pandas.DataFrame.count#

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.

See also

Series.count

Number of non-NA elements in a Series.

DataFrame.value_counts

Count unique combinations of columns.

DataFrame.shape

Number of DataFrame rows and columns (including NA elements).

DataFrame.isna

Boolean same-sized DataFrame showing places of NA elements.

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