map() function
Evaluating performance efficiency
IPython and magic commands
pandas map()
pandas apply() and applymap()
User-Defined Classes
When to create your own class
Defining classes
Docstrings
The __init__() method
The self parameter
Class objects – attribute references and instantiation
Data attributes
Methods
Scope
Dunder methods
Performing multiple operations on DataFrames
Method chaining
Using pandas pipes with custom functions
Interactive Visualisations with Bokeh
Bokeh basics
Working with Bokeh in Jupyter
Glyphs
Providing data
Using the Bokeh toolbar
Customising the Bokeh toolbar
Creating links between plots
Using interactive legends
Add tooltips
Plotting from a grouped pandas
DataFrame
Save your Bokeh chart
Prerequisites
Python for Data Analysis - Advanced builds upon the skills learned in our Beginner and Intermediate courses.
Students should have attended these courses or be very familiar with the concepts covered in them. You will not be expected to write code from scratch but having these skills will better enable you to engage with the content.
Minimum requirements:
comfortable working in Python and with
Python built-in data structures
lists, dictionaries, tuples, and sets
pandas DataFrames
DataFrame methods
Basic familiarity with Matplotlib
Terms & Conditions
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
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