Why Calling .Sort() Function on Pandas Series Sorts Its Values In-Place and Returns Nothing

Why calling .sort() function on Pandas Series sorts its values in-place and returns nothing?

.sort() sorts in-place.

That means that after you call .sort(), your existing array has been sorted. It doesn't return anything.

To take an example from "core" Python:

In [175]: L = [2, 3, 1, 5]

In [176]: L.sort()

In [177]: print(L)
[1, 2, 3, 5]

It's the same for Pandas, as documented by Pandas.sort:

Sort values and index labels by value, in place. For compatibility with ndarray API. No return value

See also: What's the difference between Series.sort() and Series.order()?

Why does "return list.sort()" return None, not the list?

list.sort sorts the list in place, i.e. it doesn't return a new list. Just write

newList.sort()
return newList

Dataframe sort future warning - trying to sort by sort_values()

Your problem is here:

OutputDf = OutputDf.sort_values("Date", ascending = True, 
             inplace = True) 

When you're setting the parameter inplace=True, it's making the changes on the actual object that's calling the function, and it returns None.

So basically what you're doing is, you're sorting your dataframe, and then initializing the return value of None back to itself.

You can fix this by removing inplace=True

sorting in pandas not giving expected results

That is because when you access df.name[0], it points to the index of the row (check out by calling df.index). The index value of the row where name is jack is 0, since when you created the df the first instance was jack.

In order to access the first row on the sorted df, use .iloc for positional indexing (or .loc if you want a label-based indexing).

import pandas as pd
df = pd.DataFrame({'score' :[1,5,7,999], 'name':['jack','jill','chris','kevin']})
df.sort_values(by= 'score', ascending=False, inplace=True)
df
df.name.iloc[0]

This returns kevin.

Why does '.sort()' cause the list to be 'None' in Python?

Simply remove the assignment from

result = result.sort()

leaving just

result.sort()

The sort method works in-place (it modifies the existing list), so it returns None. When you assign its result to the name of the list, you're assigning None. So no assignment is necessary.

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But in any case, what you're trying to accomplish can easily (and more efficiently) be written as a one-liner:

max(len(Ancestors(T,x)) for x in OrdLeaves(T))

max operates in linear time, O(n), while sorting is O(nlogn). You also don't need nested list comprehensions, a single generator expression will do.

Sorting a pandas series

Use sort_values, i.e. means = means.sort_values(). [Pandas v0.17+]

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(Very old answer, pre-v0.17 / 2015)

pandas used to use order() method: means = means.order().

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