Series.argsort()
Pandas Series.argsort()
returns indexes that will sort the underlying data this series object.
Syntax: Series.argsort (axis = 0, kind = ’quicksort’, order = None)
Parameter:
axis: Has no effect but is accepted for compatibility with numpy.
kind: {’mergesort’, ’ quicksort ’,’ heapsort ’}, default’ quicksort ’
order: Has no effect but is accepted for compatibility with numpy.Returns: argsorted: Series, with -1 indicated where nan values are present
Example # 1: Use Series.argsort ()
to return an index sequence that will sort the underlying data of the given series object.
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Output:
Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64
We will now use Series.argsort ()
to return a sequence of indices that will sort the underlying data for this series object.
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Output:
Coca Cola 4 Sprite 1 Coke 2 Fanta 5 Dew 3 ThumbsUp 0 dtype : int64 Dew 4 Sprite 5 Coke 13 ThumbsUp 15 Fanta 32 Coca Cola 34 dtype: int64
As we can see from the output, Series.argsort ()
mustache Has successfully returned a series object containing the indexes that will sort the given series object.
Example # 2: Use Series.argsort ()
to return the index sequence which will sort the underlying data of this series object.
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Output:
2010-12-31 08:45:00 11.0 2011-12-31 08:45:00 21.0 2012 -12-31 08:45:00 8.0 2013-12-31 08:45:00 18.0 2014-12-31 08:45:00 65.0 2015-12-31 08:45:00 18.0 2016-12-31 08: 45:00 32.0 2017-12-31 08:45:00 10.0 2018-12-31 08:45:00 5.0 2019-12-31 08:45:00 32.0 2020-12-31 08:45:00 NaN Freq: A-DEC, dtype: float64
We will now use Series.argsort ()
to return a sequence of indices that will sort the underlying data of a given series object.
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Output:
2010-12-31 08:45:00 8 2011-12-31 08:45 : 00 2 2012-12-31 08:45:00 7 2013-12-31 08:45:00 0 2014-12-31 08:45:00 3 2015-12-31 08:45:00 5 2016-12 -31 08:45:00 1 2017-12-31 08:45:00 6 2018-12-31 08:45:00 9 2019-12-31 08:45:00 4 2020-12-31 08:45: 00 -1 Freq: A-DEC, dtype: int64 2018-12-31 08:45:00 5.0 2012-12-31 08:45:00 8.0 2017-12-31 08:45 : 00 10.0 2010-12-31 08:45:00 11.0 2013-12-31 08:45:00 18.0 2015-12-31 08:45:00 18.0 2011-12-31 08:45:00 21.0 2016-12 -31 08:45:00 32.0 2019-12-31 08:45:00 32.0 2014-12-31 08:45:00 65.0 2020-12-31 08:45:00 NaN dtype: float64
As we can see from the output, Series.argsort ()
has successfully returned a series object containing the indices that will sort this series object. Note that the function returned -1 as the index position for missing values.