pandas.DataFrame.count — pandas 2.2.3 documentation (original) (raw)

DataFrame.count(axis=0, numeric_only=False)[source]#

Count non-NA cells for each column or row.

The values None, NaN, NaT, pandas.NA are considered NA.

Parameters:

axis{0 or ‘index’, 1 or ‘columns’}, default 0

If 0 or ‘index’ counts are generated for each column. If 1 or ‘columns’ counts are generated for each row.

numeric_onlybool, default False

Include only float, int or boolean data.

Returns:

Series

For each column/row the number of non-NA/null entries.

Examples

Constructing DataFrame from a dictionary:

df = pd.DataFrame({"Person": ... ["John", "Myla", "Lewis", "John", "Myla"], ... "Age": [24., np.nan, 21., 33, 26], ... "Single": [False, True, True, True, False]}) df Person Age Single 0 John 24.0 False 1 Myla NaN True 2 Lewis 21.0 True 3 John 33.0 True 4 Myla 26.0 False

Notice the uncounted NA values:

df.count() Person 5 Age 4 Single 5 dtype: int64

Counts for each row:

df.count(axis='columns') 0 3 1 2 2 3 3 3 4 3 dtype: int64