Python | Pandas Series.argsort()
Last Updated :
27 Feb, 2019
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.
Python3
# 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)
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.
Python3 1==
# 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])
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.
Python3
# 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)
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.
Python3 1==
# 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])
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.