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Numpy库入门

0、引入

import numpy as np

1、 Numpy中的数组对象ndarray

ndarray在程序中的别名是array，用np.array() 生成一个ndarray数组。

>>> a = np.array([[0,1,2,3,4],             [9,8,7,6,5]])  >>> print(a)  [[0 1 2 3 4] [9 8 7 6 5]]

注意：np.ndarray和np.array是不一样的，前者是class ndarray in module numpy，是类；后者是built-in function array in module numpy.core.multiarray，是函数。一般用后者将array-like对象(包括列表和元组)转换为数组对象，创建的数组是numpy.ndarray数组。

看np.array()的文档：

array(...)    array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)    Create an array.    Parameters    ----------    object : array_like        An array, any object exposing the array interface, an object whose        __array__ method returns an array, or any (nested) sequence.    dtype : data-type, optional        The desired data-type for the array.  If not given, then the type will        be determined as the minimum type required to hold the objects in the        sequence.  This argument can only be used to 'upcast' the array.  For        downcasting, use the .astype(t) method.    copy : bool, optional        If true (default), then the object is copied.  Otherwise, a copy will        only be made if __array__ returns a copy, if obj is a nested sequence,        or if a copy is needed to satisfy any of the other requirements        (`dtype`, `order`, etc.).    order : {
  'K', 'A', 'C', 'F'}, optional        Specify the memory layout of the array. If object is not an array, the        newly created array will be in C order (row major) unless 'F' is        specified, in which case it will be in Fortran order (column major).        If object is an array the following holds.        ===== ========= ===================================================        order  no copy                     copy=True        ===== ========= ===================================================        'K'   unchanged F & C order preserved, otherwise most similar order        'A'   unchanged F order if input is F and not C, otherwise C order        'C'   C order   C order        'F'   F order   F order        ===== ========= ===================================================        When ``copy=False`` and a copy is made for other reasons, the result is        the same as if ``copy=True``, with some exceptions for `A`, see the        Notes section. The default order is 'K'.    subok : bool, optional        If True, then sub-classes will be passed-through, otherwise        the returned array will be forced to be a base-class array (default).    ndmin : int, optional        Specifies the minimum number of dimensions that the resulting        array should have.  Ones will be pre-pended to the shape as        needed to meet this requirement.    Returns    -------    out : ndarray        An array object satisfying the specified requirements.

这里透漏出了np.array的具体参数，不多做实验。

2、 ndarray对象的属性

注：轴(axis) : 保存数据的维度

• ndim：秩(rank)，轴的数量，即为轴(维度)的数量
• shape：ndarray对象的尺度，即为矩阵的n行m列
• size：ndarray对象的元素个数，相当于shape中n*m的值
• dtype：ndarray对象的元素类型
• itemsize : ndaray对象中每个元素的大小，以字节为单位

The Codes Example

>>> a = np.array([[0,1,2,3,4],             [9,8,7,6,5]])  >>> aarray([[0, 1, 2, 3, 4],       [9, 8, 7, 6, 5]])>>> a.ndim  # 数组维度:a为两维数组2 >>> a.shape (2, 5)>>> a.size  # 元素个数10>>> a.dtype # 元素类型dtype('int32')>>> a.itemsize 4>>> a.T  # 矩阵的转置array([[0, 9],       [1, 8],       [2, 7],       [3, 6],       [4, 5]])>>> a.imag # 虚部数组array([[0, 0, 0, 0, 0],       [0, 0, 0, 0, 0]])>>> a.real # 实部数组array([[0, 1, 2, 3, 4],       [9, 8, 7, 6, 5]])>>> a.flat # 返回其迭代器
   

我们详细看一下ndarray类的属性：

Attributes |  ---------- |  T : ndarray |      Transpose of the array. |  data : buffer |      The array's elements, in memory. |  dtype : dtype object |      Describes the format of the elements in the array. |  flags : dict |      Dictionary containing information related to memory use, e.g., |      'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc. |  flat : numpy.flatiter object |      Flattened version of the array as an iterator.  The iterator |      allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for |      assignment examples; TODO). |  imag : ndarray |      Imaginary part of the array. |  real : ndarray |      Real part of the array. |  size : int |      Number of elements in the array. |  itemsize : int |      The memory use of each array element in bytes. |  nbytes : int |      The total number of bytes required to store the array data, |      i.e., ``itemsize * size``. |  ndim : int |      The array's number of dimensions. |  shape : tuple of ints |      Shape of the array. |  strides : tuple of ints |      The step-size required to move from one element to the next in |      memory. For example, a contiguous ``(3, 4)`` array of type |      ``int16`` in C-order has strides ``(8, 2)``.  This implies that |      to move from element to element in memory requires jumps of 2 bytes. |      To move from row-to-row, one needs to jump 8 bytes at a time |      (``2 * 4``). |  ctypes : ctypes object |      Class containing properties of the array needed for interaction |      with ctypes. |  base : ndarray |      If the array is a view into another array, that array is its `base` |      (unless that array is also a view).  The `base` array is where the |      array data is actually stored.

3、 ndarray数组元素类型之非同质

有时ndarray中的元素不是同一种类型，即由非同质对象组成，这时ndarray的dtype又是什么类型呢？

>>> x = np.array([[9,8,7,6,5],              [1,2,3,4]])  >>> x # 非同质ndarray元素类型为object,无法有效发挥NumPy优势，尽量避免使用array([[9, 8, 7, 6, 5], [1, 2, 3, 4]], dtype=object)>>> x.shape(2,)>>> x.size  # This is a question！2>>> x.dtypedtype('O')

4、 ndarray数组创建的多种方法

(1) 方法一

从Python中的元组和列表等类型创建ndarray数组，当np.array()不指定dtype时，NumPy将根据数据情况关联一个dtype类型，即会给出满足序列对象的最小类型。

用法：

>>> x = np.array([0,1,2,3]) #从列表类型创建[0 1 2 3]>>> x = np.array((4,5,6,7)) #从元组类型创建[4 5 6 7]>>> x = np.array([[1,2],[9,8],(0.1,0.2)]) #从列表和元组混合类型创建[[ 1.   2. ] [ 9.   8. ] [ 0.1  0.2]]

(2) 方法二

使用NumPy中函数创建ndarray数组，如：

• arange
• ones
• zeros
• eye
• full
• ones_like
• zeros_like
• full_like (a)
• linspace()
• concatenate() !

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