dask.dataframe.DataFrame.count

DataFrame.count(axis=None, split_every=False, numeric_only=None)

Count non-NA cells for each column or row.

This docstring was copied from pandas.core.frame.DataFrame.count.

Some inconsistencies with the Dask version may exist.

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 (Not supported in Dask)

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.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