numpy.ndarray#

class numpy.ndarray(shape, dtype=float, buffer=None, offset=0, strides=None, order=None)[source]#

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)
shapetuple of ints: Shape of created array.
dtypedata-type, optional: Any object that can be interpreted as a numpy data type.
bufferobject exposing buffer interface, optional: Used to fill the array with data.
offsetint, optional: Offset of array data in buffer.
stridestuple 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: An ndarray alias generic w.r.t. its 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:

>>> 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

Tndarray: The transposed array.
databuffer: Python buffer object pointing to the start of the array’s data.
dtypedtype object: Data-type of the array’s elements.
flagsdict: Information about the memory layout of the array.
flatnumpy.flatiter object: A 1-D iterator over the array.
imagndarray: The imaginary part of the array.
realndarray: The real part of the array.
sizeint: Number of elements in the array.
itemsizeint: Length of one array element in bytes.
nbytesint: Total bytes consumed by the elements of the array.
ndimint: Number of array dimensions.
shapetuple of ints: Tuple of array dimensions.
stridestuple of ints: Tuple of bytes to step in each dimension when traversing an array.
ctypesctypes object: An object to simplify the interaction of the array with the ctypes module.
basendarray: Base object if memory is from some other object.

Methods

`all`([axis, out, keepdims, where])	Returns True if all elements evaluate to True.
`any`([axis, out, keepdims, where])	Returns True if any of the elements of a evaluate to True.
`argmax`([axis, out, keepdims])	Return indices of the maximum values along the given axis.
`argmin`([axis, out, keepdims])	Return indices of the minimum values along the given axis.
`argpartition`(kth[, axis, kind, order])	Returns the indices that would partition this array.
`argsort`([axis, kind, order])	Returns the indices that would sort this array.
`astype`(dtype[, order, casting, subok, copy])	Copy of the array, cast to a specified type.
`byteswap`([inplace])	Swap the bytes of the array elements
`choose`(choices[, out, mode])	Use an index array to construct a new array from a set of choices.
`clip`([min, max, out])	Return an array whose values are limited to `[min, max]`.
`compress`(condition[, axis, out])	Return selected slices of this array along given axis.
`conj`()	Complex-conjugate all elements.
`conjugate`()	Return the complex conjugate, element-wise.
`copy`([order])	Return a copy of the array.
`cumprod`([axis, dtype, out])	Return the cumulative product of the elements along the given axis.
`cumsum`([axis, dtype, out])	Return the cumulative sum of the elements along the given axis.
`diagonal`([offset, axis1, axis2])	Return specified diagonals.
`dump`(file)	Dump a pickle of the array to the specified file.
`dumps`()	Returns the pickle of the array as a string.
`fill`(value)	Fill the array with a scalar value.
`flatten`([order])	Return a copy of the array collapsed into one dimension.
`getfield`(dtype[, offset])	Returns a field of the given array as a certain type.
`item`(*args)	Copy an element of an array to a standard Python scalar and return it.
`itemset`(*args)	Insert scalar into an array (scalar is cast to array's dtype, if possible)
`max`([axis, out, keepdims, initial, where])	Return the maximum along a given axis.
`mean`([axis, dtype, out, keepdims, where])	Returns the average of the array elements along given axis.
`min`([axis, out, keepdims, initial, where])	Return the minimum along a given axis.
`newbyteorder`([new_order])	Return the array with the same data viewed with a different byte order.
`nonzero`()	Return the indices of the elements that are non-zero.
`partition`(kth[, axis, kind, order])	Rearranges the elements in the array in such a way that the value of the element in kth position is in the position it would be in a sorted array.
`prod`([axis, dtype, out, keepdims, initial, ...])	Return the product of the array elements over the given axis
`ptp`([axis, out, keepdims])	Peak to peak (maximum - minimum) value along a given axis.
`put`(indices, values[, mode])	Set `a.flat[n] = values[n]` for all n in indices.
`ravel`([order])	Return a flattened array.
`repeat`(repeats[, axis])	Repeat elements of an array.
`reshape`(shape[, order])	Returns an array containing the same data with a new shape.
`resize`(new_shape[, refcheck])	Change shape and size of array in-place.
`round`([decimals, out])	Return a with each element rounded to the given number of decimals.
`searchsorted`(v[, side, sorter])	Find indices where elements of v should be inserted in a to maintain order.
`setfield`(val, dtype[, offset])	Put a value into a specified place in a field defined by a data-type.
`setflags`([write, align, uic])	Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
`sort`([axis, kind, order])	Sort an array in-place.
`squeeze`([axis])	Remove axes of length one from a.
`std`([axis, dtype, out, ddof, keepdims, where])	Returns the standard deviation of the array elements along given axis.
`sum`([axis, dtype, out, keepdims, initial, where])	Return the sum of the array elements over the given axis.
`swapaxes`(axis1, axis2)	Return a view of the array with axis1 and axis2 interchanged.
`take`(indices[, axis, out, mode])	Return an array formed from the elements of a at the given indices.
`tobytes`([order])	Construct Python bytes containing the raw data bytes in the array.
`tofile`(fid[, sep, format])	Write array to a file as text or binary (default).
`tolist`()	Return the array as an `a.ndim`-levels deep nested list of Python scalars.
`tostring`([order])	A compatibility alias for `tobytes`, with exactly the same behavior.
`trace`([offset, axis1, axis2, dtype, out])	Return the sum along diagonals of the array.
`transpose`(*axes)	Returns a view of the array with axes transposed.
`var`([axis, dtype, out, ddof, keepdims, where])	Returns the variance of the array elements, along given axis.
`view`([dtype][, type])	New view of array with the same data.

dot

The N-dimensional array (ndarray)

numpy.ndarray.all