Can Python+Pandas replace Excel+VBA in daily work?

There is a popular question on Zhihu: Can Python Pandas replace Excel VBA in daily work?

My suggestion is , the two are complementary, and no one replaces the other.

Use Python Pandas for complex data analysis and mining, and Excel VBA for daily simple data processing.

From the perspective of data processing and analysis capabilities, Python Pandas can definitely replace Excel VBA, and is far more powerful than the latter.

But from the perspective of convenience, dissemination, and market recognition, Excel VBA is still irreplaceable in the workplace.

Because Excel conforms to the usage habits of most people, the cost of use is lower.

Just like Photoshop can produce more professional photos, why do most people use Meitu Xiuxiu? The principle is the same.

From the perspective of market acceptance, there are three differences between Python and Excel.

Note that what I am talking about here is the difference, not the disadvantage, because there is no way to compare the advantages and disadvantages of different types of things.

1. The threshold for learning Python is relatively high

Although Python is the easiest to get started among programming languages, it is still a programming language that requires you to understand For most non-IT majors, the learning threshold for programming knowledge such as variables, functions, logical statements, classes, thread processes, etc. is quite high.

And learning Python data analysis is not only learning Python syntax itself, you also need to learn various data science libraries such as Pandas, Numpy, Matplotlib, SKlearn, etc. , because most data processing functions are wrapped in these libraries.

Many libraries are not easier to learn than Python itself, because the ecology of these large libraries is very complex. For example, Pandas has at least thousands of function methods, as well as countless parameters and logic, just like you are doing data analysis with the underlying code of Excel.

So Python is good at handling scenarios with high complexity, high repetition, and large amounts of data.

What about Excel? Almost most people who know something about computers can use it with zero threshold, or simply by watching some tutorials, they can use functions and pivot tables for data processing. The cost of entry-level learning is extremely low.

Of course, high-level operations and VBA also require time to study.

#2. The cost of using Python is relatively high

As mentioned before, Python is not like Excel, a graphical interface software. Ready to use, nothing will happen.

Python is more troublesome to use than Excel. It is possible that if you successfully run the code and transfer it to a colleague's computer, bugs will appear, because Python involves environment configuration, dependencies, and the syntax format is relatively different. Strict, an error will be reported if there is any error.

So many Python learners will stop at installation and configuration, bug handling, and give up before performing data analysis.

For Excel, these problems may not exist, or very few.

Almost everyone in the company, from the chairman and CEO to the lower-level employees, is using Excel. What you create with Excel can be synchronized to your leadership colleagues without any communication barriers. Even if you use complex functions such as VBA, you can easily explain it.

Of course, this does not involve complex development scenarios, just daily office data processing and collaboration. Excel is more practical than Python.

If you are running algorithms and writing automated tools, Python is definitely suitable.

3. Excel has been generalized in some application scenarios, resulting in path dependence

Most people have path dependence on Excel. Excel has been around for decades and has been widely used in all walks of life. It has accumulated a large amount of codes, formulas, processes, materials, etc. It is difficult to find alternatives overnight.

Excel is one of the most successful software in the world. Microsoft employs thousands of engineers every year to develop and maintain Excel, encapsulating Excel into the most convenient data tool for daily office work. In fact, what Microsoft considers is to meet the needs of 95% of people, and the remaining 5% can use Java, Python and other tools as much as they want.

So it doesn’t mean that the more powerful the function, the more we should use it, but also take into account the existing rules, experience, and market conditions to make the most appropriate choice.

Summary

To sum up, it is normal for most people to use Excel instead of Python for data analysis.

Because simple and effective things are often the most popular, Python has actually been working hard in this direction, and I believe its future will be extremely bright.

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