Python for Data Analysis - Advanced

  • Length 1 day
Course overview
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Why study this course

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

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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.


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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.