Here an example. df1 will hold sorted dataframe and df will be intact import pandas as pd from datetime import datetime as dt df = pd.DataFrame(data=[22,22,3], index=[dt(2016, 11, 10, 0), dt(2016, 11, 10, 13), dt(2016, 11, 13, 5)], columns=[‘foo’]) df1 = df.sort_values(by=’foo’) print(df, df1) In the case below, df will hold sorted values import pandas … Read more
I found this in the docs: https://docs.python.org/3/tutorial/controlflow.html#for Python’s for statement iterates over the items of any sequence (a list or a string), in the order that they appear in the sequence. If you need to modify the sequence you are iterating over while inside the loop (for example to duplicate selected items), it is recommended … Read more
This is called the placement new operator. It allows you to supply the memory the data will be allocated in without having the new operator allocate it. For example: Foo * f = new Foo(); The above will allocate memory for you. void * fm = malloc(sizeof(Foo)); Foo *f = new (fm) Foo(); The above … Read more
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’ … Read more
sponge (in moreutils package in Debian/Ubuntu) reads input till EOF and writes it into file, so you can grep file and write it back to itself. Like this: grep ‘pattern’ file | sponge file
Intro to Algorithms from MIT Press qualifies QuickSort as in-place – it sorts the elements within the array with at most a constant amount of them outside the array at any given time. At the end of the day, people will always have differing opinions (is Top-Down Memoization considered Dynamic Programming? Not to some “classical” … Read more
If you want to apply mathematical operations to a numpy array in-place, you can simply use the standard in-place operators +=, -=, /=, etc. So for example: >>> def foo(a): … a += 10 … >>> a = numpy.arange(10) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> foo(a) >>> a … Read more
What does a &= b mean? This depends on the implementation for the types of a and b, but semantics are essentially that a &= b is the update of a with its “AND” for b. That “AND” operation could be a set intersection, a binary AND operation, or some other operation that can be … Read more
This is a copy of the explanation on github. There is no guarantee that an inplace operation is actually faster. Often they are actually the same operation that works on a copy, but the top-level reference is reassigned. The reason for the difference in performance in this case is as follows. The (df1-df2).dropna() call creates … Read more
I’d write it this way: a[i:j] = sorted(a[i:j]) It is not in-place sort either, but fast enough for relatively small segments. Please note, that Python copies only object references, so the speed penalty won’t be that huge compared to a real in-place sort as one would expect.