pyspark.pandas.DataFrame.min

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

Return the minimum 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: Supported including NA/null values.

numeric_only: bool, default None

If True, include only float, int, boolean columns. This parameter is mainly for pandas compatibility. False is supported; however, the columns should be all numeric or all non-numeric.

Returns

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

Examples

>>> df = ps.DataFrame({'a': [1, 2, 3, np.nan], 'b': [0.1, 0.2, 0.3, np.nan]},
...                   columns=['a', 'b'])

On a DataFrame:

>>> df.min()
a    1.0
b    0.1
dtype: float64

>>> df.min(axis=1)
0    0.1
1    0.2
2    0.3
3    NaN
dtype: float64

On a Series:

>>> df['a'].min()
1.0

pyspark.pandas.DataFrame.mean pyspark.pandas.DataFrame.median