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# numpy.nanmin

`numpy.``nanmin`(a, axis=None)

  Return the minimum of array elements over the given axis ignoring any NaNs.

  Parameters: | a : array_like Array containing numbers whose sum is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the minimum is computed.The default is to compute the minimum of the flattened array.
  Returns: | y : {ndarray, scalar} An array with the same shape as a, with the specified axis removed. If a is a 0-d array, or if axis is None, a scalar is returned. The the same dtype as a is returned.

  See also

  `numpy.amin`
    Minimum across array including any Not a Numbers.
  `numpy.nanmax`
    Maximum across array ignoring any Not a Numbers.
  `isnan`
    Shows which elements are Not a Number (NaN).
  `isfinite`
    Shows which elements are not: Not a Number, positive and negative infinity

  Notes

  Numpy uses the IEEE Standard for Binary Floating-Point for Arithmetic (IEEE 754). This means that Not a Number is not equivalent to infinity. Positive infinity is treated as a very large number and negative infinity is treated as a very small (i.e. negative) number.

  If the input has a integer type, an integer type is returned unless the input contains NaNs and infinity.

  Examples

      >>> a = np.array([[1, 2], [3, np.nan]])
      >>> np.nanmin(a)
      1.0
      >>> np.nanmin(a, axis=0)
      array([ 1.,  2.])
      >>> np.nanmin(a, axis=1)
      array([ 1.,  3.])

  When positive infinity and negative infinity are present:

      >>> np.nanmin([1, 2, np.nan, np.inf])
      1.0
      >>> np.nanmin([1, 2, np.nan, np.NINF])
      -inf

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