Having introduced the essential pandas commands to upload and preprocess your data in memory completely, in smaller batches, or even in single data rows, at this point of the data science pipeline 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 advise 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. A table of data, or matrix, is arranged in rows that represent your examples, and columns that contain the characteristic observed values of your examples, which are your variables.
Following our advice, you have to wrangle between two key Python packages for scientific analysis, pandas and NumPy, and their two pivotal data structures, DataFrame and ndarray. But your data science pipeline will be more efficient and fast...