np.array v.s. np.asarray
- 二者接口
numpy.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
def asarray(a, dtype=None, order=None):
return array(a, dtype, copy=False, order=order)
由上，知np.asarray是通过np.array实现的，但两者的copy默认值不同了。
- 测试
In [1]: a
Out[1]: array([1, 2, 3])
In [2]: np.array(a)
Out[2]: array([1, 2, 3])
In [3]: np.asarray(a)
Out[3]: array([1, 2, 3])
In [4]: np.array(a) is a
Out[4]: False
In [5]: np.asarray(a) is a
Out[5]: True
=================================
In [6]: np.array(a)[1] = 10
In [7]: a
Out[7]: array([1, 2, 3])
In [8]: np.asarray(a)[1] = 10
In [9]: a
Out[9]: array([ 1, 10, 3])
可见，np.array(a)创建了一个ａ的副本，但np.asarray(a)却没有。
- read more
numpy.asarray官方文档说明
np.asarray v.s. np.asanyarray
- 定义
def asarray(a, dtype=None, order=None):
return array(a, dtype, copy=False, order=order)
def asanyarray(a, dtype=None, order=None):
return array(a, dtype, copy=False, order=order, subok=True)
可见，二者的区别就是subok这参数项。在np.asarray中使用的是np.array中默认的，即subok=False；而np.asanyarray中的是subok=True。
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).
- 测试
In [1]: a = np.matrix([1,2])
In [2]: a
Out[2]: matrix([[1, 2]])
In [3]: np.asarray(a) is a
Out[3]: False
In [4]: np.asanyarray(a) is a
Out[4]: True
======================================
In [5]: b = np.asarray(a)
In [6]: b
Out[6]: array([[1, 2]])
In [7]: c = np.asanyarray(a)
In [8]: c
Out[8]: matrix([[1, 2]])
matrix是narray的一个子类。如文档所说的，
If a is a subclass of ndarray, a base class ndarray is returned.
对于np.asarray,返回的是一个基类，即narray，而不是matrix。
Convert the input to an ndarray, but pass ndarray subclasses through.
If a is an ndarray or a subclass of ndarray, it is returned as-is and no copy is performed.
故对于np.asanyarray返回的仍是matrix类型的。
- read more
numpy.asanyarray官方文档说明
np.copy
- define
def copy(a, order='K'):
return array(a, order=order, copy=True)
np.copy(a) is equivalent to np.array(a, copy=True)
- test
In [1]: a = np.array([1, 1, 1])
In [2]: b = a
In [3]: c = a.copy()
==================================================
In [6]: c[1] = 3
In [7]: c
Out[7]: array([1, 3, 1])
In [8]: a
Out[8]: array([1, 1, 1])
In [9]: b[1] = 3
In [10]: a
Out[10]: array([1, 3, 1])
==================================================
In [12]: a is b
Out[12]: True
In [13]: a is c
Out[13]: False
- read more
numpy 官方文档
np.asmatrix
- define
def asmatrix(data, dtype=None):
return matrix(data, dtype=dtype, copy=False)
- test
In [14]: a = np.array([1, 1, 1])
In [16]: b = np.asmatrix(a)
In [17]: b
Out[17]: matrix([[1, 1, 1]])
In [18]: a[1] = 2
In [19]: b
Out[19]: matrix([[1, 2, 1]])
In [20]: a is b
Out[20]: False
- read more
NumPy 官方文档