Data Analytics With Python

Goal: Create a new column based on data from another column. Use Pandas.DataFrame and Numpy.select to create the new column.  You can see the full program at  Google Colaboratory There are three basic steps to accomplish the goal.   Define the conditions Define the values Use np.select to apply the conditions and values. First, create a list containing each "condition," then  create a list containing each return v alue.   The goal is to correlate each condition with each value.  What does that mean?  It means if the first condition in the "conditions" list evaluates as True, return the first value in the "values" list.   An error will occur when the lists have different index counts.  For example, if there are five conditions and 4 values.      This condition example below has two useful applications we would be remiss if we didn't take note:   The first is the use of .isnull() == True. The second is th...

This platform aims to systematically curate and preserve my favored Python techniques for future reference. This digital journal, accessible globally wherever an internet connection is available, is a convenient and effective learning tool.  Going down a wooded path at night, alone, with your trusted companion by your side, can be scary and daunting.  However, with support from your friends, documenting your path (curating a code base like a blog), and continuously working on the code base can make the process appear as a walk in the park!  In a world dominated by sophisticated language models like OpenAI's GPT, blogging may seem like a relic of the past. But here's a nuanced perspective: this blog serves as an innovative instrument for both teaching and mastering new programming languages. It's about immersing in syntax, applying it, and then translating the information into layman's terms for broader understanding. Each topic presented here is elaborated in a separate...