How to filter for distinct values with pandas DataFrame[].unique()

In Python pandas, you can use the unique() function to identify unique values in a column of a DataFrame. This makes it easy to get a quick overview of the different values within your dataset.

What is the syntax of pandas DataFrame[].unique()?

The basic syntax for using pandas unique() is simple. This is because the function doesn’t take any parameters:

DataFrame['column_name'].unique()

Keep in mind that unique() can only be applied to one column. Before calling the function, you’ll need to indicate which column you want to evaluate. The unique() function returns a numpy array containing all the different values in the order they appear, with duplicate values in the column removed. It doesn’t, however, sort the values.

How to use pandas DataFrame[].unique()

To use unique() in a pandas DataFrame, you need to first specify the column you want to check. In the following example, we’ll use a DataFrame that contains information about the age and city of residence of a group of individuals.

import pandas as pd
# Create a sample DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Edward'],
    'Age': [24, 27, 22, 32, 29],
    'City': ['New York', 'Los Angeles', 'New York', 'Chicago', 'Los Angeles']
}
df = pd.DataFrame(data)
print(df)

The resulting DataFrame looks like this:

Name  	Age       City
0    Alice    	24    New York
1    Bob   	27  	Los Angeles
2  Charlie    	22    	New York
3    David   	32    Chicago
4   Edward  	29  	Los Angeles

Now, let’s say we want to create a list of all the cities where the people in the DataFrame live. We can apply the pandas unique() function to the column that contains the cities.

# Find different cities
unique_cities = df['City'].unique()
print(unique_cities)

The output is a numpy array that lists each city once, showing that the individuals in the DataFrame are from a total of three cities: New York, Los Angeles and Chicago.

['New York' 'Los Angeles' 'Chicago']