bigframes.pandas.DataFrame.count#

DataFrame.count(*, numeric_only: bool = False) → Series[source]#

Count non-NA cells for each column.

The values None, NaN, NaT, and optionally numpy.inf (depending on pandas.options.mode.use_inf_as_na) are considered NA.

Examples:

>>> df = bpd.DataFrame({"A": [1, None, 3, 4, 5],
...                     "B": [1, 2, 3, 4, 5],
...                     "C": [None, 3.5, None, 4.5, 5.0]})
>>> df
       A    B          C
0    1.0    1       <NA>
1   <NA>    2        3.5
2    3.0    3       <NA>
3    4.0    4        4.5
4    5.0    5        5.0

[5 rows x 3 columns]

Counting non-NA values for each column:

>>> df.count()
A    4
B    5
C    3
dtype: Int64

Parameters:

numeric_only (bool, default False) Include only float, int or boolean data.

Returns:

For each column/row the number of

non-NA/null entries. If level is specified returns a DataFrame.

Return type:

bigframes.pandas.Series