The Ultimate Python Pandas Guide

This post is designed to outline all of the content we have for Python Pandas. Structured in a way so that you can work through the content in order, and go from zero to hero with Pandas.

Getting Started

What is Pandas

A brief overview of exactly what Python pandas is, and why you should care about it.

What is Python Pandas

Installation Guide

Installation guide. How you can start using Python Pandas today

Python Pandas Installation Guide

Intro to Jupyter Notebooks

Introduction to Jupyter Notebooks and how to use them. We will be running all of our pandas code in Jupyter notebooks

Introduction to Jupyter Notebooks

Importing CSV files

Import CSV files into Python pandas. This is how we will bring in our data for this series!

Import CSV files into Python pandas

Data Preparation with Python Pandas

Renaming Columns

Learn how to rename single or multiple columns, along with using Lambda function to rename all columns programmatically.

Renaming Columns in Pandas

Working with Data Types in Python Pandas

Learn about the different data types in Pandas. Discover how to work with numerical, categorical, and textual data, as well as dates and times.

Working with Data Types

Handling Null values with Python Pandas

Learn about the different data types in Pandas. Discover how to work with numerical, categorical, and textual data, as well as dates and times.

Handling Null values

Working with Columns

Creating Columns

In this post, we will look at how we add columns to our dataset.

Creating and adding columns

Removing Columns

Let’s look at how you can delete/remove columns.

Removing/Deleting columns

Selecting Columns

Learn how to select only the columns that you need.

Selecting Columns

Adding date related columns with Python Pandas

In our analysis, we’re going to be looking at things over time, and we need to create date columns to make this happen.

Adding date related columns

Aggregating Data With Python Pandas

Aggregating data refers to the process of summarizing data by grouping it and applying statistical functions to the groups. This can be useful for understanding patterns and trends in the data, and for creating more compact and meaningful summaries of the data.

Aggregating data

Visualisations with Pandas data

We’re now moving on to visualising data. The conclusion of this short series. Now that you can clean, prepare and aggregate data, visualising it is the last step.

Visualising data

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