Hello! In this lesson, we will dive into the basic concepts of data cleaning using the Diamonds dataset from the seaborn library. Data cleaning is a crucial step in data preprocessing, ensuring that our data is ready for analysis by dealing with inconsistencies, errors, and missing values.

Data cleaning involves identifying and handling missing values, correcting errors, and ensuring consistency. By cleaning your data, you improve the quality of your analysis and the performance of machine learning models.

Let's quickly revisit how to load the dataset, explore its structure, and identify missing values. First, load the Diamonds dataset using the seaborn library:

View the first few rows to get an initial overview:

Output:

You can access a column using either diamonds['cut'] or diamonds.get('cut'). Both will return the 'cut' column, but get is safer as it does not raise a KeyError if the column is missing.