numpy.min#

numpy.min(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)[source]#

Return the minimum of an array or minimum along an axis.

Parameters:

aarray_like

Input data.

axisNone or int or tuple of ints, optional

Axis or axes along which to operate. By default, flattened input is used.

If this is a tuple of ints, the minimum is selected over multiple axes, instead of a single axis or all the axes as before.

outndarray, optional

Alternative output array in which to place the result. Must be of the same shape and buffer length as the expected output. See Output type determination for more details.

keepdimsbool, optional

If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array.

If the default value is passed, then keepdims will not be passed through to the min method of sub-classes of ndarray, however any non-default value will be. If the sub-class method does not implement keepdims any exceptions will be raised.

initialscalar, optional

The maximum value of an output element. Must be present to allow computation on empty slice. See reduce for details.

wherearray_like of bool, optional

Elements to compare for the minimum. See reduce for details.

Returns:

minndarray or scalar

Minimum of a. If axis is None, the result is a scalar value. If axis is an int, the result is an array of dimension a.ndim - 1. If axis is a tuple, the result is an array of dimension a.ndim - len(axis).

See also

max

The maximum value of an array along a given axis, propagating any NaNs.

nanmin

The minimum value of an array along a given axis, ignoring any NaNs.

minimum

Element-wise minimum of two arrays, propagating any NaNs.

fmin

Element-wise minimum of two arrays, ignoring any NaNs.

argmin

Return the indices of the minimum values.

nanmax, maximum, fmax

Notes

NaN values are propagated, that is if at least one item is NaN, the corresponding min value will be NaN as well. To ignore NaN values (MATLAB behavior), please use nanmin.

Dont use min for element-wise comparison of 2 arrays; when a.shape[0] is 2, minimum(a[0], a[1]) is faster than min(a, axis=0).

Examples

Try it in your browser!

>>> import numpy as np
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
       [2, 3]])
>>> np.min(a)           # Minimum of the flattened array
0
>>> np.min(a, axis=0)   # Minima along the first axis
array([0, 1])
>>> np.min(a, axis=1)   # Minima along the second axis
array([0, 2])
>>> np.min(a, where=[False, True], initial=10, axis=0)
array([10,  1])

>>> b = np.arange(5, dtype=np.float64)
>>> b[2] = np.nan
>>> np.min(b)
np.float64(nan)
>>> np.min(b, where=~np.isnan(b), initial=10)
0.0
>>> np.nanmin(b)
0.0

>>> np.min([[-50], [10]], axis=-1, initial=0)
array([-50,   0])

Notice that the initial value is used as one of the elements for which the minimum is determined, unlike for the default argument Pythons max function, which is only used for empty iterables.

Notice that this isnt the same as Pythons default argument.

>>> np.min([6], initial=5)
5
>>> min([6], default=5)
6

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