pandas.DataFrame.count¶
- DataFrame.count(axis=0, level=None, numeric_only=False)[source]¶
Count non-NA cells for each column or row.
The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_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.
- levelint or str, optional
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame. A str specifies the level name.
- numeric_onlybool, default False
Include only float, int or boolean data.
- Returns
- Series or DataFrame
For each column/row the number of non-NA/null entries. If level is specified returns a DataFrame.
See also
Series.countNumber of non-NA elements in a Series.
DataFrame.value_countsCount unique combinations of columns.
DataFrame.shapeNumber of DataFrame rows and columns (including NA elements).
DataFrame.isnaBoolean 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