Removes duplicated docs for python functions

2 files changed, 2 insertions, 241 deletions

diff --git a/doc/source/reference/routines.other.rst b/doc/source/reference/routines.other.rst
index 5bd5835f6..28c9a1ad1 100644
--- a/doc/source/reference/routines.other.rst
+++ b/doc/source/reference/routines.other.rst
@@ -52,4 +52,5 @@ Matlab-like Functions
 .. autosummary::
    :toctree: generated/
 
-   who
\ No newline at end of file
+   who
+   disp
\ No newline at end of file
diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py
index 39974bb0b..584250785 100644
--- a/numpy/core/_add_newdocs.py
+++ b/numpy/core/_add_newdocs.py
@@ -3255,122 +3255,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
     """))
 
 
-add_newdoc('numpy.core.multiarray', 'shares_memory',
-    """
-    shares_memory(a, b, max_work=None)
-
-    Determine if two arrays share memory
-
-    Parameters
-    ----------
-    a, b : ndarray
-        Input arrays
-    max_work : int, optional
-        Effort to spend on solving the overlap problem (maximum number
-        of candidate solutions to consider). The following special
-        values are recognized:
-
-        max_work=MAY_SHARE_EXACT  (default)
-            The problem is solved exactly. In this case, the function returns
-            True only if there is an element shared between the arrays.
-        max_work=MAY_SHARE_BOUNDS
-            Only the memory bounds of a and b are checked.
-
-    Raises
-    ------
-    numpy.TooHardError
-        Exceeded max_work.
-
-    Returns
-    -------
-    out : bool
-
-    See Also
-    --------
-    may_share_memory
-
-    Examples
-    --------
-    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
-    False
-
-    """)
-
-
-add_newdoc('numpy.core.multiarray', 'may_share_memory',
-    """
-    may_share_memory(a, b, max_work=None)
-
-    Determine if two arrays might share memory
-
-    A return of True does not necessarily mean that the two arrays
-    share any element.  It just means that they *might*.
-
-    Only the memory bounds of a and b are checked by default.
-
-    Parameters
-    ----------
-    a, b : ndarray
-        Input arrays
-    max_work : int, optional
-        Effort to spend on solving the overlap problem.  See
-        `shares_memory` for details.  Default for ``may_share_memory``
-        is to do a bounds check.
-
-    Returns
-    -------
-    out : bool
-
-    See Also
-    --------
-    shares_memory
-
-    Examples
-    --------
-    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
-    False
-    >>> x = np.zeros([3, 4])
-    >>> np.may_share_memory(x[:,0], x[:,1])
-    True
-
-    """)
-
-
-add_newdoc('numpy.lib.utils', 'byte_bounds',
-    """
-    byte_bounds(a)
-
-    Returns pointers to the end-points of an array in the form of tuple
-    of two integers. The first integer is the first byte of the array,
-    the second integer is just past the last byte of the array.
-
-    If `a` is not contiguous it will not use every byte between the
-    returned integer values.
-
-    Parameters
-    ----------
-    a : ndarray
-        Input array. It must conform to the Python-side of the array
-        interface.
-
-    Returns
-    -------
-    (low, high) : tuple of 2 integers
-
-    Examples
-    --------
-    >>> x = np.array([1,2,4])
-    >>> np.byte_bounds(x)
-    (32030368, 32030392)
-    >>> I = np.eye(2, dtype='f'); I.dtype
-    dtype('float32')
-    >>> low, high = np.byte_bounds(I)
-    >>> high - low == I.size*I.itemsize
-    True
-
-    """)
-
-
 add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
     """
     arr.newbyteorder(new_order='S')
@@ -4383,53 +4267,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
            [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
     """))
 
-add_newdoc('numpy.lib.utils', 'who',
-    """
-    who(verdict=None)
-
-    Print the NumPy arrays in the given dictionary.
-
-    If there is no dictionary passed in or `vardict` is None then returns
-    NumPy arrays in the globals() dictionary (all NumPy arrays in the
-    namespace).
-
-    Parameters
-    ----------
-    vardict : dict, optional
-        A dictionary possibly containing ndarrays.  Default is globals().
-
-    Returns
-    -------
-    out : None
-        Returns 'None'.
-
-    Notes
-    -----
-    Prints out the name, shape, bytes and type of all of the ndarrays
-    present in `vardict`.
-
-    Examples
-    --------
-    >>> a = np.arange(10)
-    >>> b = np.ones(20)
-    >>> np.who()
-    Name            Shape            Bytes            Type
-    ===========================================================
-    a               10               80               int64
-    b               20               160              float64
-    Upper bound on total bytes  =       240
-
-    >>> d = {'x': np.arange(2.0), 'y': np.arange(3.0), 'txt': 'Some str',
-    ... 'idx':5}
-    >>> np.who(d)
-    Name            Shape            Bytes            Type
-    ===========================================================
-    x               2                16               float64
-    y               3                24               float64
-    Upper bound on total bytes  =       40
-
-    """)
-
 
 ##############################################################################
 #
@@ -7080,80 +6917,3 @@ for float_name in ('half', 'single', 'double', 'longdouble'):
         (-1, 4)
         """.format(ftype=float_name)))
 
-##############################################################################
-#
-# Miscellaneous utility routines
-#
-##############################################################################
-
-add_newdoc('numpy.lib.utils', 'get_include',
-    """
-
-    Parameters
-    ----------
-    None
-
-    Returns
-    -------
-    Directory that contains the NumPy \\*.h header files.
-
-    Extension modules that need to compile against NumPy should use this
-    function to locate the appropriate include directory.
-
-    """)
-
-add_newdoc('numpy.lib.utils', 'deprecate',
-    """
-
-    deprecate(func, old_name, new_name, message)
-
-    Parameters
-    ----------
-    func : function
-        The function to be deprecated.
-    old_name : str, optional
-        The name of the function to be deprecated. Default is None, in
-        which case the name of `func` is used.
-    new_name : str, optional
-        The new name for the function. Default is None, in which case the
-        deprecation message is that `old_name` is deprecated. If given, the
-        deprecation message is that `old_name` is deprecated and `new_name`
-        should be used instead.
-    message : str, optional
-        Additional explanation of the deprecation.  Displayed in the
-        docstring after the warning.
-
-    Returns
-    -------
-    old_func : function
-        The deprecated function.
-
-    Examples
-    --------
-    Note that ``olduint`` returns a value after printing Deprecation
-    Warning:
-
-    >>> olduint = np.deprecate(np.uint)
-    DeprecationWarning: `uint64` is deprecated! # may vary
-    >>> olduint(6)
-    6
-
-    Notes
-    -----
-    Deprecate may be run as a function or as a decorator
-    If run as a function, we initialise the decorator class
-    and execute its __call__ method.
-
-    """)
-
-add_newdoc('numpy.lib.utils', 'deprecate_with_doc',
-    """
-
-    Lambda function which calls the ``_Deprecate`` with a predefined
-    message.
-
-    Parameters
-    ----------
-    message : str
-
-    """)