NumPy API文档：矩阵操作与数据分析核心工具

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"numpy文件档，包含了关于NumPy库的详细参考文档，是机器学习和数据挖掘的重要工具，专注于矩阵操作和高效数组处理。" NumPy是Python编程语言中的一个核心库，专门用于处理多维数据结构，即所谓的“ndarray”（n-dimensional array）。这个库在科学计算领域极其重要，特别是在机器学习、数据挖掘和数据分析中扮演着关键角色。NumPy提供的功能强大且高效，能够方便地执行大规模数值计算。 1. **Array objects** NumPy的ndarray是一种通用的数组对象，可以存储单一类型的数据，如整数、浮点数或复数。它支持广播机制，允许不同形状的数组进行数学运算。数组的形状可以通过reshape()函数改变，而索引和切片则与Python列表类似，但更加强大，支持多维索引。 2. **Scalars** 在NumPy中，标量是单个元素，如数组中的一个数字。它们可以与数组进行操作，且这些操作会自动应用到数组的所有元素上。 3. **Datatype objects (dtype)** dtype是NumPy中用来描述数组元素类型的数据类型对象。它可以是基本类型，如int、float或bool，也可以是自定义结构体类型，用于存储复杂数据。 4. **Indexing** NumPy数组支持高级索引，包括切片、整数索引、布尔索引以及混合索引。这使得能够以非常灵活的方式选取和操作数组的子集。 5. **Iterating Over Arrays** NumPy提供了迭代器对象，允许逐个访问数组元素，同时保持了计算效率。此外，还可以通过numpy.nditer()函数进行高效的迭代。 6. **Standard array subclasses** NumPy库还包含一些预定义的数组子类，如structured arrays和record arrays，它们扩展了基本ndarray的功能，以适应特定的数据结构需求。 7. **Masked arrays** Masked arrays允许在数组中包含缺失或无效的数据，并提供一种方法来处理这些值，而不影响其他有效数据。 8. **The Array Interface** The Array Interface是一种标准，定义了如何在Python对象之间共享数组数据。许多Python库都遵循这一接口，确保了数据的兼容性。 9. **Datetimes and Timedeltas** NumPy支持日期和时间类型，可以进行日期时间的运算和处理，这对于处理时间序列数据至关重要。 10. **Universal functions (ufunc)** ufunc是NumPy中的通用函数，可以对数组的每个元素执行元素级操作。它们类似于数学函数，但作用于整个数组，且支持向量化运算。 11. **Routines** NumPy库包含了大量函数，用于数组创建、操纵、数学运算、字符串操作、C-Types Foreign Function Interface、datetime支持、数据类型操作等，提供了全方位的数值计算工具。这些内容构成了NumPy的核心，使得Python成为处理大规模数据和进行科学计算的强大平台。无论是初学者还是经验丰富的开发者，都能从这个详尽的参考文档中获益，更好地理解和利用NumPy的功能。

NumPy Reference, Release 1.12.0

This attribute creates an object that makes it easier to use arrays when calling shared libraries with the

ctypes module. The returned object has, among others, data, shape, and strides attributes (see Notes

below) which themselves return ctypes objects that can be used as arguments to a shared library.

Parameters

None

Returns

c : Python object

Possessing attributes data, shape, strides, etc.

See also:

numpy.ctypeslib

Notes

Below are the public attributes of this object which were documented in “Guide to NumPy” (we have

omitted undocumented public attributes, as well as documented private attributes):

•data: A pointer to the memory area of the array as a Python integer. This memory area may contain

data that is not aligned, or not in correct byte-order. The memory area may not even be writeable.

The array ﬂags and data-type of this array should be respected when passing this attribute to arbitrary

C-code to avoid trouble that can include Python crashing. User Beware! The value of this attribute is

exactly the same as self._array_interface_[’data’][0].

•shape (c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the C-integer

corresponding to dtype(‘p’) on this platform. This base-type could be c_int, c_long, or c_longlong

depending on the platform. The c_intp type is deﬁned accordingly in numpy.ctypeslib. The ctypes

array contains the shape of the underlying array.

•strides (c_intp*self.ndim): A ctypes array of length self.ndim where the basetype is the same as for

the shape attribute. This ctypes array contains the strides information from the underlying array.

This strides information is important for showing how many bytes must be jumped to get to the next

element in the array.

•data_as(obj): Return the data pointer cast to a particular c-types object. For example, calling

self._as_parameter_ is equivalent to self.data_as(ctypes.c_void_p). Perhaps you want to use the data

as a pointer to a ctypes array of ﬂoating-point data: self.data_as(ctypes.POINTER(ctypes.c_double)).

•shape_as(obj): Return the shape tuple as an array of some other c-types type. For example:

self.shape_as(ctypes.c_short).

•strides_as(obj): Return the strides tuple as an array of some other c-types type. For example:

self.strides_as(ctypes.c_longlong).

Be careful using the ctypes attribute - especially on temporary arrays or arrays constructed on the ﬂy. For

example, calling (a+b).ctypes.data_as(ctypes.c_void_p) returns a pointer to memory that

is invalid because the array created as (a+b) is deallocated before the next Python statement. You can avoid

this problem using either c=a+b or ct=(a+b).ctypes. In the latter case, ct will hold a reference to

the array until ct is deleted or re-assigned.

If the ctypes module is not available, then the ctypes attribute of array objects still returns something useful,

but ctypes objects are not returned and errors may be raised instead. In particular, the object will still have

the as parameter attribute which will return an integer equal to the data attribute.

Examples

>>> import ctypes

>>> x

12 Chapter 1. Array objects

NumPy Reference, Release 1.12.0

array([[0, 1],

[2, 3]])

>>> x.ctypes.data

30439712

>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))

<ctypes.LP_c_long object at 0x01F01300>

>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents

c_long(0)

>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents

c_longlong(4294967296L)

>>> x.ctypes.shape

<numpy.core._internal.c_long_Array_2 object at 0x01FFD580>

>>> x.ctypes.shape_as(ctypes.c_long)

<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>

>>> x.ctypes.strides

<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>

>>> x.ctypes.strides_as(ctypes.c_longlong)

<numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>

ndarray.base

Base object if memory is from some other object.

Examples

The base of an array that owns its memory is None:

>>> x = np.array([1,2,3,4])

>>> x.base is None

True

Slicing creates a view, whose memory is shared with x:

>>> y = x[2:]

>>> y.base is x

True

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.

Continued on next page

1.1. The N-dimensional array (ndarray) 13

NumPy Reference, Release 1.12.0

Table 1.3 – continued from previous page

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]) 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]) 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 value of the element in kth position is in the position it would be in a sorted array.

prod([axis, dtype, out, keepdims]) Return the product of the array elements over the given axis

ptp([axis, out]) 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, 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]) 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 a (possibly nested) list.

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.

ndarray.all(axis=None, out=None, keepdims=False)

Returns True if all elements evaluate to True.

Refer to numpy.all for full documentation.

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