About the third parameter of np.zeros(): c represents similar to c language, row priority; F represents column priority record

关于np.zeros()第三个参数：c代表与c语言类似,行优先;FRepresents column-first records

关于np.zeros()的用法

英文原文：

def zeros(shape, dtype=None, order='C', *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ 
    """
    zeros(shape, dtype=float, order='C', *, like=None)
    
        Return a new array of given shape and type, filled with zeros.
    
        Parameters
        ----------
        shape : int or tuple of ints
            Shape of the new array, e.g., ``(2, 3)`` or ``2``.
        dtype : data-type, optional
            The desired data-type for the array, e.g., `numpy.int8`.  Default is
            `numpy.float64`.
        order : {
    'C', 'F'}, optional, default: 'C'
            Whether to store multi-dimensional data in row-major
            (C-style) or column-major (Fortran-style) order in
            memory.
        like : array_like
            Reference object to allow the creation of arrays which are not
            NumPy arrays. If an array-like passed in as ``like`` supports
            the ``__array_function__`` protocol, the result will be defined
            by it. In this case, it ensures the creation of an array object
            compatible with that passed in via this argument.
    
            .. versionadded:: 1.20.0
    
        Returns
        -------
        out : ndarray
            Array of zeros with the given shape, dtype, and order.
    
        See Also
        --------
        zeros_like : Return an array of zeros with shape and type of input.
        empty : Return a new uninitialized array.
        ones : Return a new array setting values to one.
        full : Return a new array of given shape filled with value.
    
        Examples
        --------
        >>> np.zeros(5)
        array([ 0.,  0.,  0.,  0.,  0.])
    
        >>> np.zeros((5,), dtype=int)
        array([0, 0, 0, 0, 0])
    
        >>> np.zeros((2, 1))
        array([[ 0.],
               [ 0.]])
    
        >>> s = (2,2)
        >>> np.zeros(s)
        array([[ 0.,  0.],
               [ 0.,  0.]])
    
        >>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
        array([(0, 0), (0, 0)],
              dtype=[('x', '<i4'), ('y', '<i4')])
    """

About the meaning of the third parameter

直接上链接,This is the setting for record storage.
https://www.jb51.net/article/101882.htm