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 methods and attributes of an array.
Parameters : (for the __new__ method; see Notes below) shape tuple of ints
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 , optional
Row-major (C-style) or column-major (Fortran-style) order.
Create an array, each element of which is zero.
Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).
An ndarray alias generic w.r.t. its dtype.type .
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.
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]])
>>> 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])
View of the transposed array.
Python buffer object pointing to the start of the array’s data.
Data-type of the array’s elements.
Information about the memory layout of the array.
A 1-D iterator over the array.
The imaginary part of the array.
The real part of the array.
Number of elements in the array.
Length of one array element in bytes.
Total bytes consumed by the elements of the array.
Number of array dimensions.
Tuple of array dimensions.
Tuple of bytes to step in each dimension when traversing an array.
An object to simplify the interaction of the array with the ctypes module.
Base object if memory is from some other object.
Returns True if all elements evaluate to True.
Returns True if any of the elements of a evaluate to True.
Return indices of the maximum values along the given axis.
Return indices of the minimum values along the given axis.
Returns the indices that would partition this array.
Returns the indices that would sort this array.
astype (dtype[, order, casting, subok, copy])
Copy of the array, cast to a specified type.
Swap the bytes of the array elements
Use an index array to construct a new array from a set of choices.
Return an array whose values are limited to [min, max] .
Return selected slices of this array along given axis.
Complex-conjugate all elements.
Return the complex conjugate, element-wise.
Return a copy of the array.
Return the cumulative product of the elements along the given axis.
Return the cumulative sum of the elements along the given axis.
Return specified diagonals.
Dump a pickle of the array to the specified file.
Returns the pickle of the array as a string.
Fill the array with a scalar value.
Return a copy of the array collapsed into one dimension.
Returns a field of the given array as a certain type.
Copy an element of an array to a standard Python scalar and return it.
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.
Return the array with the same data viewed with a different byte order.
Return the indices of the elements that are non-zero.
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
Peak to peak (maximum — minimum) value along a given axis.
Set a.flat[n] = values[n] for all n in indices.
Repeat elements of an array.
Returns an array containing the same data with a new shape.
Change shape and size of array in-place.
Return a with each element rounded to the given number of decimals.
Find indices where elements of v should be inserted in a to maintain order.
Put a value into a specified place in a field defined by a data-type.
Set array flags WRITEABLE, ALIGNED, WRITEBACKIFCOPY, respectively.
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.
Return a view of the array with axis1 and axis2 interchanged.
Return an array formed from the elements of a at the given indices.
Construct Python bytes containing the raw data bytes in the array.
Write array to a file as text or binary (default).
Return the array as an a.ndim -levels deep nested list of Python scalars.
A compatibility alias for tobytes , with exactly the same behavior.
trace ([offset, axis1, axis2, dtype, out])
Return the sum along diagonals of the array.
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.
New view of array with the same data.
The N-dimensional array ( ndarray )
Получение формы или размера массива Numpy в Python
Чтобы получить форму или размеры массива Numpy в Python, используйте ndarray.shape, где ndarray – это имя интересующего вас массива numpy.ndarray.shape возвращает кортеж с размерами по всей оси.
Пример 1
В следующем примере мы инициализировали многомерный массив numpy. Конечно, мы знаем форму массива по его определению. Но мы будем использовать свойство ndarray.shape, чтобы программно получить форму массива.
import numpy as np #initialize an array arr = np.array([[[11, 11, 9, 9], [11, 0, 2, 0] ], [[10, 14, 9, 14], [0, 1, 11, 11]]]) # get array shape shape = arr.shape print(shape)
Пример 2: получение формы двумерного массива
В следующем примере мы создадим двумерный массив numpy и найдем его форму. Есть две строки и четыре столбца. Итак, мы должны получить кортеж из (2, 4).
import numpy as np #initialize an array arr = np.array([[11, 11, 9, 9], [11, 0, 2, 0]]) # get array shape shape = arr.shape print(shape)
Пример 3: получение формы одномерного массива
В следующем примере мы найдем форму одномерного массива numpy.
import numpy as np #initialize an array arr = np.array([11, 11, 9, 9]) # get array shape shape = arr.shape print(shape)
Есть четыре элемента, и, конечно же, фигура должна быть кортежем с четырьмя.
В этом учебнике Numpy на примерах Python мы узнали, как получить форму заданного массива numpy в виде кортежа.