5 Excel chores worth automating with Python (and how to tell which ones aren't)
Most "Excel problems" I get asked about are not Excel problems. They are the same five boring steps someone does every Monday by hand. Here is how I decide whether a task is worth scripting, and the small Python patterns that cover most of them. 1. Count the clicks, not the rows A 200,000-row file that you open once a year does not need a script. A 40-row file that three people copy-paste into a…
Five manual tasks in Excel that can benefit from automation using Python are discussed. To determine if a task merits scripting, consider the number of clicks involved rather than the number of rows, as a frequent repetitive sequence taking longer than 10 minutes to complete manually will yield cost savings via automation within a month.
The automation process typically involves merging files with identical columns, cleaning data by trimming spaces, unifying dates, splitting and removing duplicates, summarizing data through pivot tables, and renaming and sorting files. Once written, the script should be user-friendly, enabling a non-programmer to run it with a simple click, which involves creating a run.bat file, an "in/" and "out/" folder, and a README with straightforward instructions.
To ensure data integrity, the script should record the number of rows input, output, and rejected, as even error-free scripts can unintentionally drop rows. Importantly, the script must never overwrite existing files; instead, it should create new files to preserve data. This approach is employed by a small studio in Seoul, providing tailored solutions for repetitive spreadsheet chores, including the delivery of source code and a concise usage guide, often within a single day.
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