statsmodels.sandbox.stats.multicomp.ccols¶
- statsmodels.sandbox.stats.multicomp.ccols = array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])¶
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:
- (for the __new__ method; see Notes below)
- shape
tuple
of
ints
Shape of created array.
- dtypedata-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.
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
A generic version of ndarray.
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:
>>> 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])
- Attributes:
- T
ndarray
Transpose of the array.
- data
buffer
The array’s elements, in memory.
- dtype
dtype
object
Describes the format of the elements in the array.
- flags
dict
Dictionary containing information related to memory use, e.g., ‘C_CONTIGUOUS’, ‘OWNDATA’, ‘WRITEABLE’, etc.
- flat
numpy.flatiter
object
Flattened version of the array as an iterator. The iterator allows assignments, e.g.,
x.flat = 3
(See ndarray.flat for assignment examples; TODO).- imag
ndarray
Imaginary part of the array.
- real
ndarray
Real part of the array.
- size
int
Number of elements in the array.
- itemsize
int
The memory use of each array element in bytes.
- nbytes
int
The total number of bytes required to store the array data, i.e.,
itemsize * size
.- ndim
int
The array’s number of dimensions.
- shape
tuple
of
ints
Shape of the array.
- strides
tuple
of
ints
The step-size required to move from one element to the next in memory. For example, a contiguous
(3, 4)
array of typeint16
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
).- ctypes
ctypes
object
Class containing properties of the array needed for interaction with ctypes.
- base
ndarray
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.
- T