This module provides combination of statistics as single operation.
For now it provides min/max (and optionally positive min) and indices of first occurrences (i.e., argmin/argmax) in a single pass.
- min_max(data, bool min_positive=False, bool finite=False)¶
Returns min, max and optionally strictly positive min of data.
It also computes the indices of first occurrence of min/max.
NaNs are ignored while computing min/max unless all data is NaNs, in which case returned min/max are NaNs.
The result data type is that of the input data, except for the following cases. For input using non-native bytes order, the result is returned as native floating-point or integers. For input using 16-bits floating-point, the result is returned as 32-bits floating-point.
>>> import numpy >>> data = numpy.arange(10)
Usage as a function returning min and max:
>>> min_, max_ = min_max(data)
Usage as a function returning a result object to access all information:
>>> result = min_max(data) # Do not get positive min >>> result.minimum, result.argmin 0, 0 >>> result.maximum, result.argmax 9, 10 >>> result.min_positive, result.argmin_positive # Not computed None, None
Getting strictly positive min information:
>>> result = min_max(data, min_positive=True) >>> result.min_positive, result.argmin_positive # Computed 1, 1
If finite is True, min/max information is computed only from finite data. Then, all result fields (include minimum and maximum) can be None when all data is infinity or NaN.
data – Array-like dataset
min_positive (bool) – True to compute the positive min and argmin Default: False.
finite (bool) – True to compute min/max from finite data only Default: False.
An object with minimum, maximum and min_positive attributes and the indices of first occurrence in the flattened data: argmin, argmax and argmin_positive attributes. If all data is <= 0 or min_positive argument is False, then min_positive and argmin_positive are None.
ValueError if data is empty