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# numpy.choose

`numpy.``choose`(a, choices, out=None, mode='raise')

  Construct an array from an index array and a set of arrays to choose from.

  First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = `numpy.lib.index_tricks`):

  `np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])`.

  But this omits some subtleties. Here is a fully general summary:

  Given an “index” array (a) of integers and a sequence of n arrays (choices), a and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these Ba and Bchoices[i], i = 0,...,n-1 we have that, necessarily, `Ba.shape == Bchoices[i].shape` for each i. Then, a new array with shape `Ba.shape` is created as follows:

  - if `mode=raise` (the default), then, first of all, each element of a (and thus Ba) must be in the range [0, n-1]; now, suppose that i (in that range) is the value at the (j0, j1, ..., jm) position in Ba - then the value at the same position in the new array is the value in Bchoices[i] at that same position;
  - if `mode=wrap`, values in a (and thus Ba) may be any (signed) integer; modular arithmetic is used to map integers outside the range [0, n-1] back into that range; and then the new array is constructed as above;
  - if `mode=clip`, values in a (and thus Ba) may be any (signed) integer; negative integers are mapped to 0; values greater than n-1 are mapped to n-1; and then the new array is constructed as above.

  Parameters : | a : int array This array must contain integers in [0, n-1], where n is the number of choices, unless `mode=wrap` or `mode=clip`, in which cases any integers are permissible. choices : sequence of arrays Choice arrays. a and all of the choices must be broadcastable to the same shape. If choices is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to `choices.shape[0]`) is taken as defining the “sequence”. out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. mode : {‘raise’ (default), ‘wrap’, ‘clip’}, optional Specifies how indices outside [0, n-1] will be treated: ‘raise’ : an exception is raised ‘wrap’ : value becomes value mod n ‘clip’ : values < 0 are mapped to 0, values > n-1 are mapped to n-1
  Returns : | merged_array : array The merged result.
  Raises : | ValueError: shape mismatch : If a and each choice array are not all broadcastable to the same shape.

  See also

  `ndarray.choose`
    equivalent method

  Notes

  To reduce the chance of misinterpretation, even though the following “abuse” is nominally supported, choices should neither be, nor be thought of as, a single array, i.e., the outermost sequence-like container should be either a list or a tuple.

  Examples

      >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
      ...   [20, 21, 22, 23], [30, 31, 32, 33]]
      >>> np.choose([2, 3, 1, 0], choices
      ... # the first element of the result will be the first element of the
      ... # third (2+1) "array" in choices, namely, 20; the second element
      ... # will be the second element of the fourth (3+1) choice array, i.e.,
      ... # 31, etc.
      ... )
      array([20, 31, 12,  3])
      >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
      array([20, 31, 12,  3])
      >>> # because there are 4 choice arrays
      >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
      array([20,  1, 12,  3])
      >>> # i.e., 0

  A couple examples illustrating how choose broadcasts:

      >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
      >>> choices = [-10, 10]
      >>> np.choose(a, choices)
      array([[ 10, -10,  10],
             [-10,  10, -10],
             [ 10, -10,  10]])

      >>> # With thanks to Anne Archibald
      >>> a = np.array([0, 1]).reshape((2,1,1))
      >>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
      >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
      >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
      array([[[ 1,  1,  1,  1,  1],
              [ 2,  2,  2,  2,  2],
              [ 3,  3,  3,  3,  3]],
             [[-1, -2, -3, -4, -5],
              [-1, -2, -3, -4, -5],
              [-1, -2, -3, -4, -5]]])

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