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The easiest way to obtain a list of unique values in a pandas DataFrame column is to use the function.

This tutorial provides several examples of how to use this function with the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'team': ['A', 'A', 'A', 'B', 'B', 'C'],
                   'conference': ['East', 'East', 'East', 'West', 'West', 'East'],
                   'points': [11, 8, 10, 6, 6, 5]})

#view DataFrame
df

        team	conference  points
0	A	East	    11
1	A	East	    8
2	A	East	    10
3	B	West	    6
4	B	West	    6
5	C	East	    5

Find Unique Values in One Column

The following code shows how to find the unique values in a single column of the DataFrame:

df.team.unique()

array(['A', 'B', 'C'], dtype=object)

We can see that the unique values in the team column include “A”, “B”, and “C.”

Find Unique Values in All Columns

The following code shows how to find the unique values in all columns of the DataFrame:

for col in df:
  print(df[col].unique())

['A' 'B' 'C']
['East' 'West']
[11  8 10  6  5]

Find and Sort Unique Values in a Column

The following code shows how to find and sort by unique values in a single column of the DataFrame:

#find unique points values
points = df.points.unique()

#sort values smallest to largest
points.sort()

#display sorted values
points

array([ 5,  6,  8, 10, 11])

Find and Count Unique Values in a Column

The following code shows how to find and count the occurrence of unique values in a single column of the DataFrame:

df.team.value_counts()

A    3
B    2
C    1
Name: team, dtype: int64

Additional Resources