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# numpy.digitize

`numpy.``digitize`(x, bins, right=False)

  Return the indices of the bins to which each value in input array belongs.

  Each index `i` returned is such that `bins[i-1] <= x < bins[i]` if bins is monotonically increasing, or `bins[i-1] > x >= bins[i]` if bins is monotonically decreasing. If values in x are beyond the bounds of bins, 0 or `len(bins)` is returned as appropriate. If right is True, then the right bin is closed so that the index `i` is such that `bins[i-1] < x <= bins[i]` or bins[i-1] >= x > bins[i]`` if bins is monotonically increasing or decreasing, respectively.

  Parameters : | x : array_like Input array to be binned. It has to be 1-dimensional. bins : array_like Array of bins. It has to be 1-dimensional and monotonic. right : bool, optional Indicating whether the intervals include the right or the left bin edge. Default behavior is (right==False) indicating that the interval does not include the right edge. The left bin and is open in this case. Ie., bins[i-1] <= x < bins[i] is the default behavior for monotonically increasing bins.
  Returns : | out : ndarray of ints Output array of indices, of same shape as x.
  Raises : | ValueError : If the input is not 1-dimensional, or if bins is not monotonic. TypeError : If the type of the input is complex.

  See also

  `bincount`, `histogram`, `unique`

  Notes

  If values in x are such that they fall outside the bin range, attempting to index bins with the indices that `digitize` returns will result in an IndexError.

  Examples

      >>> x = np.array([0.2, 6.4, 3.0, 1.6])
      >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
      >>> inds = np.digitize(x, bins)
      >>> inds
      array([1, 4, 3, 2])
      >>> for n in range(x.size):
      ...   print bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]]
      ...
      0.0 <= 0.2 < 1.0
      4.0 <= 6.4 < 10.0
      2.5 <= 3.0 < 4.0
      1.0 <= 1.6 < 2.5

      >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
      >>> bins = np.array([0,5,10,15,20])
      >>> np.digitize(x,bins,right=True)
      array([1, 2, 3, 4, 4])
      >>> np.digitize(x,bins,right=False)
      array([1, 3, 3, 4, 5])

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