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
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