Category: Python

Mira Celine Klein needs to create Excel reports:

In this article you will learn how to get data from Python into an Excel file and add some formatting. Excel reports are a great way to communicate data or results, especially to people who don’t use Python. Another great advantage is that you can create automated reports: You define once what the reports should look like, and then you can create it very quickly for, for example, different subgroups of data, or data that is updated regularly.

The first part of the article describes the most important functions and actions, for example, setting column widths, changing font colors, or adding hyperlinks to other sheets. In the second part, all of these features are combined in one Excel file.

This looks a lot like programming against the Excel COM objects in Powershell but maybe a little easier.

Adrian Tam shows how you can test the performance of calls in Python:

Profiling is a technique to figure out how time is spent in a program. With this statistics, we can find the “hot spot” of a program and think about ways of improvement. Sometimes, hot spot in unexpected location may hint a bug in the program as well.

In this tutorial, we will see how we can use the profiling facility in Python. Specifically, you will see

– How we can compare small code fragments using timeit module

– How we can profile the entire program using cProfile module

– How we can invoke a profiler inside an existing program

– What the profiler cannot do

Read on for those techniques.

John Mount performs mapping en masse:

The tricky part is: data science application scale easily has hundreds of string valued variables, each having hundreds of thousands of tracked values. The possibility of a large number of variable values or level renders the CASE/WHEN solution undesirable- as the query size is proportional to the number variables and values. The JOIN solutions build a query size proportional to the number of variables (again undesirable, but tolerable). However, super deeply nested queries are just not what relational databases expect. And a sequence of updates isn’t easy to support as a single query or view.

As an example of remapping, John shows translating “a” in a column to 1, “b” to 2, “d” to 3, etc.—that is, perhaps mapping each unique string to a unique number.

Adrian Tam takes us through debugging options with Python:

The purpose of a debugger is to provide you a slow motion button to control the flow of a program. It also allow you to freeze the program at certain point of time and examine the state.

The simplest operation under a debugger is to step through the code. That is to run one line of code at a time and wait for your acknowledgment before proceeding into next. The reason we want to run the program in a stop-and-go fashion is to allow us to check the logic and value or verify the algorithm.

For a larger program, we may not want to step through the code from the beginning as it may take a long time before we reached the line that we are interested in. Therefore, debuggers also provide a breakpoint feature that will kick in when a specific line of code is reached. From that point onward, we can step through it line by line.

This is something I definitely need to get better at when doing Python development.