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 # postion 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.
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