count(self, axis: 'Axis' = 0, level: 'Level | None' = None, numeric_only: 'bool' = False)

The values :None:None:`None`, NaN , :None:None:`NaT`, and optionally :None:None:`numpy.inf` (depending on :None:None:`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.

level : int or str, optional

If the axis is a MultiIndex (hierarchical), count along a particular :None:None:`level`, collapsing into a DataFrame . A :None:None:`str` specifies the level name.

numeric_only : bool, default False

Include only :None:None:`float`, :None:None:`int` or boolean data.

Returns

Series or DataFrame

For each column/row the number of non-NA/null entries. If :None:None:`level` is specified returns a DataFrame .

Count non-NA cells for each column or row.

See Also

DataFrame.isna

Boolean same-sized DataFrame showing places of NA elements.

DataFrame.shape

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

DataFrame.value_counts

Count unique combinations of columns.

Series.count

Number of non-NA elements in a Series.

Examples

Constructing DataFrame from a dictionary:

This example is valid syntax, but we were not able to check execution

>>> 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:

This example is valid syntax, but we were not able to check execution

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

Counts for each row:

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

pandas.core.generic.NDFrame.describe pandas.core.window.expanding.Expanding.count pandas.core.frame.DataFrame.nunique pandas.core.groupby.groupby.GroupBy.describe pandas.core.base.IndexOpsMixin.value_counts pandas.core.series.Series.count pandas.core.groupby.generic.SeriesGroupBy.describe pandas.core.window.rolling.Rolling.count

Local connectivity graph

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SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

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