Once our data is clean, we may want to arrange it.

• sort_values(): The sort_values() method sorts the Series according to its values. By default, it sorts the values in ascending order.
  For example:

• marks = pd.Series([85, 65, 95, 72])

• marks.sort_values()

output:

1    65

3    72

0    85

2    95

dtype: int64

We can sort the values in descending order using:

marks.sort_values(ascending=False)

output:

2    95

0    85

3    72

1    65

dtype: int64

• sort_index(): The sort_index() method sorts the Series according to its index rather than its values.
  For example:

marks = pd.Series([85, 65, 95], index=["C", "A", "B"])

marks.sort_index()

output:

A    65

B    95

C    85

dtype: int64

This becomes useful when we are working with custom indexes.

• nlargest(): The nlargest() method returns the largest n values from the Series.
  For example:

marks = pd.Series([85, 72, 90, 65, 95])

marks.nlargest(3)

output:

4    95

2    90

0    85

dtype: int64

Here, the 3 highest marks are returned.
This is useful when we want to find top performers.

• nsmallest(): The nsmallest() method returns the smallest n values from the Series.
  For example:

marks = pd.Series([85, 72, 90, 65, 95])

marks.nsmallest(3)

output:

3    65

1    72

0    85

dtype: int64

Here, the 3 lowest marks are returned.