Pandas – Get All Unique Values in a Column

In this tutorial, we will look at how to get a list of unique values in a pandas dataframe column. Additionally, we will also look at how to get a count of each unique value within the column and the total unique count.

First, let’s create a sample dataframe that we will be using throughout this tutorial for demonstrating the usage.

import pandas as pd

# dataframe of height and weight football players
df = pd.DataFrame({
    'Height': [167, 175, 170, 186, 190, 188, 158, 169, 183, 180],
    'Weight': [65, 70, 72, 80, 86, 94, 50, 58, 78, 85],
    'Team': ['A', 'A', 'B', 'B', 'B', 'C', 'A', 'C', 'B', 'C']
})

# display the dataframe
print(df)

Output:

   Height  Weight Team
0     167      65    A
1     175      70    A
2     170      72    B
3     186      80    B
4     190      86    B
5     188      94    C
6     158      50    A
7     169      58    C
8     183      78    B
9     180      85    C

Now we have a dataframe with 10 rows and three columns storing information on height, weight, and teams of top scorers in a football competition.

1. List of all unique values in a pandas dataframe column

You can use the pandas unique() function to get the different unique values present in a column. It returns a numpy array of the unique values in the column. For example, let’s see what are the unique values present in the column “Team” of the dataframe “df” created above.

# unique values in column "Team"
print(df['Team'].unique())
# check the return type
print(type(df['Team'].unique()))

Output:

['A' 'B' 'C']
<class 'numpy.ndarray'>

You can see we get a numpy array of all the unique values present in the column “Team” – “A”, “B”, and “C”.

For more on the pandas unique() function, refer to its documentation.

2. Count of unique values in a column

If you just need the count of unique values present in a pandas dataframe column, you can use the pandas nunique() function. It returns the number of unique values present in the dataframe as an integer. For example, let’s count the number of unique values in the column “Team” of the dataframe “df”.

# count of unique values 
print(df['Team'].nunique())

Output:

3

We get 3 as the output because there are three unique values present in the column “Team”. Note that you can use the nunique() function to count the unique values in a row as well.

3. Count of each unique value in a column

You can use the pandas value_counts() function to get the number of times each unique value occurs in a column. For example, let’s find the what’s the count of each unique value in the “Team” column.

# value counts of each unique value
print(df['Team'].value_counts())

Output:

B    4
A    3
C    3
Name: Team, dtype: int64

You can see that the value “B” occurs 4 times, and “A” and “C” occur 3 times each in the column “Team”. The value_counts() is a nice function to check the distribution of data points across a categorical field.

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having pandas version 1.0.5

Tutorials on common column operations in pandas –

• Pandas – Rename Column Names
• Select One or More Columns in Pandas
• Pandas – Get All Unique Values in a Column
• Pandas – Shift column values up or down
• Get Column Names as List in Pandas DataFrame
• Change Order of Columns of a Pandas DataFrame
• Pandas – Random Sample of Columns