Pandas GroupBy: When Should I Use `count()` vs. `size()`?

Understanding the Difference Between size and count in Pandas

In Pandas, groupby operations provide powerful tools for data exploration and aggregation. Among the commonly used groupby operations are count and size. Understanding their distinction is crucial to effectively analyze your data.

count vs. size

The count operation counts the number of non-null values within a group. In contrast, the size operation counts all values, including NaN values. This difference becomes evident when working with datasets containing missing values.

For instance, consider the following DataFrame:

df = pd.DataFrame({'a':[0,0,1,2,2,2], 'b':[1,2,3,4,np.NaN,4], 'c':np.random.randn(6)})

If we group by column 'a' and apply count to column 'b':

print(df.groupby(['a'])['b'].count())

We get the following output:

a
0    2
1    1
2    2
Name: b, dtype: int64

This shows that there are two non-null values for group 0, one for group 1, and two for group 2.

On the other hand, if we use size:

print(df.groupby(['a'])['b'].size())

We obtain:

a
0    2
1    1
2    3
dtype: int64

In this case, the result includes the NaN value in group 2, indicating that size accounts for all values.

Therefore, it becomes essential to choose between count and size based on the specific context and desired analysis. If you wish to exclude null values from your count, use count. If you need to account for all values, regardless of their presence or absence, use size.

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