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

Understanding the Distinction between Size and Count in Pandas

Data manipulation often involves utilizing Pandas' groupby function to aggregate data based on specific criteria. Two commonly used aggregation functions, count and size, provide different insights into the grouped data.

groupby("x").count vs. groupby("x").size

The fundamental difference between count and size lies in their treatment of missing values. count calculates the number of non-null values within a group, excluding any missing values (e.g., NaN or None). On the other hand, size calculates the total number of observations in a group, regardless of whether they contain missing values.

Example

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)})

Using count and size, we can observe the following:

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

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

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

# Output:
# a  
# 0    2
# 1    1
# 2    3
# dtype: int64  

As you can see, count excludes the missing value in group 2, resulting in a count of 2 for that group. In contrast, size includes the missing value, yielding a total count of 3. This distinction highlights the importance of understanding the behavior of these functions when dealing with missing data.

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