Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.
Pandas Series.argsort() function returns the indices that would sort the underlying data of the given 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() function to return the sequence of index which will sort the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([34, 5, 13, 32, 4, 15]) # Create the Index index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp'] # set the index sr.index = index_ # Print the series print(sr) |
Output :
Coca Cola 34 Sprite 5 Coke 13 Fanta 32 Dew 4 ThumbsUp 15 dtype: int64
Now we will use Series.argsort() function to return a sequence of indices which will sort the underlying data of the given series object.
# return the indices which will # sort the series result = sr.argsort() # Print the result print(result) # Let's sort the series using the result print(sr[result]) |
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 in the output, the Series.argsort() function has successfully returned a series object containing the indices which will sort the given series object.
Example #2 : Use Series.argsort() function to return the sequence of index which will sort the underlying data of the given series object.
# importing pandas as pd import pandas as pd # Creating the Series sr = pd.Series([11, 21, 8, 18, 65, 18, 32, 10, 5, 32, None]) # Create the Index # apply yearly frequency index_ = pd.date_range('2010-10-09 08:45', periods = 11, freq ='Y') # set the index sr.index = index_ # Print the series print(sr) |
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
Now we will use Series.argsort() function to return a sequence of indices which will sort the underlying data of the given series object.
# return the indices which will # sort the series result = sr.argsort() # Print the result print(result) # Let's sort the series using the result print(sr[result]) |
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 in the output, the Series.argsort() function has successfully returned a series object containing the indices which will sort the given series object. Notice the function has returned -1 as the index position for the missing values.
