You can use the following basic syntax to find the unique values in a column of a pandas DataFrame and then sort them:

df['my_column'].drop_duplicates().sort_values()

This will return a pandas Series that contains each unique value in a column sorted in ascending order.

To instead sort the unique values in descending order, use **ascending=False**:

df['my_column'].drop_duplicates().sort_values(ascending=False)

The following example shows how to use this syntax in practice.

**Example: Find Unique Values in Pandas Column and Sort Them**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'], 'points': [5, 5, 9, 12, 12, 5, 10, 13, 13, 19]}) #view DataFrame print(df) team points 0 A 5 1 A 5 2 A 9 3 A 12 4 A 12 5 B 5 6 B 10 7 B 13 8 B 13 9 B 19

We can use the following syntax to get the unique values from the **points** column and then sort them in ascending order:

#get unique values in points column and sort them df['points'].drop_duplicates().sort_values() 0 5 2 9 6 10 3 12 7 13 9 19 Name: points, dtype: int64

The output displays each of the unique values in the **points** column sorted in ascending order:

- 5
- 9
- 10
- 12
- 13
- 19

We can also get the unique values in the points column sorted in descending order by specifying **ascending=False** within the **sort_values()** function:

#get unique values in points column and sort them in descending order df['points'].drop_duplicates().sort_values(ascending=False) 9 19 7 13 3 12 6 10 2 9 0 5 Name: points, dtype: int64

The output displays each of the unique values in the **points** column sorted in descending order:

- 19
- 13
- 12
- 10
- 9
- 5

**Note**: You can find the complete documentation for the pandas **drop_duplicates()** function here.

**Additional Resources**

The following tutorials explain how to perform other common functions in pandas:

Pandas: How to Select Unique Rows in DataFrame

Pandas: How to Get Unique Values from Index Column

Pandas: How to Count Unique Combinations of Two Columns