Having introduced the essential pandas commands to upload and preprocess your data in memory completely, or even in smaller batches (or in single data rows), you'll have to work on it in order to prepare a suitable data matrix for your supervised and unsupervised learning procedures.

As a best practice, we suggest that you divide the task between a phase of your work when your data is still heterogeneous (a mix of numerical and symbolic values) and another phase when it is turned into a numeric table of data arranged in rows that represent your examples, and columns that contain the characteristic observed values of your examples, which are your variables.

In doing so, you'll have to wrangle between two key Python packages for scientific analysis, pandas and NumPy, and their two pivotal data structures, DataFrame and ndarray.

Since the target data structure is a NumPy ndarray object, let's start from the result we want to achieve.