Exploratory Data Analysis with F#, Plotly.NET, and ML.NET DataFrames

Using Polyglot Notebooks to explore datasets with F# & Plotly.NET

This article is my entry as part of F# Advent 2023. Visit Sergey Tihon’s blog for more articles in the series by other authors.

One of the most common tasks with data roles is the need to perform exploratory data analysis (EDA).

With EDA a data scientist, data analyst, or other data-oriented programmer can:

• Understand the value distributions of their data
• Identify outliers and data anomalies
• Visualize correlations, trends, and relationships between multiple variables

Exploratory data analysis usually involves:

1. Loading the data into a DataFrame
2. Performing descriptive statistics to identify the raw shape of the data
3. Visualizing variables of interest on their own or with other variables.

In this article I’ll walk you through the process of loading data from a sample dataset into a Microsoft.Data.Analysis DataFrame (the kind featured in ML.NET). Next, we'll look at the descriptive statistics the DataFrame class provides and then explore the process of creating some simple…