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# numpy.mean

`numpy.``mean`(a, axis=None, dtype=None, out=None)

  Compute the arithmetic mean along the specified axis.

  Returns the average of the array elements. The average is taken over the flattened array by default, otherwise over the specified axis. float64 intermediate and return values are used for integer inputs.

  Parameters: | a : array_like Array containing numbers whose mean is desired. If a is not an array, a conversion is attempted. axis : int, optional Axis along which the means are computed. The default is to compute the mean of the flattened array. dtype : dtype, optional Type to use in computing the mean. For integer inputs, the default is float64; for floating point, inputs it is the same as the input dtype. out : ndarray, optional Alternative output array in which to place the result. It must have the same shape as the expected output but the type will be cast if necessary.
  Returns: | mean : ndarray, see dtype parameter above If out=None, returns a new array containing the mean values, otherwise a reference to the output array is returned.

  See also

  `average`
    Weighted average

  Notes

  The arithmetic mean is the sum of the elements along the axis divided by the number of elements.

  Examples

      >>> a = np.array([[1,2],[3,4]])
      >>> np.mean(a)
      2.5
      >>> np.mean(a,0)
      array([ 2.,  3.])
      >>> np.mean(a,1)
      array([ 1.5,  3.5])

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