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

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