bigframes.pandas.DataFrame.min#

DataFrame.min(axis: str | int = 0, *, numeric_only: bool = False) → Series[source]#

Return the minimum of the values over the requested axis.

If you want the index of the minimum, use idxmin. This is the equivalent of the numpy.ndarray method argmin.

Examples:

>>> df = bpd.DataFrame({"A": [1, 3], "B": [2, 4]})
>>> df
    A       B
0   1       2
1   3       4

[2 rows x 2 columns]

Finding the minimum value in each column (the default behavior without an explicit axis parameter).

>>> df.min()
A    1
B    2
dtype: Int64

Finding the minimum value in each row.

>>> df.min(axis=1)
0    1
1    3
dtype: Int64

Parameters:

• axis ({index (0), columns (1)}) Axis for the function to be applied on. For Series this parameter is unused and defaults to 0.

• numeric_only (bool, default False) Default False. Include only float, int, boolean columns.

Returns:

Series with the minimum of the values.

Return type:

bigframes.pandas.Series