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Sort a Pandas Series in Python
  • Last Updated : 28 Jul, 2020

Series is a one-dimensional labeled array capable of holding data of the type integer, string, float, python objects, etc. The axis labels are collectively called index. 

Now, Let’s see a program to sort a Pandas Series.

For sorting a pandas series the Series.sort_values() method is used.

Syntax: Series.sort_values(axis=0, ascending=True, inplace=False, kind=’quicksort’, na_position=’last’)Sorted

Returns: Sorted series



Examples 1: Sorting a numeric series in ascending order.

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# importing pandas as pd
import pandas as pd
  
# define a numeric series
s = pd.Series([100, 200, 54.67
               300.12, 400])
  
# print the unsorted series
s

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Output:

series

Now we will use Series.sort_values() method to sort a numeric series in ascending order.

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# sorting series s with 
# s.sort_value() method in
# ascending order
sorted_series = s.sort_values(ascending 
                              = True)
# print the sorted series
sorted_series

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Output:

sorted series



From the output, we can see that the numeric series is sorted in ascending order.

Example 2:  Sorting a numeric series in descending order.

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# importing pandas as pd
import pandas as pd
  
# define a numeric series
s = pd.Series([100, 200, 54.67
               300.12, 400])
  
# print the unsorted series
s

chevron_right


Output:

series

Now we will use Series.sort_values() method to sort a numeric series in descending order.

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# sorting the series s with 
# s.sort_values() method
# in descending order
sorted_series = s.sort_values(ascending
                              = False)
# printing the sorted series
sorted_series

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Output:

sorted series

From the output, we can see that the numeric series is sorted in descending order.

Example 3: Sorting a series of strings.

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# importing pandas as pd
import pandas as pd
  
#d efine a string series s
s = pd.Series(["OS","DBMS","DAA",
               "TOC","ML"])
  
# print the unsorted series
s

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Output:

series

Now we will use Series.sort_values() method to sort a series of strings.

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# sorting the series s with 
# s.sort_values() method
# in ascending order
sorted_series = s.sort_values(ascending 
                              = True)
# printing the sorted series
sorted_series

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Output:

sorted series

From the output, we can see that the string series is sorted in a lexicographically ascending order.

Example 4: Sorting values inplace.

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# importing numpy as np
import numpy as np
  
# importing pandas as pd
import pandas as pd
  
# define a numeric series
# s with a NaN
s = pd.Series([np.nan, 1, 3,
               10, 5])
  
# print the unsorted series
s

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Output:

series

Now we will use Series.sort_values() method to sort values inplace

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# sorting the series s with 
# s.sort_values() method in 
# descending order and inplace
s.sort_values(ascending  = False,
                              inplace = True)
  
# printing the sorted series
s

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Output:

so

The output shows that the inplace sorting in the Pandas Series.

Example 5: Sorting values in the series by putting NaN first.

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# importing numpy as np
import numpy as np
  
# importing pandas as pd
import pandas as pd
   
# define a numeric series
# s with a NaN
s = pd.Series([np.nan, 1, 3,
               10, 5])
  
# print the unsorted series
s

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Output: 

series

Now we will use Series.sort_values() method to sort values in the series by putting NaN first.

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# sorting the series s with 
# s.sort_values() method in 
# ascending order with na 
# postion at first
sorted_series = s.sort_values(na_position =
                              'first')
  
# printing the sorted series
sorted_series

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Output:

sorted series

The output shows that the NaN (not a number) value is given the first place in the sorted series.

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