statsmodels.sandbox.stats.multicomp.ccols¶

statsmodels.sandbox.stats.multicomp.ccols = array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])¶

ndarray(shape, dtype=float, buffer=None, offset=0,: strides=None, order=None)

An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data-type object describes the format of each element in the array (its byte-order, how many bytes it occupies in memory, whether it is an integer, a floating point number, or something else, etc.)

Arrays should be constructed using array, zeros or empty (refer to the See Also section below). The parameters given here refer to a low-level method (ndarray(…)) for instantiating an array.

For more information, refer to the numpy module and examine the methods and attributes of an array.

Parameters:¶

below) : (for the __new__ method; see Notes
shape : tuple of ints: Shape of created array.
dtype : data-type, optional: Any object that can be interpreted as a numpy data type.
buffer : object exposing buffer interface, optional: Used to fill the array with data.
offset : int, optional: Offset of array data in buffer.
strides : tuple of ints, optional: Strides of data in memory.
order : {'C', 'F'}, optional: Row-major (C-style) or column-major (Fortran-style) order.

statsmodels.sandbox.stats.multicomp.T¶

Transpose of the array.

Type:¶: ndarray

statsmodels.sandbox.stats.multicomp.data¶

The array’s elements, in memory.

Type:¶: buffer

statsmodels.sandbox.stats.multicomp.dtype¶

Describes the format of the elements in the array.

Type:¶: dtype object

statsmodels.sandbox.stats.multicomp.flags¶

Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.

Type:¶: dict

statsmodels.sandbox.stats.multicomp.flat¶

Flattened version of the array as an iterator. The iterator allows assignments, e.g., x.flat = 3 (See ndarray.flat for assignment examples; TODO).

Type:¶: numpy.flatiter object

statsmodels.sandbox.stats.multicomp.imag¶

Imaginary part of the array.

Type:¶: ndarray

statsmodels.sandbox.stats.multicomp.real¶

Real part of the array.

Type:¶: ndarray

statsmodels.sandbox.stats.multicomp.size¶

Number of elements in the array.

Type:¶: int

statsmodels.sandbox.stats.multicomp.itemsize¶

The memory use of each array element in bytes.

Type:¶: int

statsmodels.sandbox.stats.multicomp.nbytes¶

The total number of bytes required to store the array data, i.e., itemsize * size.

Type:¶: int

statsmodels.sandbox.stats.multicomp.ndim¶

The array’s number of dimensions.

Type:¶: int

statsmodels.sandbox.stats.multicomp.shape¶

Shape of the array.

Type:¶: tuple of ints

statsmodels.sandbox.stats.multicomp.strides¶

The step-size required to move from one element to the next in memory. For example, a contiguous (3, 4) array of type int16 in C-order has strides (8, 2). This implies that to move from element to element in memory requires jumps of 2 bytes. To move from row-to-row, one needs to jump 8 bytes at a time (2 * 4).

Type:¶: tuple of ints

statsmodels.sandbox.stats.multicomp.ctypes¶

Class containing properties of the array needed for interaction with ctypes.

Type:¶: ctypes object

statsmodels.sandbox.stats.multicomp.base¶

If the array is a view into another array, that array is its base (unless that array is also a view). The base array is where the array data is actually stored.

Type:¶: ndarray

See also

array: Construct an array.
zeros: Create an array, each element of which is zero.
empty: Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).
dtype: Create a data-type.
numpy.typing.NDArray: An ndarray alias generic w.r.t. its dtype.type <numpy.dtype.type>.

Notes

There are two modes of creating an array using __new__:

If buffer is None, then only shape, dtype, and order are used.
If buffer is an object exposing the buffer interface, then all keywords are interpreted.

No __init__ method is needed because the array is fully initialized after the __new__ method.

Examples

These examples illustrate the low-level ndarray constructor. Refer to the See Also section above for easier ways of constructing an ndarray.

First mode, buffer is None:

>>> import numpy as np
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[0.0e+000, 0.0e+000], # random
       [     nan, 2.5e-323]])

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
...            offset=np.int_().itemsize,
...            dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])