pyspark.pandas.DataFrame.sum

DataFrame.sum(axis: Union[int, str, None] = None, skipna: bool = True, numeric_only: bool = None, min_count: int = 0) → Union[int, float, bool, str, bytes, decimal.Decimal, datetime.date, datetime.datetime, None, Series]

Return the sum of the values.

Parameters

axis: {index (0), columns (1)}

Axis for the function to be applied on.

skipna: bool, default True

Exclude NA/null values when computing the result.

Changed in version 3.4.0: Added skipna to exclude.

numeric_only: bool, default None

Include only float, int, boolean columns. False is not supported. This parameter is mainly for pandas compatibility.

min_count: int, default 0

The required number of valid values to perform the operation. If fewer than

min_count non-NA values are present the result will be NA.

Returns

sum: scalar for a Series, and a Series for a DataFrame.

Examples

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, np.nan, 0.3, np.nan]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.sum()
a    6.0
b    0.4
dtype: float64

>>> df.sum(axis=1)
0    1.1
1    2.0
2    3.3
3    0.0
dtype: float64

>>> df.sum(min_count=3)
a    6.0
b    NaN
dtype: float64

>>> df.sum(axis=1, min_count=1)
0    1.1
1    2.0
2    3.3
3    NaN
dtype: float64

On a Series:

>>> df['a'].sum()
6.0

>>> df['a'].sum(min_count=3)
6.0
>>> df['b'].sum(min_count=3)
nan

pyspark.pandas.DataFrame.skew pyspark.pandas.DataFrame.std