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

Distinguishing Pandas's 'size' and 'count' for Grouping Operations

When working with pandas's groupby() function, it's crucial to understand the distinction between 'size' and 'count'. These functions seemingly produce similar results when applied to group counts, but there's a subtle difference that can impact your data analysis.

The 'count' function specifically counts the number of non-null values in a group. This means that if there are any missing values (NaN or None) in a group, they will be excluded from the count. This behavior ensures you only consider valid observations when calculating group counts.

On the other hand, the 'size' function counts the total number of observations in a group, including those with missing values. This means that both valid and invalid observations are counted, giving you a broader picture of the group's size.

To illustrate this difference, consider the following example:

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

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

The output will be:

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

a
0    2
1    1
2    3
dtype: int64

As you can see, the 'count' function excludes the NaN value in group 'a=2', while the 'size' function includes it. This distinction is crucial when your dataset contains missing data and you need to handle it appropriately for your analysis.

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