Hello and welcome! In today's lesson, you'll learn how to display and interpret summary statistics for categorical data within the Diamonds dataset. By the end of this lesson, you'll know how to group data by categories and generate meaningful statistical summaries using Python's data science libraries such as pandas and numpy.

Grouping data by categories is a fundamental part of Exploratory Data Analysis (EDA). It allows us to segment our dataset into different categories and analyze each group separately. For example, if you have sales data from multiple cities, you might want to group the data by city to understand sales performance in each location.

In Python, we achieve this using the groupby() function from the pandas library. This function groups data by one or more columns, which enables us to apply aggregation functions like mean, median, or standard deviation to each group.

Before proceeding with analysis, it is crucial to ensure that the data is in the right format. In this lesson, we'll focus on the price column, which should be numeric to compute summary statistics.

We'll use the pd.to_numeric() function to ensure that the price column contains numeric values. This function converts values to numeric types, and we'll use the parameter to convert any invalid parsing into NaNs.