numpy.ndarray

class numpy.ndarray

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 the methods and attributes of an array.

Parameters :

(for the __new__ method; see Notes below) :

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 or column-major 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.

Notes

There are two modes of creating an array using __new__:

1. If buffer is None, then only shape, dtype, and order are used.
2. 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([[ -1.13698227e+002,   4.25087011e-303],
       [  2.88528414e-306,   3.27025015e-309]])         #random

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
data
dtype	Create a data type object.
flags
flat
imag
real
size
itemsize
nbytes	Base object for a dictionary for look-up with any alias for an array dtype.
ndim
shape
strides
ctypes
base

Methods

all
any
argmax
argmin
argsort
astype
byteswap
choose
clip
compress
conj()	Return the complex conjugate, element-wise.
conjugate()	Return the complex conjugate, element-wise.
copy
cumprod
cumsum
diagonal
dot
dump
dumps
fill
flatten
getfield
item
itemset
max
mean
min
newbyteorder
nonzero
prod
ptp
put
ravel
repeat
reshape
resize
round
searchsorted
setasflat
setfield
setflags
sort
squeeze
std
sum
swapaxes
take
tofile
tolist
tostring
trace
transpose
var
view