![]() Performance: In complex workbooks, changing one number can affect hundreds of reiterative calculations and Excel takes time to work each one out.When this happens, the risk of data corruption goes way up. Scalability: Excel has a limit on the amount of rows and columns it can hold, but with datasets increasing at exponential speed, spreadsheets soon run out of memory or utilise most of the CPU.A few cases have been publicised where things have gone awry with a huge cost implications. Scarily, some of them go unseen until it is too late. Cascading Errors: Excel is notorious for errors being propagated down a column and then across the whole spreadsheet creating a snowball that turns into an avalanche of trouble.Here are some of the issues Excel power users face: ![]() Trying to use spreadsheets for advanced, responsive analytics over a large volume of data, is using the wrong tool for the job. Let’s see what makes Excel popular and where it falls through the cracks, but most importantly why □□□ should start using Jupyter Notebooks as an alternative powerful analytical tool. □□ Sounds familiar? You are probably nodding fervently.Īs useful as it can be, for delivering real insight from data, spreadsheets simply won’t provide all of the answers you seek. And when you dare to press F9 to refresh the results, you can grab a cuppa and still wait a bit more (if your computer has not crashed in between)! You can crunch a solution quickly but before you know it, your spreadsheet expands in tens of tabs, thousands of rows and spaghetti VBA - so its readability and maintenance goes right out the window. You are an expert in pivot tables, formulae, charts or even VBA and PowerQuery. If you are a trader or you are working in the financial services, Excel is your bread and butter you can analyse prices and other tick data, evaluate your trading portfolio, calculate VaR, perform back testing and the list goes on and on. ![]()
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