Listen, I’m not a coder. I’m not against coding. In fact, I have a lot of respect for people who are capable of coding. I just don’t think people who don’t code for a living, should be asked to code to make their living. Then again, I don’t think people should have pet Pythons either. And, hundreds of thousands of people do.
But if your boss wants you to use Python, I’m here to help. If your boss wants you to get a pet Python, get a new job (unless you’re a zookeeper, in which case…cool!).
What is Python?
According to The Python Software Foundation, Python is interpreted, object-oriented, high-level programming language with dynamic semantics. There are many programming languages, but Python has become very popular due to its relatively easy to learn syntax and the fact that it supports modules and packages. Add in a cool name and a standard library available without cost (it’s free!) and it’s no wonder why you may have an Office Space moment when your boss asks for TPS reports (yes, they’re a real thing!) and says something like “Ummmm, I’m gonna need you to go ahead and start learning to code, mmmk…that would be great.”
Drawbacks of Python
If you’re looking for drawbacks of Python on the internet, you may find things like Python consumes memory and it relies on trusting the open-source community. I’m going to assume that you don’t have a Commodore 64 and that the open-source community are a bunch of well-meaning contributors, not Pinky and the Brain.
Here’s what I believe are the two critical challenges to using Python:
Learning Python requires training and practice
Depending on who you ask, learning Python can take anywhere from 3 to 6 months, which doesn’t make you proficient using it to prepare and analyze data. Compare that to learning Minitab Statistical Software which takes a few days of training (if you aren’t already proficient). Even after you’re trained, coding mistake free is nearly impossible for anyone, let alone a novice. Not only will mistakes require debugging and increase the time it takes to do your analysis, but worse, an accidental error could give you a “wrong answer,” defeating the purpose of your analysis.
Python is a time suck
If your coding is error-prone, naturally, simple analyses will take a long time. However, even if you become a solid programmer, the code required to wrangle, prepare and analyze data is much lengthier than point-and-click software. And even if you use a large language model to expedite your coding, there are many details that require time and review, like filling in missing values that the coding cannot read. If the saying about “time is money” is one of your boss’ favorites, this might be a time to remind them to read Advice to a Young Tradesman (famous for Franklin’s lesson: “Remember that Time is Money.”).
Click here to watch our webinar: Building Predictive Analytics Models: Python vs. Minitab
Sure…But Python is Free! But Not if You Consider the “Costs.”
Technically, Python is free, but there are two significant costs: training and opportunity cost. Sure, you could teach yourself Python, but the reality is most folks learning coding (if they haven’t in school) will look for training courses or boot camps, which cost money. The much larger cost is your time. Wouldn’t your time be better served learning, improving, and advancing your analytical skills to make better decisions, versus learning to code? Is the time spent accessing and preparing your data a better use than performing tasks, planning, or working on projects that your job requires?
Value Add Versus Non-Value Add Activities
According to Six Sigma Daily, Lean provides straightforward guidelines that for something to be add value, three things must happen:
- The step must change the form or function of the product or service.
- The customer must be willing to pay for the change.
- The step must be performed correctly the first time.
If learning to code simply helps you complete the same task, in a longer-period of time, with no value to the customer, and carries more risk with performing the analysis correctly – due to the need for training – isn’t the more important argument against adopting Python simple? It’s non-value activity. And isn’t the whole purpose of continuous improvement to eliminate those activities? Just sayin’.
How Minitab Facilitates Collaboration with Python
As a budget holder, I can appreciate hearing that something is “free” and quickly informing my team to investigate it as an alternative to something else they’re doing. However, I’ve also learned that “free” rarely comes without some drawbacks, as I’m sure your boss has too. Highlighting some of the complexities associated with Python might help you keep your software, and, in turn, increase your productivity, which is a win-win for everyone involved.
Further, if the goal of your manager is to facilitate more collaboration between the data scientists who live in Python and other folks, it might be good to know that Python can be installed in Minitab Statistical Software. Alternatively, if there is a very specific algorithm or visual that sits in Python, it is very easy to incorporate it into Minitab. To learn more about how to use the new Minitab/Python Integration Module, watch our free webinar
Minitab solutions can offer the ease, efficiency and repeatability of problem solving – while enabling collaboration and access to the Python library.
The Python integration offers the flexibility of custom Python code within Minitab’s easy-to-use interface, and the results can be saved, stored, and shared in Minitab Project Files.
Click below for more information on Minitab’s Python module as well as what other Minitab products can help accelerate your business.
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