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) 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 (C-style) or column-major (Fortran-style) order. |
|---|
See also
Construct an array.
Create an array, each element of which is zero.
Create an array, but leave its allocated memory unchanged (i.e., it contains “garbage”).
Create a data-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: |
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. |
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Methods
all([axis, out, keepdims]) |
Returns True if all elements evaluate to True. |
any([axis, out, keepdims]) |
Returns True if any of the elements of a evaluate to True. |
argmax([axis, out]) |
Return indices of the maximum values along the given axis. |
argmin([axis, out]) |
Return indices of the minimum values along the given axis of a. |
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. |
dot(b[, out]) |
Dot product of two arrays. |
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]) |
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 and UPDATEIFCOPY), respectively. |
sort([axis, kind, order]) |
Sort an array in-place. |
squeeze([axis]) |
Remove single-dimensional entries from the shape of a. |
std([axis, dtype, out, ddof, keepdims]) |
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]) |
Construct Python bytes containing the raw data bytes in the array. |
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]) |
Returns the variance of the array elements, along given axis. |
view([dtype, type]) |
New view of array with the same data. |
NumPy 最重要的一个特点是其 N 维数组对象 ndarray，它是一系列同类型数据的集合，以 0 下标为开始进行集合中元素的索引。
ndarray 对象是用于存放同类型元素的多维数组。ndarray 中的每个元素在内存中都有相同存储大小的区域。
ndarray 内部由以下内容组成：
-
一个指向数据（内存或内存映射文件中的一块数据）的指针。
-
数据类型或dtype，描述在数组中的固定大小值的格子。
-
一个表示数组形状（shape）的元组，表示各维度大小的元组。
-
一个跨度元组（stride），其中的整数指的是为了前进到当前维度下一个元素需要"跨过"的字节数。
ndarray 的内部结构：
跨度可以是负数，这样会使数组在内存中后向移动，切片中 obj[::-1] 或 obj[:,::-1] 就是如此。
创建一个 ndarray 只需调用 NumPy 的 array 函数即可：
numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0)参数说明：
| 名称 | 描述 |
|---|---|
| object | 数组或嵌套的数列 |
| dtype | 数组元素的数据类型，可选 |
| copy | 对象是否需要复制，可选 |
| order | 创建数组的样式，C为行方向，F为列方向，A为任意方向（默认） |
| subok | 默认返回一个与基类类型一致的数组 |
| ndmin | 指定生成数组的最小维度 |
实例
接下来可以通过以下实例帮助我们更好的理解。
实例 1
import numpy as np
a = np.array([1,2,3])
print (a)
Output:
----------
[1, 2, 3]
----------
实例 2
# 多于一个维度
import numpy as np
a = np.array([[1, 2], [3, 4]])
print (a)
Output：
----------
[[1, 2]
[3, 4]]
---------- 实例 3
# 最小维度
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print (a)
Output：
-------------------
[[1, 2, 3, 4, 5]]
------------------- 实例 4
# dtype 参数
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print (a)
Output：
----------------------------
[ 1.+0.j, 2.+0.j, 3.+0.j]
---------------------------- ndarray对象由计算机内存的连续一维部分组成，并结合索引模式，将每个元素映射到内存块中的一个位置。内存块以行顺序(C样式)或列顺序(FORTRAN或MatLab风格，即前述的F样式)来保存元素。
