How to sort a Pandas Series?

Sorting a Pandas Series is a common data manipulation task. The sort_values() method provides flexible options for arranging data in ascending or descending order while preserving the original index associations.

Basic Sorting with sort_values()

The sort_values() method sorts a Series by its values and returns a new sorted Series ?

import pandas as pd

# Create an unsorted Series
numbers = pd.Series([18, 15, 66, 92, 55, 989])
print("Unsorted Pandas Series:")
print(numbers)

# Sort in ascending order (default)
sorted_asc = numbers.sort_values()
print("\nSorted in Ascending Order:")
print(sorted_asc)

Unsorted Pandas Series:
0     18
1     15
2     66
3     92
4     55
5    989
dtype: int64

Sorted in Ascending Order:
1     15
0     18
4     55
2     66
3     92
5    989
dtype: int64

Sorting in Descending Order

Use the ascending=False parameter to sort in descending order ?

import pandas as pd

numbers = pd.Series([18, 15, 66, 92, 55, 989])

# Sort in descending order
sorted_desc = numbers.sort_values(ascending=False)
print("Sorted in Descending Order:")
print(sorted_desc)

Sorted in Descending Order:
5    989
3     92
2     66
4     55
0     18
1     15
dtype: int64

Sorting with String Values

The sort_values() method also works with string data, sorting alphabetically ?

import pandas as pd

fruits = pd.Series(['banana', 'apple', 'cherry', 'date'])
print("Original Series:")
print(fruits)

sorted_fruits = fruits.sort_values()
print("\nSorted Alphabetically:")
print(sorted_fruits)

Original Series:
0    banana
1     apple
2    cherry
3      date
dtype: object

Sorted Alphabetically:
1     apple
0    banana
2    cherry
3      date
dtype: object

In-Place Sorting

Use inplace=True to modify the original Series instead of creating a new one ?

import pandas as pd

numbers = pd.Series([18, 15, 66, 92, 55])
print("Before in-place sorting:")
print(numbers)

# Sort in-place
numbers.sort_values(inplace=True)
print("\nAfter in-place sorting:")
print(numbers)

Before in-place sorting:
0    18
1    15
2    66
3    92
4    55
dtype: int64

After in-place sorting:
1    15
0    18
4    55
2    66
3    92
dtype: int64

Key Parameters

Conclusion

Use sort_values() to sort Pandas Series by values. The method preserves original indices and offers flexible options for ascending/descending order and in-place modification.