Sort a Pandas Series in Python
Last Updated :
05 Jul, 2021
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.
Python3
# 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
Output:
Now we will use Series.sort_values() method to sort a numeric series in ascending order.
Python3
# sorting series s with
# s.sort_value() method in
# ascending order
sorted_series = s.sort_values(ascending
= True)
# print the sorted series
sorted_series
Output:
From the output, we can see that the numeric series is sorted in ascending order.
Example 2: Sorting a numeric series in descending order.
Python3
# 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
Output:
Now we will use Series.sort_values() method to sort a numeric series in descending order.
Python3
# 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
Output:
From the output, we can see that the numeric series is sorted in descending order.
Example 3: Sorting a series of strings.
Python3
# 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
Output:
Now we will use Series.sort_values() method to sort a series of strings.
Python3
# 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
Output:
From the output, we can see that the string series is sorted in a lexicographically ascending order.
Example 4: Sorting values inplace.
Python3
# 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
Output:
Now we will use Series.sort_values() method to sort values inplace
Python3
# 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
Output:
The output shows that the inplace sorting in the Pandas Series.
Example 5: Sorting values in the series by putting NaN first.
Python3
# 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
Output:
Now we will use Series.sort_values() method to sort values in the series by putting NaN first.
Python3
# sorting the series s with
# s.sort_values() method in
# ascending order with na
# position at first
sorted_series = s.sort_values(na_position =
'first')
# printing the sorted series
sorted_series
Output:
The output shows that the NaN (not a number) value is given the first place in the sorted series.