· Pandas · 2 min read
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.
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.
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.
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.
Working with Columns
Creating Columns
In this post, we will look at how we add columns to our dataset.
Removing Columns
Let’s look at how you can delete/remove columns.
Selecting Columns
Learn how to select only the columns that you need.
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.
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.
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.
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