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Exploratory Data Analysis (EDA) When first starting out with a data set, it's always a good idea to go through the data description and see what you can already learn. When it comes to scikit-learn, you don't immediately have this information readily available, but in the case where you import data from another source, there's usually a data description present, which will already be a sufficient amount of information to gather some insights into your data. However, these insights are not merely deep enough for the analysis that you are going to perform. You really need to have a good working knowledge about the data set. Performing an exploratory data analysis (EDA) on a data set like the one that this tutorial now has might seem difficult. Where do you start exploring these handwritten digits? Gathering Basic Information on Your Data Let's say that you haven't checked any data description folder (or maybe you want to double-check the information that has been given to you). Then you should start with gathering the basic information. When you printed out the digits data after having loaded it with the help of the scikit-learn datasets module, you will have noticed that there is already a lot of information available. You already have knowledge of things such as the target values and the description of your data. You can access the digits data through the attribute data. Similarly, you can also access the target values or labels through the target attribute and the description through the DESCR attribute. To see which keys you have available to already get to know your data, you can just run digits.keys ( )