Why don't people working in IB, PE, Corporate finance, etc. not use pandas Dataframes over Microsoft Excel?
I'm currently working on a FinTech related project and I wanted to know why so many people in the finance industry feel the need to use Microsoft Excel over Pandas dataframes for organizing data. I understand that maybe if you want to make a budget or do some simple accounting, Excel is easier on the eyes, but even usability is disputable. I have some experience with both and it surprises me how only data scientists use Python instead of literally anybody who wants to easily organize vast amounts of data and analyze it, because A.) Python makes functions/formulas easy and repeatable B.) It makes accessing outside data much easier So when investment bankers or equity researchers go about analyzing financial statements, why don't they just use Python for valuation techniques and then convert to Excel for ease of viewing? Is it simply because Excel has reputability in the industry, or is there something about Excel VBA and Macros that I'm missing out on?
Excel provides all what you need. No need to change the whole system. If you know what you are doing the models will work smooth. If you are doing weird things or trying to flex too much maybe Python would work better. But still this is not rocketscience, xls is more than enough
Yes Excel has everything you need but it seems like Python would be faster, and there isn't much of a learning curve either. Idk why, I'm not even a CS lover, but I think python is more practical and faster for doing any numerical analysis
Because financial statements aren't complex.
Pandas is great for data analysis, but financial statements isn't data (it's like 15 rows per statement).
How I'm thinking about it would be each row index would be a year, and a column would be revenue, gross profit, etc. Sounds like a pain in the ass to do, especially when you have to start connecting the statements. I guess you could use it to scrape data from the SEC and yahoo finance to automatically calculate EV, but Factset does it already.
Yes, but if you had to constantly analyze financial statements or were training some AI to do it, Pandas would be good. But the thing is, even in small scale, Pandas seems much more intuitive than Excel. All of the Excel functions seem kind of slow and counterproductive. I don't know much about both to be honest, though.
That's not what you do in traditional finance. The models are meant to represent an abstract way of how businesses function and how cash flows through said businesses. It's less science and more art. You don't need insane tools to do this, people did this stuff on the back of envelopes over dinner before we had digital spreadsheets.
Python's libraries are great for analysing vast records of data in a systematic way. It's also great for performing non-trivial calculations involving multiple variables and sequential transformations on those variables. It is not great for persisting a representational model of a business that's easily configurable and understandable to most office workers.
It's like suggesting someone who wants to paint a concept they have in mind to use one of https://www.amazon.co.uk/Wagner-Universal-Sprayer-590-FLEXiO/dp/B01BUDJ…</a">these. Maybe it'll get the same result but realistically? That's overkill and a completely unfit tool for the purpose of the task at hand. Don't get me wrong, learning to use Python's data analysis libraries will be useful at some point (especially for things that are beyond the scope of Excel). But, for financial modelling? For business planning? For strategy? It'll just slow you down.
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