modin.pandas.DataFrame.count

DataFrame.count(axis: Axis | None = 0, numeric_only: bool = False)[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/modin/plugin/extensions/base_overrides.py#L706-L720)

Count non-NA cells for each column or row.

The values None, NaN, NaT 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. Not supported yet.

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

Returns:

For each column/row the number of non-NA/null entries.

Return type:

Snowpark pandas Series

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
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Notice the uncounted NA values:

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