Exploratory Data Analysis with Graphext vs Jupyter Notebooks: A Comprehensive Guide

May 21, 2023
Short Studies
Victoriano Izquierdo
Victoriano Izquierdo

In this video, I demonstrate how Graphext can be used for exploratory data analysiseven if you're already proficient in Python and Pandas. Here's a breakdown of the video by chapters.

Chapter 1: Importing and Understanding Data

I demonstrated how to import a CSV file into Graphext and provided a preview of the dataset. I then compared this process to the steps taken in a Python notebook, highlighting how Graphext automatically displays the number of rows and columns, as well as the distribution of data for each variable.

Chapter 2: Data Preparation

In this chapter, I showed how to remove or hide irrelevant columns and cast data types in Graphext. I also demonstrated how to rename columns and check for missing values, emphasizing the visual nature of Graphext that allows users to see distributions while making these decisions.

Chapter 3: Feature Understanding

I delved into univariate analysis, demonstrating how Graphext automatically plots variables and allows users to adjust the granularity of the data. I also showed how to add titles to charts, save them as insights, and customize their appearance.

Chapter 4: Feature Relationships

Here, I explored relationships between pairs of variables. While Graphext doesn't currently support scatter plots, I demonstrated how box plots can be used to visualize these relationships. I also showed how Graphext's explore mode allows for interactive filtering of data.

Chapter 5: Correlation and Mutual Information

In this chapter, I discussed how Graphext uses mutual information to show correlations between variables, which can work with both numerical and categorical data types.

Chapter 6: Asking Questions About the Data

The final chapter focused on using Graphext to answer specific questions about the data. I demonstrated how to filter and map data to find the locations with the fastest roller coasters.


Graphext offers a compelling alternative to traditional coding in Python and Pandas. This video is a testament to the power and versatility of Graphext in the realm of data analysis.


The Data

Kaggle Project

Explore Yourself

Graphext Project

Key Variables

Type of Analysis

Relevant Industries

Other stories

Ready To Get Started?

Ready To Get Started?

Let's dive into your data with Graphext. It's super simple, and you'll get your project ready in a few minutes.