NumPy Reference.pdf文档详细介绍了Python NumPy 数据处理库的功能、函数

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详细说明：文档详细介绍了Python NumPy 数据处理库的功能、函数、以及相关示例，极具参考、学习价值。CONTENTS I Array objec 1.1 The N-dimensional array (ndarray) 2 Scala 1.3 Data type objects(dt ype) 1. 4 Indexing l04 111 1.6 SLandard array subclasses .122 1.7 Masked arrays 1. 8 The Array Interface 431 1. 9 Datetimes and Timedeltas ...436 2 Universal functions (ufunc) 445 2. Broadcasti 445 2.2 Output type determination 446 2.3 Use of internal buffers 446 2.4 Error handling 446 2.5 Casting rules 449 2.6 Overriding Func behavior 451 2.7 func 451 2.8 Available ufuncs 461 3 Routin 465 3.1 Array creation routines 465 3.2 Array manipulation routines .502 3.3 Binary operations ..548 3.5 C-Types Foreign Function Interface (numpy. ctypeslib/ 3.4 String operations 555 601 3.6 Datetime Support Functions 3.7 Data type routines ... 608 3.8 Optionally Scipy-accelerated routines (numpy dual) 626 3.9 Mathematical functions with automatic domain(numpy emath) .627 3.10 Floating point error handling ..628 3.11 Discrete Fourier Transform(numpy fft) .634 3.12 Financial functions 658 3.13 Functional programming 668 3.14 Numpy-specific help functions ..674 3.15 Indexing routines 676 3.16 Input and output 710 3. 17 Lincar algebra(numpy. linalg) 737 3. 18 Logic functions ·· ..775 3.19 Masked array operations 793 3.20 Mathematical functions ,,,..915 3.21 Matrix library (numpy. matlib) 985 3.22 Miscellaneous routines 990 3.23 Padding Arrays 994 3.24 Polynomials 997 3.25 Random sampling(numpy random) ..,,,1173 3.26 Set routines 1275 3.27 Sorting, searching, and counting · ..1280 3.28 Statistics 3.29 Test Support(numpy testing) 1334 3.30 Window functions 1347 4 Packaging(numpy distutils) 1355 4.1 Modules in numpy distutils 1355 4.2 Building Installable C libraries 1366 4.3 Conversion of, src fles 1367 5 Numpy C·API 1369 5.1 Python Types and C-Structures ,,,,,1369 5.2 System configuration 1382 5.3 Data Type API 1383 5.4 Array API 1388 5.5 rray Iterator API 1428 5.6 FUnc API ..14415 5.7 Generalized Universal Function APl 1451 5.8 Numpy core libraries .,.,.1454 5.9 C API Deprecations ,,..,,,1459 6 Numpy internals 1461 6. 1 Numpy c Code explanations 146I 6.2 Internal organization of numpy arrays 1468 6.3 Multidimensional Array Indexing Order Issues 1468 7 Numpy and SWIG 1471 7.1 Numpy. 1: a SWIG Interface File for NumPy l471 7.2 Testing the numpy. i Typemaps 1486 8 Acknowledgements 1489 Bibliography 1491 Index 1501 Num Py Reference, Release 1.12.0.devo+e6593fb Release 1.12.deV0 Date May29,2016 This reference manual details functions, modules, and objects included in Numpy, describing what they are and what they do. For learning how to use NumPy, see also user. CONTENTS NumPy Reference, Release 1.12.0.dev0+e6593fb CONTENTS CHAPTER ONE ARRAY OBJECTS NumPy provides an N-dimensional array type, the ndarray, which describes a collection of"items"of the same type The items can be indexed using for example n integers All ndarrays are homogenous: every item takes up the same size block of memory, and all blocks are interpreted in xactly the same way. How each item in the array is to be interpreted is specified by a separate data-type object, one of which is associated with every array. In addition to basic types (integers, floats, etc. ) the data type objects can also represent data structures An item extracted from an array, e.g. by indexing, is represented by a Python object whose type is one of the array scalar types built in Numpy. The array scalars allow easy manipulation of also more complicated arrangements of head data-type y scalar header ●● ndarray Figure 1.1: Figure Conceptual diagram showing the relationship between the three fundamental objects used to de- scribe the data in an array: 1) the ndarray itself, 2)the data-type object that describes the layout of a single fixed-size element of the array, 3)the array-scalar Python object that is returned when a single element of the array is accessed 1.1 The N-dimensional array (ndarray An ndarray is a(usually fixed-size) multidimensional container of items of the same type and size. The number of dimensions and items in an array is defined by its shape, which is a tuple of n positive integers that specify the sizes of each dimension. The type of items in the array is specified by a separate data-type object (dtype), one of which is associated with each ndarray. As with other container objects in Python, the contents of an ndarray can be accessed and modified by indexing or slicing the arrayusing, for example, N integers), and via the methods and attributes of the ndarray 3 NumPy Reference, Release 1.12.0.devo+e6593fb Different ndarrays can share the same data, so that changes made in one ndarray may be visible in another. That is, an ndarray can be a"view"to another ndarray, and the data it is referring to is taken care of by the base "ndarray ndarrays can also be views to memory owned by Python strings or objects implementing the buffer or array interfaces Example A 2-dimensional array of size 2 X 3, composed of 4-byte integer elements >>>X=np. array([1;2,3],[4,5,6]],np.-nL32) >>> (2,3) yp dtype(int32) The array can be indexed using Python container-like syntax >>> The element of x in the *second* row, *third* column, namely,6 x[1,2] For example slicing can produce views of the arra y([2,5]) >>>y[o]=9 this also changes the corresponding element inx array([9, 51 array([[1,9,3] [4,5,6]1) 1.1.1 Constructing arrays New arrays can be constructed using the routines detailed in Array creation routines, and also by using the low-level ndarray constructor: ndarray An array object represents a multidimensional, homogeneous array of fixed-size items class numpy. ndarray An array object represents a multidimensional, homogeneous array of fixed-size items. An associated data type bject 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 thethe methods and attributes of an arra Parameters (for the new method; see Notes below) shape: tuple of ints Shape of created array Chapter 1. Array objects Num Py Reference, Release 1.12.0.devo+e6593fb dtype dat Any object that can be interpreted as a numpy data type buffer: object exposing buffe Used to fill the array with data ffset: int optional Offset of array data in buffer strides: tuple of ints, optional Strides of data in memory order:(C',“F”},o Row-major(C-style)or column-major( Fortran-style)order array Construct an array. ex。s Create an array, each element of which is zero pty Create an array, but leave its allocated memory unchanged (i.e, it contains"garbage") atype Create a data-type Notes There are two modes of creating an array using new_: 1. f buffer is None, then only shape, dtype, and order are used 2. If buffer is an object exposing the buffer interface, then all key words are interpreted N nit 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 ays of constructing an ndarray First mode, buffer is none: p. ndarray(she (2, 2), dtype-float, order='E array([[-1.13698227e+002;4.25087011e-303] [2.88528414e-306;3.27025015e-309]]) #random Second mode. np. ndarray((2,), buffe offset-np.int_()·it slype=⊥nL)#a⊥CseL=1* iTerms⊥∠e,⊥,e,ski0⊥ irsL elemer array([2, 31) Attributes 1.1. The N-dimensional array( ndarray) NumPy Reference, Release 1.12.0.dev0+e6593fb data Python buffer object pointing to the start of the array 's dale di Same as self transpose(), except that self is returned if self. n datype Data-type of the array s elements flags nformation about the memory layout of the array flat A 1-d iterator over the array ⊥mag The imaginary part of the array rea⊥ The real part of the array Number of elements in the array itemsize Length of one array element in bytes nbytes Total bytes consumed by the elements of the array ndim Number of array dimensions shape Tuple of array dimensions strides Tuple of bytes to step in each dimension when traversing an array types An object to simplify the interaction of the array with the ctypes module base Base object if memory is from some other object ndarray.T Same as self transpose(, except that self is returned if self ndim <2 Examples >>>x=np. array([[1.,2.],[3.,4.]]) >>>又 array([[ 1., 2 4.1] y([[1 >>>x=np. array([1.,2.,3.,4.]) array([ l >>>又.T array([ 1 4.]) darray data Python buffer object pointing to the start of the arrays data ndarray dtype Data-type of the array's elements Parameters None Returns d: numpy dtype object See also numpy atype xamples array([[o, 1] ype dtype( int32) Chapter 1. Array objects

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