34

Pandas operations usually create a copy of the original dataframe. As some answers on SO point out, even when using inplace=True, a lot of operations still create a copy to operate on.

Now, I think I'd be called a madman if I told my colleagues that everytime I want to, for example, apply +2 to a list, I copy the whole thing before doing it. Yet, it's what Pandas does. Even simple operations such as append always reallocate the whole dataframe.

Having to reallocate and copy everything on every operation seems like a very inefficient way to go about operating on any data. It also makes operating on particularly large dataframes impossible, even if they fit in your RAM.

Furthermore, this does not seem to be a problem for Pandas developers or users, so much so that there's an open issue #16529 discussing the removal of the inplace parameter entirely, which has received mostly positive responses; some started getting deprecated since 1.0. It seems like I'm missing something. So, what am I missing?

What are the advantages of always copying the dataframe on operations, instead of executing them in-place whenever possible?

Note: I agree that method chaining is very neat, I use it all the time. However, I feel that "because we can method chain" is not the whole answer, since Pandas sometimes copies even in inplace=True methods, which are not meant to be chained. So, I'm looking some other answers for why this would be a reasonable default.

10
  • 7
    So as the issue of removing inplace mentions the reason it's being removed is that it is a misnomer. It does create a copy it just hides away the reassignment. There is almost no difference between df = df.some_operation) and df.some_operation(inplace=True) There are (almost) no true inplace operations. In my opinion, this question is a great reason for removing the inplace parameter, because it makes people think they're not making copies when they are.
    – Henry Ecker
    Nov 15, 2021 at 4:43
  • 3
    "inplace does not generally do anything inplace but makes a copy and reassigns the pointer" github.com/pandas-dev/pandas/issues/… and "there are absolutely no performance benefits to using inplace=True" from the linked answer by cs95
    – Henry Ecker
    Nov 15, 2021 at 4:44
  • 1
    @HenryEcker that isn't true. There's a big difference, df = df.some_operation() is not the same as df.some_operation(inplace=True), because for the latter, ever other place the dataframe is being referred to it changes, in the former, it doesn't. Of course, the underlying buffer may or may not be re-allocated. Nov 15, 2021 at 4:53
  • 9
    I don't really know if this question is answerable in some ways... We could get into how Pandas DataFrames actually store data and how individual block managers are used to group collections of variables of the same type into ndarrays by dtype and how it is not reasonable to reshape a DataFrame without rebuilding the block managers to structure them in the correct sequence to reduce total overall memory and fragmentation. But these are largely design decisions and the library could have been designed differently...
    – Henry Ecker
    Nov 15, 2021 at 4:54
  • 1
    @juanpa.arrivillaga Okay. Fair enough. What I meant was there is almost no difference in the number of copies or memory needed to do an "inplace" operation vs a not "inplace" operation given the context of pandas and standard use cases. (not considering the actual program logic that may or may not apply to overwriting the self of a class instance like the inplace operations do)
    – Henry Ecker
    Nov 15, 2021 at 4:56

1 Answer 1

9

As evidenced here in the pandas documentation, "... In general we like to favor immutability where sensible." The Pandas project is in the camp of preferring immutable (stateless) objects over mutable (objects with state) to guide programmers into creating more scalable / parallelizable data processing code. They are guiding the users by making the 'inplace=False' behavior the default.

In this software engineering stack exchange Peter Torok discusses the pros and cons between mutable and immutable object programming really nicely. https://softwareengineering.stackexchange.com/a/151735

In summary some software engineers feel that objects that are immutable (unchanging) lead to

  • less errors in the code - because object states are easy to lose track of and hard to track down
  • increased scalability - it is easier to write multithreaded code, since one thread will not inadvertently modify the value contained by an object in another thread
  • more concise code - since code is forced to be written in a functional programming and more mathematical style

I will agree that this does have it's inefficiencies since constantly making copies of the same objects for minor changes does not seem ideal. It has other benefits noted above.

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.