Get Unique Values from a Column in Pandas DataFrame

Retrieving unique values from a column in a Pandas DataFrame helps identify distinct elements, analyze categorical data, or detect duplicates. For example, if a column B contains ['B1', 'B2', 'B3', 'B4', 'B4'], the unique values are ['B1', 'B2', 'B3', 'B4'].

Here is the sample DataFrame used in this article:

import pandas as pd
data = {'A': ['A1', 'A2', 'A3', 'A4', 'A5'],
        'B': ['B1', 'B2', 'B3', 'B4', 'B4'],
        'C': ['C1', 'C2', 'C3', 'C3', 'C3'],
        'D': ['D1', 'D2', 'D2', 'D2', 'D2'],
        'E': ['E1', 'E1', 'E1', 'E1', 'E1']}
        
df = pd.DataFrame(data)
print(df)

    A   B   C   D   E
0  A1  B1  C1  D1  E1
1  A2  B2  C2  D2  E1
2  A3  B3  C3  D2  E1
3  A4  B4  C3  D2  E1
4  A5  B4  C3  D2  E1

Let's explore different methods to get unique values from a column in Pandas.

Using unique() method

The unique() method returns a NumPy array. The order of the unique values is preserved based on their first occurrence.

Example: In this example, we retrieve the unique values from column 'B'.

unique_B = df['B'].unique()
print(unique_B)

Output

['B1' 'B2' 'B3' 'B4']

Using nunique()

The nunique() method counts the number of unique values in a column. It is useful when you only need the count of distinct values rather than the values themselves.

Example: Here we get the number of unique values in columns 'A', 'B', 'C', and 'D'.

print("Unique count in A:", df['A'].nunique())
print("Unique count in B:", df['B'].nunique())
print("Unique count in C:", df['C'].nunique())
print("Unique count in D:", df['D'].nunique())

Output

Unique count in A: 5
Unique count in B: 4
Unique count in C: 3
Unique count in D: 2

Using drop_duplicates()

The drop_duplicates() method removes duplicate values in the specified column, returning a DataFrame with only the unique values. The index of the original DataFrame is preserved.

Example: This code retrieves unique values from column 'C'.

unique_C = df['C'].drop_duplicates()
print(unique_C)

Output

0 C1
1 C2
2 C3
Name: C, dtype: object

Using value_counts()

The value_counts() method counts the occurrences of each unique value in the column and returns the result as a Series.

Example: Here we count unique values in column 'D' and also extract only the unique values.

counts_D = df['D'].value_counts()
unique_D = counts_D.index
print("Value counts in D:\n", counts_D)
print("Unique values in D:", list(unique_D))

Output

Value counts in D:
D
D2 4
D1 1
Name: count, dtype: int64
Unique values in D: ['D2', 'D1']

Using set()

You can also use Python’s built-in set() function, which converts the column values into a set, automatically removing duplicates.

Example: Here we get unique values from column 'D' using set().

unique_set_D = set(df['D'])
print(unique_set_D)

Output

{'D2', 'D1'}