How to Count Unique Values Within Groups Using nunique() in Pandas?

Counting Unique Values per Group in Pandas with nunique

In pandas, counting unique values in a group is possible using the nunique() method. This is particularly useful when working with data where you need to determine the number of distinct values within specific categories or groups.

Problem:

Consider a DataFrame with the following data:

ID	domain
123	vk.com
123	vk.com
123	twitter.com
456	vk.com
456	facebook.com
456	vk.com
456	google.com
789	twitter.com
789	vk.com

The task is to count the unique IDs for each domain in this DataFrame.

Solution:

To count unique values per group, use the nunique() method with the desired grouping columns. In this case, the domain column represents the groups:

<code class="python">df = df.groupby('domain')['ID'].nunique()
print(df)</code>

Output:

domain	count
facebook.com	1
google.com	1
twitter.com	2
vk.com	3

Additional Considerations:

• To remove any single quotes (') from the domain values, use the str.strip("'") method before grouping.
• To preserve the column name, use the as_index=False argument in groupby() and pass the nunique() method to the agg() function.

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