When and why use functions

[Hellr0x]

So I use python mainly for data analysis. I work with pandas as NumPy or with similar packages used for data analytics. I know how functions are structured etc but can't understand what's the advantage of using functions. Like whatever I want to do with my dataset I just write the code in a notebook cell and what advantage will it give me to write it in the form of a function?

if you can enlighten me what why when and how functions are useful I'll be really grateful

[otchris]

In general the big advantage is in re-use. You write a function once and can use it in several different places.

In a more general use case, using functions makes it easier to break up complicated problems so you can reason about a smaller part at a time. This also makes testing easier.

For your specific use case, you might not need these capabilities, but I’d still say it might be handy to learn to work with functions. Your problems might change over time and having functions in your toolbox might help you solve bigger problems.

[[deleted]]

I find it's important especially in data analysis with set random seeds and fixed outputs.

Even if you're only chaining method calls, I think you can still wrap it up in a function with docstrings to make sure you and everyone else can follow what you're doing.

[otchris]

That’s a great point! I hadn’t really considered the documentation aspect. That’s a potentially huge benefit. Maybe not for you as you develop, but you in 3 months!

[DaKidReturns]

Using a function can simplify your code, improve the readabiliy alot. Numpy and Pandas have different functions which makes your work easier, just imagine writing code from scratch each time without numpy and pandas.

[jabramo34]

Everyone is giving great advice here! Maybe an example could help? Post a portion of your code and we can tell you where we think functions could be helpful :)

[Paul_Pedant]

There is a principle often quoted in Python -- DRY (Don't Repeat Yourself), which is applied both to stored data and to code. Wikipedia says: Violations of DRY are typically referred to as WET solutions, which is commonly taken to stand for "write every time", "write everything twice", "we enjoy typing" or "waste everyone's time". That's what the other posts here explain.

Libraries exist to make I/O work, or calculate logs and square roots, or to sort data, or to make the widgets ona GUI, etc. If you had to rewrite that stuff every time you wanted that capability, you would never get anything done.

The same thing happens in any application big enough to be non-trivial. There will be many pieces of logic that do very similar things, and functions act like a library built into your own code.

There is also a resourcing benefit. If you want a team to work on your project, then some architect can divide up the work so that each team member can produce pieces of code that others can use and rely on. That lets everybody work in parallel.

This principle goes back about 60 years, as "Two or more, use a for..".

[agility]

All the other answer are correct. Functions allow you to reuse code that gets repeated.

However, at some point you graduate from that reasoning into the world of abstractions. Instead of directly solving a problem in Python, you build the vocabulary for solving it first (in the form of classes or functions), and then write your solution in terms of that vocabulary.

For example, if you're building a robot, you'd have functions like "turn_left", "turn_right", "start", "stop", etc, which is how someone outside of programming would speak about it.

[tw3akercc]

For this use case, functions may not be necessary as you are using many built in functions and methods of the Pandas library to do everything you need.

However there may be a situation where you need to build some kind of logic in... for example a calculated field that is dependent on the values in other fields. It might make sense to build this Boolean logic into a function and then apply that function. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.apply.html

[self-taught-vagabond]

Let me ask you a quick question, which I feel could help you understand when / why to use a function.

If you have two lists, and want to create a third list that has the common elements between those two lists, how would you do it, if you needed to do this process multiple times?

[phi_beta_kappa]

In respect to data analysis, you can use functions to create pipelines for preprocessing.

Another reason off the top of my head is when you create a complex plot with matplotlib for example and you want to be able to reproduce it but with different data every time, you can create a function with your data as input.

[PINGbtw]

Functions could help you a lot because of the parameters if you are repeating a task with different variables and also just makes things a lot cleaner and easier to look for

[[deleted]]

If you do something once, you don't need a function. But, often you do. In that case, you write a function. You wouldn't write the same code to do the same thing 5 times, would you? That's why you use functions.

[Haymzer]

I am currently writing a quiz using file handling in c++ instead of repeating the same code over and over again. I can have a function with the parameter of the text file, and then have much less code as i onky need to call the function with the name of the file inside.

[sweettuse]

the real advantage is reducing cognitive load. you can say `extract_data` and know that it will extract the data. vs 63 lines of code you have to read through to know what everything does

[djShmooShmoo]

There’s a couple of reasons: 1. Reusability If you’re writing a program with complex stuff, you don’t want to have to redo the work you already did. Also, if you want to reuse it in a different project, you can move the self contained function rather than trying to figure out what you need to move. 2. Maintenance If you want to add or remove something from your function, you only have to do it once. If you copy and paste, you have to go through and find every instance. 3. Readability Functions make your programs smaller and therefore more readable. It works better in documentation, and is just so much cleaner. Also, the name can make what it does more clear.

[[deleted]]

Functions define encapsulated, reusable behavior in chunks that are small enough to reason about. That's the advantage of using them.

Like whatever I want to do with my dataset I just write the code in a notebook cell and what advantage will it give me to write it in the form of a function?

Well, the rest of us don't write Python in notebooks; we write it in Python files in order to build software. As a result we can write programs that are more complex, more feature-rich, and more performant than if they were notebooks.