# Pandas: Find Unique Values in Column and Sort Them

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.