numpy argsort ignore nan

stackoverflow.com › questions › 21189548 › python-sorting-an-array-with-nans

Not sure if it can be done with numpy.sort, but you can use numpy.argsort for sure:

>>> arr
array([[ 105.,    4.],
       [  53.,  520.],
       [ 745.,  902.],
       [  19.,   nan],
       [ 184.,   nan],
       [  22.,   10.],
       [ 104.,   26.]])
>>> arr[np.argsort(arr[:,1])]
array([[ 105.,    4.],
       [  22.,   10.],
       [ 104.,   26.],
       [  53.,  520.],
       [ 745.,  902.],
       [  19.,   nan],
       [ 184.,   nan]])

Answer from alko on Stack Overflow

Stack Overflow

stackoverflow.com › questions › 21189548 › python-sorting-an-array-with-nans

Python: sorting an array with NaNs - Stack Overflow

Top answer

1 of 5

7

Not sure if it can be done with numpy.sort, but you can use numpy.argsort for sure:

>>> arr
array([[ 105.,    4.],
       [  53.,  520.],
       [ 745.,  902.],
       [  19.,   nan],
       [ 184.,   nan],
       [  22.,   10.],
       [ 104.,   26.]])
>>> arr[np.argsort(arr[:,1])]
array([[ 105.,    4.],
       [  22.,   10.],
       [ 104.,   26.],
       [  53.,  520.],
       [ 745.,  902.],
       [  19.,   nan],
       [ 184.,   nan]])

2 of 5

5

You can create a masked array:

a = np.loadtxt('test.txt')

mask = np.isnan(a)
ma = np.ma.masked_array(a, mask=mask)

And then sort a using the masked array:

a[np.argsort(ma[:, 1])]

Stack Overflow

stackoverflow.com › questions › 74131398 › ignore-nan-values-when-ranking-using-argsort

numpy - Ignore nan values when ranking using argsort - Stack Overflow

I have a below function that ranks 3rd column of 2D arrays inside of a three-dimensional array. arr[:, :, 3] = arr[:, :, 3].argsort(axis=1)[:, ::-1].argsort(axis=1) + 1 The problem is that nan val...

NumPy

numpy.org › doc › stable › reference › generated › numpy.argsort.html

numpy.argsort — NumPy v2.4 Manual

As of NumPy 1.4.0 argsort works with real/complex arrays containing nan values.

GitHub

github.com › pandas-dev › pandas › issues › 12694

BUG: Series.argsort() incorrect handling of NaNs · Issue #12694 · pandas-dev/pandas

March 22, 2016 - It appears that Series.argsort() is implemented as s.dropna().argsort().reindex(s.index, fill_value=-1) = np.argsort(s.dropna()).reindex(s.index, fill_value=-1). There are two problems with this: (a) Since the result represents integer indices into the original series s, the result should not have the same index as s.index -- it should either be a Series with index [0, 1, ...], or more likely simply be a NumPyarray; (b) The way NaNs are effectively removed before calling np.argsort() leads to indexes that are no longer appropriate into the original Series, resulting in the nonsensical results shown in [9] and [10] below.

Author seth-p

Omz Software

omz-software.com › pythonista › numpy › reference › generated › numpy.argsort.html

numpy.argsort — NumPy v1.8 Manual

As of NumPy 1.4.0 argsort works with real/complex arrays containing nan values.

Pandas

pandas.pydata.org › docs › reference › api › pandas.Series.argsort.html

pandas.Series.argsort — pandas 3.0.1 documentation - PyData |

Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. ... Unused. Parameter needed for compatibility with DataFrame. kind{‘mergesort’, ‘quicksort’, ‘heapsort’, ‘stable’}, default ‘quicksort’ · Choice of sorting algorithm.

TutorialsPoint

tutorialspoint.com › how-to-leave-nan-as-nan-in-the-pandas-series-argsort-method

How to leave nan as nan in the pandas series argsort method?

March 9, 2022 - If the Series object contains any null values or missing values, then the argsort() method gives -1 to indicate the index of that missing value (Nan value). Unfortunately, the argsort method doesn't have any parameters to skip the Null values. If you want to change the default representation ...

Practiceprobs

practiceprobs.com › problemsets › python-numpy › proficient

Proficient | Python NumPy - Practice Probs

There are numerous ways to exclude NaNs or treat them as 0s.

Pandas

pandas.pydata.org › pandas-docs › stable › reference › api › pandas.Series.argsort.html

pandas.Series.argsort — pandas 2.3.3 documentation

Argsorts the value, omitting NA/null values, and places the result in the same locations as the non-NA values. ... Unused. Parameter needed for compatibility with DataFrame. kind{‘mergesort’, ‘quicksort’, ‘heapsort’, ‘stable’}, default ‘quicksort’ · Choice of sorting algorithm.

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Omz Software

omz-software.com › pythonista › numpy › reference › generated › numpy.sort.html

numpy.sort — NumPy v1.8 Manual

If both the real and imaginary parts are non-nan then the order is determined by the real parts except when they are equal, in which case the order is determined by the imaginary parts. Previous to numpy 1.4.0 sorting real and complex arrays containing nan values led to undefined behaviour.

CopyProgramming

copyprogramming.com › howto › using-numpy-argpartition-ignoring-nans

NumPy argpartition: Complete Guide to Partial Sorting, NaN Handling & 2026 Best Practices - Numpy argpartition complete guide to partial sorting

January 9, 2026 - NaN values require explicit handling: Count and exclude them or pre-filter before partitioning to prevent incorrect results. Combine argpartition + argsort for sorted top-K results in O(n + k log k) complexity, delivering 7-25x speedups over pure argsort on large datasets.

NumPy

numpy.org › doc › 2.1 › reference › generated › numpy.argsort.html

numpy.argsort — NumPy v2.1 Manual

As of NumPy 1.4.0 argsort works with real/complex arrays containing nan values.

Stack Overflow

stackoverflow.com › questions › 35038675 › reverse-sort-of-numpy-array-with-nan-values

python - Reverse sort of Numpy array with NaN values - Stack Overflow

Top answer

1 of 4

7

I think you're probably right -- there is no built in special method to do this. But you could do it in two steps as follows by rolling your NaNs into the place you want them:

a = np.array([  1.,  -1.,   np.nan,  0.,  np.nan], dtype=np.float32)
sa = np.sort(a)[::-1]
np.roll(sa,-np.count_nonzero(np.isnan(a)))

array([  1.,   0.,  -1.,  nan,  nan], dtype=float32)

2 of 4

6

What about negating the values twice:

>>> a = np.array([2., -1., nan,  0., nan])
>>> np.sort(a)
array([ -1.,   0.,   2.,  nan,  nan])
>>> -np.sort(-a)
array([  2.,   0.,  -1.,  nan,  nan])

NumPy

numpy.org › doc › stable › reference › generated › numpy.sort.html

numpy.sort — NumPy v2.4 Manual

where R is a non-nan real value. Complex values with the same nan placements are sorted according to the non-nan part if it exists.