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 pdimport pandas as pd# define a numeric seriess = pd.Series([100, 200, 54.67, 300.12, 400])# print the unsorted seriess |
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 ordersorted_series = s.sort_values(ascending = True)# print the sorted seriessorted_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 pdimport pandas as pd# define a numeric seriess = pd.Series([100, 200, 54.67, 300.12, 400])# print the unsorted seriess |
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 ordersorted_series = s.sort_values(ascending = False)# printing the sorted seriessorted_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 pdimport pandas as pd#d efine a string series ss = pd.Series(["OS","DBMS","DAA", "TOC","ML"])# print the unsorted seriess |
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 ordersorted_series = s.sort_values(ascending = True)# printing the sorted seriessorted_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 npimport numpy as np# importing pandas as pdimport pandas as pd# define a numeric series# s with a NaNs = pd.Series([np.nan, 1, 3, 10, 5])# print the unsorted seriess |
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 inplaces.sort_values(ascending = False, inplace = True)# printing the sorted seriess |
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 npimport numpy as np# importing pandas as pdimport pandas as pd # define a numeric series# s with a NaNs = pd.Series([np.nan, 1, 3, 10, 5])# print the unsorted seriess |
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 firstsorted_series = s.sort_values(na_position = 'first')# printing the sorted seriessorted_series |
Output:
The output shows that the NaN (not a number) value is given the first place in the sorted series.
