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Loading the Data into Jupyter Using a Pandas DataFrame
Oftentimes, data is stored in tables, which means it can be saved as a comma-separated variable (CSV) file. This format, and many others, can be read into Python as a DataFrame object, using the Pandas library. Other common formats include tab-separated variable (TSV), SQL tables, and JSON data structures. Indeed, Pandas has support for all of these. In this example, however, we are not going to load the data this way because the dataset is available directly through scikit-learn.
An important part after loading data for analysis is ensuring that it's clean. For example, we would generally need to deal with missing data and ensure that all columns have the correct datatypes. The dataset we use in this section has already been cleaned, so we will not need to worry about this. However, we'll see messier data in the second chapter and explore techniques for dealing with it.