What\'s the Difference Between `groupby().count()` and `groupby().size()` in Pandas?

Understanding the Distinction between Size and Count in Pandas

Pandas, a powerful Python library for data manipulation and analysis, offers flexible operations such as grouping data by categories. When working with grouped data, understanding the difference between the count and size methods is crucial.

Question: What separates groupby("x").count and groupby("x").size in Pandas? Does size merely exclude nulls?

Answer:

The distinction between count and size lies in their handling of NaN values:

• size: Includes NaN values, essentially providing the total number of observations in each group.
• count: Excludes NaN values, yielding the number of non-null observations for each group.

Example:

Consider the following Pandas DataFrame:

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

Evaluating the count and size methods on the 'b' column grouped by 'a':

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

Output:

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

a
0    2
1    1
2    3
dtype: int64

As evident, the count method excludes the NaN value in group 4 (where 'a' is 2), while the size method includes it.

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