Python vs. Excel: Use-case and Job Opportunities – Techbytes

Excel has undoubtedly been an essential tool for companies over the years. While it still has a place in every scientist and data analyst’s toolkit, Python is slowly becoming a favorite for many.

The demand for Microsoft excel skills is still high, and despite being in the game for over three decades, this spreadsheet software is going strong in a world with fast-paced tech changes. The financial sector still uses this seasoned tool for data analysis to organize and present data. With recent developments and updates, the software boasts more effective functionalities and user-friendliness for all businesses.

However, many companies are currently transitioning to Python from Excel. Python, created by a Dutch programmer named Guido van Rossum is a general-purpose, high-level programming language. The differences between Python and Excel in functionality have had software developers prefer the former to the latter, mainly because of its benefits.

In this article, we shall explore the differences between the two in data analytics. Let's explore how the two tools function across different dimensions to help you learn and grow professionally.

Using Excel vs. Using Python

As earlier mentioned, excel is a commonly used tool for analytical operations, mainly in the finance industry. Using Excel, however, requires VBAs application, making it more complex to use. VBAs are rather complicated, making excel a challenge to work with, especially when dealing with multiple operations in data analysis.

Python, on the other hand, is an open-source programming language, offering more benefits than excel. It allows volunteers to contribute and give regular code updates to improve its functionality.

This is unlike excel, where program updates are only provided to those who purchased the application, limiting its use.

In addition, Python comes with a vast array of pre-installed libraries, saving time for developers who would have otherwise created the projects from scratch.

• Functional Integrations

Integrating with other non-analytical and analytical software is a feature that a good data analysis software should have. Python, compared to excel, can integrate well with other programs, allowing users to import and export different file formats into Python.

A good example is Pythons compatibility with SQL syntax and running through its framework to extract tables and data into its environment. Python's environment also effectively automates tasks like importing and writing analyzed data into CSV or Excel.

The transition from excel to Python is justified from the functional integration angle. It is user-friendly such that both experienced analysts and newbies can understand and use the language without much difficulty.

On the other hand, Excel uses VBA language, a personalized platform using macros for task automation in data analysis. Automation of tasks in the python environment is much easier than using macros to do the same.

• Reproducibility

Reproducibility, in this case, basically means that any visualizations or analytics created should be straightforward for another person to reproduce. They need to re-run the process to end up with the same result and also walk through the steps followed to ensure accuracy.

The reproducibility concept becomes more important when you are relying on automation. When automation works correctly, it's bliss; however, the automated reports can be something else if it doesn't.

With excel, reproducibility has proven to be quite a challenge. At any scale, checking excel calculations in cells is impossible. While VBA makes reproducibility slightly better in excel, it would be best to invest in learning Python.

• Scale and Automation

Doing a quick, ad-hoc analysis of small data with excel is a walk in the park. However, moving to a larger scale is a nightmare as it can only support a certain amount of data.

Python scales up to your memory size and has numerous tools to support out-of-memory calculations or computations. It is also data-agnostic, meaning it works well with multiple data sources, unlike excel, which is both the data storage unit and computation engine.

It is also very stress-free to automate updates with Python. The fact that it connects directly to any data source means you can schedule a job that re-generates data with any updates made, do your calculations, and even create a report, saving you a lot of time. Excel does not automate updates hence demanding a lot of time and labor.

• Transferable skills

Python and excel differ in terms of transferability of skills. If you are good in excel, you're good in excel. The skills are not transferrable or applicable to anything else.

With Python, which is closer than excel to other programming languages, it is easier for you to pick up other languages along the way. It opens more doors for you than excel would.

Needless to say that the demand for Python is on the rise, rated as the fourth most popular and the first most wanted language in programming.

What Jobs can benefit from Python?

• Accounting

Python is a handy tool to implement for accountants and accounts managers to anyone dealing with massive datasets. Its knowledge allows for extraction and manipulation of large-scale data from numerous sources to help identify inconsistencies, which would consume a lot of time when using excel.

• Data analysts

If you are a data analyst not using Python, you are missing out. With a data analyst's job mostly entailing probing through vast amounts of data, Python helps in automation, thus saving on time, effort and increases productivity.

• Marketing

Are you a marketer? Python can help you too. It can help monitor campaigns, automate SEO processes, data collection automation, and automated error checks. Anything you would go to excel for can easily be done by simply writing a python code.

• Journalism

Python comes in handy for journalists who use data to tell stories. They can promptly sort through data or information, making them efficient, especially when writing to meet specific deadlines.

Conclusion

In conclusion, both Python and Excel are essential and valuable tools when it comes to data analysis. However, the main advantage with excel is its easy ramp-up and simplicity, while Python is a more robust solution for analytics and data science.

Excel is easier to use and best for small-scale and one-time data analysis, while Python is perfect for large-scale data analysis. It can be harder to learn Python as you will have to install several packages and set the right development environment on your Pc, while excel doesn't require much experience to use thanks to its graphic user interphase.