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# numpy.max

numpy.max(a, axis=None, out=None, keepdims=<no value>, initial=<no value>, where=<no value>)

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

  Parameters:
    a array_like

      Input data.

    axis None 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 maximum is selected over multiple axes, instead of a single axis or all the axes as before.

    out ndarray, 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.

    keepdims bool, 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 `max` 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.

    initial scalar, optional

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

    where array_like of bool, optional

      Elements to compare for the maximum. See `reduce` for details.

  Returns:
    max ndarray or scalar

      Maximum 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

  `min`

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

  `nanmax`

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

  `maximum`

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

  `fmax`

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

  `argmax`

    Return the indices of the maximum values.

  `nanmin`, `minimum`, `fmin`

  Notes

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

  Don’t use `max` for element-wise comparison of 2 arrays; when `a.shape[0]` is 2, `maximum(a[0], a[1])` is faster than `max(a, axis=0)`.

  Examples

      >>> import numpy as np
      >>> a = np.arange(4).reshape((2,2))
      >>> a
      array([[0, 1],
             [2, 3]])
      >>> np.max(a)           # Maximum of the flattened array
      3
      >>> np.max(a, axis=0)   # Maxima along the first axis
      array([2, 3])
      >>> np.max(a, axis=1)   # Maxima along the second axis
      array([1, 3])
      >>> np.max(a, where=[False, True], initial=-1, axis=0)
      array([-1,  3])
      >>> b = np.arange(5, dtype=np.float64)
      >>> b[2] = np.nan
      >>> np.max(b)
      np.float64(nan)
      >>> np.max(b, where=~np.isnan(b), initial=-1)
      4.0
      >>> np.nanmax(b)
      4.0

  You can use an initial value to compute the maximum of an empty slice, or to initialize it to a different value:

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

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

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