parameters: (a, axis=None, dtype=None, out=None)
return_statement: return cumsum(axis, dtype, out)
docstring: Return the cumulative sum of the elements along a given axis.
  
      Parameters
      ----------
      a : array_like
          Input array.
      axis : int, optional
          Axis along which the cumulative sum is computed. The default
          (None) is to compute the cumsum over the flattened array.
      dtype : dtype, optional
          Type of the returned array and of the accumulator in which the
          elements are summed.  If `dtype` is not specified, it defaults
          to the dtype of `a`, unless `a` has an integer dtype with a
          precision less than that of the default platform integer.  In
          that case, the default platform integer is used.
      out : ndarray, optional
          Alternative output array in which to place the result. It must
          have the same shape and buffer length as the expected output
          but the type will be cast if necessary. See `doc.ufuncs`
          (Section "Output arguments") for more details.
  
      Returns
      -------
      cumsum_along_axis : ndarray.
          A new array holding the result is returned unless `out` is
          specified, in which case a reference to `out` is returned. The
          result has the same size as `a`, and the same shape as `a` if
          `axis` is not None or `a` is a 1-d array.
  
  
      See Also
      --------
      sum : Sum array elements.
  
      trapz : Integration of array values using the composite trapezoidal rule.
  
      diff :  Calculate the n-th order discrete difference along given axis.
  
      Notes
      -----
      Arithmetic is modular when using integer types, and no error is
      raised on overflow.
  
      Examples
      --------
      >>> a = np.array([[1,2,3], [4,5,6]])
      >>> a
      array([[1, 2, 3],
             [4, 5, 6]])
      >>> np.cumsum(a)
      array([ 1,  3,  6, 10, 15, 21])
      >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
      array([  1.,   3.,   6.,  10.,  15.,  21.])
  
      >>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
      array([[1, 2, 3],
             [5, 7, 9]])
      >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
      array([[ 1,  3,  6],
             [ 4,  9, 15]])
docstring_summary: Return the cumulative sum of the elements along a given axis.
docstring_tokens: Return the cumulative sum of the elements along a given axis .
function: def cumsum (a, axis=None, dtype=None, out=None):
      """
      Return the cumulative sum of the elements along a given axis.
  
      Parameters
      ----------
      a : array_like
          Input array.
      axis : int, optional
          Axis along which the cumulative sum is computed. The default
          (None) is to compute the cumsum over the flattened array.
      dtype : dtype, optional
          Type of the returned array and of the accumulator in which the
          elements are summed.  If `dtype` is not specified, it defaults
          to the dtype of `a`, unless `a` has an integer dtype with a
          precision less than that of the default platform integer.  In
          that case, the default platform integer is used.
      out : ndarray, optional
          Alternative output array in which to place the result. It must
          have the same shape and buffer length as the expected output
          but the type will be cast if necessary. See `doc.ufuncs`
          (Section "Output arguments") for more details.
  
      Returns
      -------
      cumsum_along_axis : ndarray.
          A new array holding the result is returned unless `out` is
          specified, in which case a reference to `out` is returned. The
          result has the same size as `a`, and the same shape as `a` if
          `axis` is not None or `a` is a 1-d array.
  
  
      See Also
      --------
      sum : Sum array elements.
  
      trapz : Integration of array values using the composite trapezoidal rule.
  
      diff :  Calculate the n-th order discrete difference along given axis.
  
      Notes
      -----
      Arithmetic is modular when using integer types, and no error is
      raised on overflow.
  
      Examples
      --------
      >>> a = np.array([[1,2,3], [4,5,6]])
      >>> a
      array([[1, 2, 3],
             [4, 5, 6]])
      >>> np.cumsum(a)
      array([ 1,  3,  6, 10, 15, 21])
      >>> np.cumsum(a, dtype=float)     # specifies type of output value(s)
      array([  1.,   3.,   6.,  10.,  15.,  21.])
  
      >>> np.cumsum(a,axis=0)      # sum over rows for each of the 3 columns
      array([[1, 2, 3],
             [5, 7, 9]])
      >>> np.cumsum(a,axis=1)      # sum over columns for each of the 2 rows
      array([[ 1,  3,  6],
             [ 4,  9, 15]])
  
      """
      try:
          cumsum = a.cumsum
      except AttributeError:
          return _wrapit(a, 'cumsum', axis, dtype, out)
      return cumsum(axis, dtype, out)
function_tokens: def cumsum ( a , axis = None , dtype = None , out = None ) : try : cumsum = a . cumsum except AttributeError : return _wrapit ( a , 'cumsum' , axis , dtype , out ) return cumsum ( axis , dtype , out )