Yesterday, I completed another course of a specialisation cycle dedicated to Python for data analytics.
While the first course was dealing with the very basics of variables, conditional loops, iterations and functions, this course further builds on data structures such as strings, files, lists, dictionaries and tuples. In general, there are multiple ways to perform a task on data, but only few of them are simple and smart (“pythonic”). Selecting the right data structure is of utmost importance. The assimilation of Python idioms necessitates a little bit of time, but it is fundamental step to build on, and allows for very short and efficient code that can perform complex tasks.