In this video, I demonstrate how Graphext can be used for exploratory data analysis, even 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.
Conclusion
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.
