Sorting rows in pandas DataFrame

Sorting rows in a Pandas DataFrame means rearranging rows based on the values of one or more columns. Pandas provides the sort_values() method to do this efficiently.

In this example, we sort movies by their release year in ascending order.

 Movie Year
0 The Godfather 1972
2 Titanic 1997
1 Inception 2010

Explanation:

• sort_values(by='Year') sorts rows using the values in the Year column.
• Movies are arranged from the oldest (1972) to the most recent (2010).

Syntax

DataFrame.sort_values(by, axis=0, ascending=True, inplace=False, na_position='last')

Parameters:

• by: column label(s) to sort by.
• axis: 0 for rows (default), 1 for columns.
• ascending: True = smallest first, False = largest first.
• na_position: 'first' puts NaN first, 'last' puts them last.

Return Value: Returns a DataFrame with rows sorted by the given column(s).

Examples

Example 1: In this example, we sort student scores in Science from highest to lowest.

 Name Science
1 Marsh 9
0 Simon 7
3 Selena 7
2 Alex 4

Explanation:

• ascending=False sorts Science marks from highest to lowest.
• Marsh with 9 comes first, Alex with 4 comes last.

Example 2: This code sorts students first by Maths, then by English, both in ascending order.

 Name Maths English
1 Marsh 5 4
2 Gaurav 6 7
0 Simon 8 7
3 Alex 9 6

Explanation:

• Sorting happens by Maths first.
• If two rows have the same Maths score, they are sorted by English.

Example 3: In this example, we sort by Science but display missing values (NaN) first.

 Name Science
1 Marsh NaN
2 Alex 4.0
0 Simon 7.0
3 Selena 9.0

Explanation:

• na_position='first' places NaN values before numeric values.
• Marsh with missing Science score comes first.