How to Efficiently Count Term Occurrences by ID and Group in Pandas?

Retrieving Term Counts by ID and Group with Pandas' Groupby

Problem

Given a DataFrame containing columns for ID (id), group (group), and term (term), the goal is to efficiently count the occurrences of each term for each unique combination of ID and group.

Solution

Utilizing Pandas' powerful groupby and size functions, we can achieve this without resorting to loops:

df.groupby(['id', 'group', 'term']).size().unstack(fill_value=0)

Results

This operation produces a hierarchical MultiIndex DataFrame presenting the term counts:

</p>
<pre class="brush:php;toolbar:false">         term

group term1 term2 term3
id
1 3 2 0
2 2 1 1

Performance Analysis

Even for massive datasets with millions of rows, this vectorized approach demonstrates exceptional performance:

1,000,000 rows
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Elapsed time: 1.2 seconds

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