Python numpy max vs amax vs 最大值

numpy max vs amax vs maximum

提问by DilithiumMatrix

numpy has three different functions which seem like they can be used for the same things --- except that numpy.maximumcan onlybe used element-wise, while numpy.maxand numpy.amaxcan be used on particular axes, or all elements. Why is there more than just numpy.max? Is there some subtlety to this in performance?

numpy的具有看起来他们可被用于同样的东西三个不同的函数---不同之处在于numpy.maximum可仅被用于逐元素，而numpy.max且numpy.amax可以在特定轴，或所有元件一起使用。为什么不止如此numpy.max？这在性能上有什么微妙之处吗？

(Similarly for minvs. aminvs. minimum)

（类似于minvs. aminvs. minimum）

采纳答案by Alex Riley

np.maxis just an alias for np.amax. This function only works on a singleinput array and finds the value of maximum element in that entire array (returning a scalar). Alternatively, it takes an axisargument and will find the maximum value along an axis of the input array (returning a new array).

np.max只是 的别名np.amax。此函数仅适用于单个输入数组，并在整个数组中查找最大元素的值（返回标量）。或者，它接受一个axis参数并将沿输入数组的轴找到最大值（返回一个新数组）。

>>> a = np.array([[0, 1, 6],
                  [2, 4, 1]])
>>> np.max(a)
6
>>> np.max(a, axis=0) # max of each column
array([2, 4, 6])

The default behaviour of np.maximumis to take twoarrays and compute their element-wise maximum. Here, 'compatible' means that one array can be broadcast to the other. For example:

的默认行为np.maximum是采用两个数组并计算它们的元素最大值。在这里，“兼容”意味着一个数组可以广播到另一个数组。例如：

>>> b = np.array([3, 6, 1])
>>> c = np.array([4, 2, 9])
>>> np.maximum(b, c)
array([4, 6, 9])

But np.maximumis also a universal functionwhich means that it has other features and methods which come in useful when working with multidimensional arrays. For example you can compute the cumulative maximum over an array (or a particular axis of the array):

但np.maximum也是一个通用函数，这意味着它具有在处理多维数组时有用的其他功能和方法。例如，您可以计算数组（或数组的特定轴）上的累积最大值：

>>> d = np.array([2, 0, 3, -4, -2, 7, 9])
>>> np.maximum.accumulate(d)
array([2, 2, 3, 3, 3, 7, 9])

This is not possible with np.max.

这是不可能的np.max。

You can make np.maximumimitate np.maxto a certain extent when using np.maximum.reduce:

使用时可以在一定程度上进行np.maximum模仿：np.maxnp.maximum.reduce

>>> np.maximum.reduce(d)
9
>>> np.max(d)
9

Basic testing suggests the two approaches are comparable in performance; and they should be, as np.max()actually calls np.maximum.reduceto do the computation.

基本测试表明这两种方法的性能相当；他们应该是，因为np.max()实际上调用np.maximum.reduce进行计算。

回答by tmdavison

You've already stated why np.maximumis different - it returns an array that is the element-wise maximum between two arrays.

您已经说明了为什么np.maximum不同 - 它返回一个数组，该数组是两个数组之间的元素最大值。

As for np.amaxand np.max: they both call the same function - np.maxis just an alias for np.amax, and they compute the maximum of all elements in an array, or along an axis of an array.

至于np.amaxand np.max：它们都调用相同的函数 -np.max只是 的别名np.amax，它们计算数组中或沿数组轴的所有元素的最大值。

In [1]: import numpy as np

In [2]: np.amax
Out[2]: <function numpy.core.fromnumeric.amax>

In [3]: np.max
Out[3]: <function numpy.core.fromnumeric.amax>

回答by YaOzI

For completeness, in Numpy there are four maximumrelated functions. They fall into two different categories:

为了完整起见，在 Numpy 中有四个最大的相关函数。它们分为两个不同的类别：

• np.amax/np.max, np.nanmax: for single arrayorder statistics
• and np.maximum, np.fmax: for element-wise comparison of two arrays

• np.amax/np.max, np.nanmax: 用于单数组顺序统计
• and np.maximum, np.fmax: 用于两个数组的逐元素比较

---

---

I. For single arrayorder statistics

一、对于单数组顺序统计

NaNs propagator np.amax/np.maxand its NaN ignorant counterpart np.nanmax.

NaN 传播器np.amax/np.max及其 NaN 无知对应物np.nanmax。

• np.maxis just an alias of np.amax, so they are considered as one function.

  >>> np.max.__name__
  'amax'
  >>> np.max is np.amax
  True
  

• np.maxpropagates NaNs while np.nanmaxignores NaNs.

  >>> np.max([np.nan, 3.14, -1])
  nan
  >>> np.nanmax([np.nan, 3.14, -1])
  3.14
  

• np.max只是 的别名np.amax，因此它们被视为一个函数。

  >>> np.max.__name__
  'amax'
  >>> np.max is np.amax
  True
  

• np.max传播 NaN 而np.nanmax忽略 NaN。

  >>> np.max([np.nan, 3.14, -1])
  nan
  >>> np.nanmax([np.nan, 3.14, -1])
  3.14
  

II. For element-wise comparison of two arrays

二、用于两个数组的逐元素比较

NaNs propagator np.maximumand its NaNs ignorant counterpart np.fmax.

NaNs 传播器np.maximum和它的 NaNs 无知对应物np.fmax。

• Both functions require two arrays as the first two positional args to compare with.

  # x1 and x2 must be the same shape or can be broadcast
  np.maximum(x1, x2, /, ...);
  np.fmax(x1, x2, /, ...)
  

• np.maximumpropagates NaNs while np.fmaxignores NaNs.

  >>> np.maximum([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([ nan,  nan, 2.72])
  >>> np.fmax([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([-inf, 3.14, 2.72])
  

• The element-wise functions are np.ufunc(Universal Function), which means they have some special properties that normal Numpy function don't have.

  >>> type(np.maximum)
  <class 'numpy.ufunc'>
  >>> type(np.fmax)
  <class 'numpy.ufunc'>
  >>> #---------------#
  >>> type(np.max)
  <class 'function'>
  >>> type(np.nanmax)
  <class 'function'>
  

• 这两个函数都需要两个数组作为要比较的前两个位置参数。

  # x1 and x2 must be the same shape or can be broadcast
  np.maximum(x1, x2, /, ...);
  np.fmax(x1, x2, /, ...)
  

• np.maximum传播 NaN 而np.fmax忽略 NaN。

  >>> np.maximum([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([ nan,  nan, 2.72])
  >>> np.fmax([np.nan, 3.14, 0], [np.NINF, np.nan, 2.72])
  array([-inf, 3.14, 2.72])
  

• 逐元素函数是np.ufunc( Universal Function)，这意味着它们具有一些普通 Numpy 函数没有的特殊属性。

  >>> type(np.maximum)
  <class 'numpy.ufunc'>
  >>> type(np.fmax)
  <class 'numpy.ufunc'>
  >>> #---------------#
  >>> type(np.max)
  <class 'function'>
  >>> type(np.nanmax)
  <class 'function'>
  

---

---

And finally, the same rules apply to the four minimumrelated functions:

最后，同样的规则适用于四个最小相关函数：

• np.amin/np.min, np.nanmin;
• and np.minimum, np.fmin.

• np.amin/np.min, np.nanmin;
• 和np.minimum，np.fmin。

回答by shivaraj karki

np.maximumnot only compares elementwise but also compares array elementwise with single value

np.maximum不仅按元素进行比较，还可以将数组元素与单个值进行比较

>>>np.maximum([23, 14, 16, 20, 25], 18)
array([23, 18, 18, 20, 25])