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.
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What you’ll learn
In this course, you will:
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
Stay ahead of the technology curve
Don’t let your tech outpace the skills of your people
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
30 years driving innovative, award-winning learning solutions
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations and from more vendors.
Quality instructors and content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world’s leading vendors as preferred training partner.
Ahead of the technology curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
30 years driving innovative, award-winning learning solutions
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations and from more vendors.
Quality instructors and content
Expert instructors with real world experience and the latest vendor-approved in-depth course content.
Partner-Preferred Supplier
Chosen and awarded by the world’s leading vendors as preferred training partner.
Ahead of the technology curve
No matter your chosen technologies or platforms, we can help you stay one step ahead.
Train Anywhere
From our state-of-the-art classrooms to telepresence to your offices, our instructor-led training caters to your needs.
Track Record
30 years driving innovative, award-winning learning solutions
More Courses, More Often
When you train with Lumify Work you get more courses, more often, in more locations and from more vendors.
Who is the course for?
It is aimed at 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.
Request Course Information
By submitting an enquiry, you agree to our privacy policy and receiving email and other forms of communication from us. You can opt-out at any time.
