• Post category:StudyBullet-3
  • Reading time:5 mins read


Let’s get to grips with the Python Pandas library for data analytics / analysis

What you will learn

Perform data analysis with the Pandas library

Learn about dataframes and how to conduct data analysis in Python

Understand how to handle missing values in your data

Understand how to handle & clean up messy data

Description

The demand for data engineers is greater than ever. So, there is no better time to upskill; learn Python and specifically data engineering.

I’ll take you through the core concepts of dataframes, which are a key data structure within Pandas. We’ll learn to ingest, clean and analyse the data and by the end of the course, you’ll be in a position to use Python & Pandas on your own data to extract valuable insight.


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The idea isn’t to become an expert through this course. The idea is to become confident in the core concepts of Python and Pandas so you can solve real-world problems today and so you can continue your learning by doing.

Because, nobody becomes an expert through taking a course (no matter how long they are), you only truly become an expert by getting out there & solving problems.

For this course, you’ll need some basic Python knowledge, which you can gain from my FREE No Nonsense Python course here on Udemy.

You will need to have Python installed and the Pandas library installed – which you can do using ‘pip install pandas’.

English
language

Content

Introduction
Introduction
A project to get us started
Project introduction: What’s the data & what do we want to achieve?
Ingesting data & cleaning it up
Initial insight from the data
Extracting a little more insight
The Pandas Essentials
Importing data
Inspecting our dataframe – what do we have to work with?
Handling missing values
Removing duplicated data
Where Statements
Selecting specific fields from the dataframe
Replacing values in our dataframe
Group By
Ranking our dataframes on a specific field value
The Apply function: data cleanup & additional insight
Write dataframes back to files
Practicing what we have learned
Your challenge, if you accept it
Challenge solution
Conclusion
A real world example of using Pandas
Where do I go from here?
Thank you