NumPy 1.34 教程：掌握核心功能与API

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"numpy 1.34 用户手册" NumPy是Python编程语言中用于科学计算的核心库，提供了高效的多维数组对象以及大量的数学函数来操作这些数组。在NumPy 1.34版本的用户手册中，包含了对所有API调用的详细解释，帮助用户在不需在线查询的情况下，快速理解和使用NumPy的功能。 1. **Array objects** (数组对象) - **ndarray**: NumPy的基础数据结构，支持多维数组，可以存储同类型的元素。这种数据结构非常高效，适用于大规模数据处理。 - **Scalars**: ndarray中的单个元素，与数组的数据类型相同。 - **Datatype objects (dtype)**: 描述数组中元素的数据类型，如int, float, complex等。 2. **Indexing and Iterating Over Arrays** - 数组索引允许用户通过坐标访问数组元素，包括切片、整数索引和布尔索引。 - 迭代数组时，可以遍历每个元素，支持多种迭代方式，如常规for循环和迭代器。 3. **Standard array subclasses** - NumPy提供了一些标准子类，如结构化数组（structured arrays）和记录数组（record arrays），它们扩展了ndarray的功能，支持更复杂的数据结构。 4. **Masked arrays** - 掩码数组允许隐藏或忽略数组中的某些元素，这些元素可能表示缺失值或无效数据。 5. **The Array Interface** - 数组接口定义了与其他Python对象交互的标准化方式，使得不同库之间的数据交换变得简单。 6. **Datetimes and Timedeltas** - 支持日期和时间间隔的处理，提供专门的dtype来存储和操作日期时间数据。 7. **Universal functions (ufunc)** - ufunc是NumPy中的核心功能，是一类向量化操作，可以应用于整个数组，类似于数学中的元素级运算。ufunc支持广播、输出类型确定、错误处理等功能。 8. **Routines** - 包含了大量的数组创建、操作、数学计算、字符串处理、C接口、日期时间支持函数、数据类型操作等方法，提供了广泛的数据处理工具。 - **Array creation routines**: 用于创建新数组的函数，如`numpy.array`, `numpy.zeros`, `numpy.ones`等。 - **Array manipulation routines**: 提供数组形状改变、拼接、分割等操作。 - **Binary operations**: 包括加减乘除等二元运算。 - **String operations**: 对字符串数组进行处理，如连接、分割等。 - **C-Types Foreign Function Interface (numpy.ctypeslib)**: 允许使用C类型与Python数组交互，进行低级数据操作。 - **Datetime Support Functions**: 提供与日期时间相关的操作。 - **Datatype routines**: 处理数组数据类型的函数，如转换数据类型。 - **Optionally Scipy-accelerated routines (numpy.dual)**: 部分功能可以利用Scipy库加速。 - **Mathematical functions with automatic domain (numpy.emath)**: 自动处理复数域的数学函数。 - **Floating-point error handling**: 控制浮点数计算中的误差处理策略。 - **Discrete Fourier Transform (numpy.fft)**: 实现离散傅立叶变换，用于信号分析和处理。 NumPy 1.34 用户手册是深入学习和应用NumPy的强大资源，无论是在数据预处理、数值计算还是科学分析中，都能为用户提供详尽的指导和支持。

NumPy Reference, Release 1.14.0

>>> offset=sum(y.strides

np.array((1,1,1)))

>>> offset/y.itemsize

>>> x = np.reshape(np.arange(5

8), (5,6,7,8)).transpose(2,3,1,0)

>>> x.strides

(32, 4, 224, 1344)

>>> i = np.array([3,5,2,2])

>>> offset = sum(i

x.strides)

>>> x[3,5,2,2]

813

>>> offset / x.itemsize

813

ndarray.ctypes

An object to simplify the interaction of the array with the ctypes module.

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)).

12 Chapter 1. Array objects

NumPy Reference, Release 1.14.0

•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

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

1.1. The N-dimensional array (ndarray) 13

NumPy Reference, Release 1.14.0

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]) 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 dif-

ferent 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.

Continued on next page

14 Chapter 1. Array objects

NumPy Reference, Release 1.14.0

Table 1.3 – continued from previous page

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, (WRITE-

BACKIFCOPY 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 inter-

changed.

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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