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Python for Data Analysis - Intermediate

Length 1 day

Course overview

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Why study this course

Following on from the Python Beginner course, Python Intermediate will build on your foundational knowledge of Python and pandas. You will learn how to manipulate data, create custom functions, plot with Matplotlib and display visualisations.

Understanding how to use Python for Data Analysis, empowers you to be much more efficient and opens up the possibility of using a wide array of freely available tools.

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Nexacu is part of the Lumify Group, offering you the largest public schedule of end user applications and professional development training in Australia, New Zealand, and the Philippines. You can now access the schedule of courses and book, by clicking on the button below.

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What you’ll learn

After completing this course, students will be able to:

Use the extensive data manipulation capabilities of pandas DataFrames
Customise the display of the output in Jupyter Notebooks
Use the plotting capabilities of Matplotlib to plot distributions and bar charts
Use the data visualisation library, Seaborn
Fit a basic model using scikit-learn

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.

View all Python at Lumify Work courses

Who is the course for?

This course is intended for those who want to build on their foundational knowledge of Python and pandas.

Course subjects

Introduction

Python Intermediate

User-Defined Functions in Python

Function basics
Parameters
Positional vs keyword arguments
Defining a function
Indentation
Scope
*args and **kwargs
Unpacking operators
Lambda expressions
Conditional expressions
List comprehensions

Modify the DataFrame Display

pandas options
Working with pandas styles
Applying a style that is not dependent on values
Formatting values
String formats
Applying a style that is dependent on values
Built-in conditional formatting

Export Notebook as

Export to PDF or HTML
Create slides

Copy vs View

Setting with copy warning

Working with Missing Values

Missing values
inf and -inf
Removing missing values
Replacing missing values

Importing Data

Importing into a pandas DataFrame

Manipulating Data

Summarise a dataset
Report and display multiple summary statistics
Ordering data
Working with dates
Add columns with assign()
Working with strings
Reordering and dropping columns
Selecting rows based on values
Grouping and summarising data
Replacing values
Concatenate data
Bin continuous variables into categories

Working with Relational Data

Joining data from two DataFrames

Visualising Distributions

Visual representation of distributions with Matplotlib and Seaborn
Histograms
Boxplots
Bar and column charts

Multivariate Analysis

Scatterplot matrix
Bar and column charts

Basic Modelling

Create a linear model with scikit-learn

Prerequisites

Students should have attended the Python for Data Analysis - Beginner course or have a foundational knowledge of Python and pandas.

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.