numpy函数库详解：数组操作与通用函数全览

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numpy函数库是Python中一个核心且强大的数学计算库，它提供了多维数组对象、数据类型处理、索引和迭代操作，以及广泛的通用函数（ufunc）功能。这个文档详细介绍了NumPy 1.14.5版本的内容，涵盖了以下几个关键部分： 1. **N-dimensional Array (ndarray)**: NumPy的核心是ndarray，这是一个多维度数组，支持高效的向量化操作。它是数组的所有操作和计算的基础，允许用户在Python中进行大规模数值计算。 2. **Scalars**: 在NumPy中，数组元素可以是标量值，这些标量被封装在ndarray中，方便进行统一的处理和操作。 3. **Data Type Objects (dtype)**: dtype用于定义数组的元素类型，如整型、浮点型、复数等，这有助于确保数据的一致性和内存优化。 4. **Indexing**: 该部分详细解释了如何通过索引和切片访问数组的不同部分，包括基本的下标选择和高级的布尔索引。 5. **Iterating Over Arrays**: NumPy提供了高效的迭代方法，如迭代整个数组或者按特定维度迭代，这对于遍历数组进行特定操作非常有用。 6. **Standard Array Subclasses**: 除了基本的ndarray，NumPy还提供了其他类如 masked arrays，用于处理缺失或不完整数据。 7. **Masked Arrays**: 这是一种特殊的数组类型，用于表示数组中的缺失值，便于处理和分析可能包含缺失数据的数据集。 8. **The Array Interface**: 数组接口规范定义了一套标准，使得NumPy可以与许多其他科学计算库无缝交互，提高了代码的可移植性。 9. **Datetime and Timedelta Support**: NumPy提供了内置的支持日期和时间的处理功能，这对于处理时间序列数据至关重要。 10. **Universal Functions (ufunc)**: ufuncs是NumPy的灵魂，它们是针对数组的高效操作函数，例如加法、乘法等，支持广播机制和自定义行为。 11. **Routines**: NumPy提供了一系列创建、操纵、运算和特殊功能的函数，包括数组创建、数学运算、字符串操作、C-Types Foreign Function Interface（用于调用C语言库）、datetime支持、数据类型转换、Scipy加速的特定算法、数学函数、错误处理和离散傅立叶变换（DFT）等。 numpy函数库是Python数据分析和科学计算的重要工具，它为开发人员提供了丰富的功能和高效的性能，无论是在机器学习项目中处理大规模数据，还是在研究环境中执行复杂计算，都能发挥重要作用。学习并熟练掌握这些内容将极大地提升你的编程能力和数据分析能力。

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NumPy Reference, Release 1.14.5

This array is stored in memory as 40 bytes, one after the other (known as a contiguous block of memory).

The strides of an array tell us how many bytes we have to skip in memory to move to the next position

along a certain axis. For example, we have to skip 4 bytes (1 value) to move to the next column, but 20

bytes (5 values) to get to the same position in the next row. As such, the strides for the array x will be

(20, 4).

Examples

>>> y = np.reshape(np.arange(2

4), (2,3,4))

>>> y

array([[[ 0, 1, 2, 3],

[ 4, 5, 6, 7],

[ 8, 9, 10, 11]],

[[12, 13, 14, 15],

[16, 17, 18, 19],

[20, 21, 22, 23]]])

>>> y.strides

(48, 16, 4)

>>> y[1,1,1]

>>> 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):

12 Chapter 1. Array objects

NumPy Reference, Release 1.14.5

• 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

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>

(continues on next page)

1.1. The N-dimensional array (ndarray) 13

NumPy Reference, Release 1.14.5

Table 3 – continued from previous page

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.

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.

Continued on next page

1.1. The N-dimensional array (ndarray) 15

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