How Can Pandas Efficiently Count Terms within Grouped DataFrames?

Counting Terms in Grouped DataFrames: A Pandas Solution

This article addresses the challenge of counting terms within groups and summarizing the results in a DataFrame. With Pandas, this task can be elegantly solved without resorting to inefficient looping. Consider the following DataFrame:

df = pd.DataFrame([
    (1, 1, 'term1'),
    (1, 2, 'term2'),
    (1, 1, 'term1'),
    (1, 1, 'term2'),
    (2, 2, 'term3'),
    (2, 3, 'term1'),
    (2, 2, 'term1')
])

The goal is to group by 'id' and 'group' and count the occurrences of each 'term'. To achieve this, Pandas offers a concise solution:

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

This operation groups the DataFrame by 'id', 'group', and 'term' columns, counts the occurrences of each unique combination, and returns a summarized DataFrame with multi-index columns and a single value column named 'size' containing the counts. The 'unstack' function reshapes the DataFrame into a wide format, with one column for each unique term, as shown below:

id  group term   size
1   1     term1  3
    1     term2  2
    2     term3  1
2   2     term1  3

Timing Analysis

For larger datasets, understanding the performance characteristics of this solution is crucial. To assess this, consider a DataFrame with 1 million rows generated using the following code:

df = pd.DataFrame(dict(id=np.random.choice(100, 1000000),
                       group=np.random.choice(20, 1000000),
                       term=np.random.choice(10, 1000000)))

Profiling the grouping and counting operation reveals that it can efficiently handle even large datasets:

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

This performance is attributed to the optimized nature of Pandas' underlying grouping and aggregation mechanisms, making it an excellent tool for efficiently working with large datasets.

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