NumPy 基本操作

數(shù)組上的算術(shù)運(yùn)算符按元素應(yīng)用。創(chuàng)建一個(gè)新數(shù)組并填充結(jié)果。

>>> a = np.array([20, 30, 40, 50])
>>> b = np.arange(4)
>>> b
array([0, 1, 2, 3])
>>> c = a - b
>>> c
array([20, 29, 38, 47])
>>> b**2
array([0, 1, 4, 9])
>>> 10 * np.sin(a)
array([ 9.12945251, -9.88031624,  7.4511316 , -2.62374854])
>>> a < 35
array([ True,  True, False, False])

與許多矩陣語(yǔ)言不同，乘積運(yùn)算符*在 NumPy 數(shù)組中按元素進(jìn)行運(yùn)算?？梢允褂?code>@運(yùn)算符（在python>=3.5中）或dot函數(shù)或方法來(lái)執(zhí)行矩陣乘積：

>>> A = np.array([[1, 1],
...               [0, 1]])
>>> B = np.array([[2, 0],
...               [3, 4]])
>>> A * B     # elementwise product
array([[2, 0],
       [0, 4]])
>>> A @ B     # matrix product
array([[5, 4],
       [3, 4]])
>>> A.dot(B)  # another matrix product
array([[5, 4],
       [3, 4]])

某些操作，例如+=and?*=，會(huì)修改現(xiàn)有數(shù)組而不是創(chuàng)建新數(shù)組。

>>> rg = np.random.default_rng(1)  # create instance of default random number generator
>>> a = np.ones((2, 3), dtype=int)
>>> b = rg.random((2, 3))
>>> a *= 3
>>> a
array([[3, 3, 3],
       [3, 3, 3]])
>>> b += a
>>> b
array([[3.51182162, 3.9504637 , 3.14415961],
       [3.94864945, 3.31183145, 3.42332645]])
>>> a += b  # b is not automatically converted to integer type
Traceback (most recent call last):
    ...
numpy.core._exceptions._UFuncOutputCastingError: Cannot cast ufunc 'add' output from dtype('float64') to dtype('int64') with casting rule 'same_kind'

當(dāng)處理不同類型的數(shù)組時(shí)，結(jié)果數(shù)組的類型對(duì)應(yīng)于更一般或更精確的類型（一種稱為向上轉(zhuǎn)換的行為）。

>>> a = np.ones(3, dtype=np.int32)
>>> b = np.linspace(0, pi, 3)
>>> b.dtype.name
'float64'
>>> c = a + b
>>> c
array([1.        , 2.57079633, 4.14159265])
>>> c.dtype.name
'float64'
>>> d = np.exp(c * 1j)
>>> d
array([ 0.54030231+0.84147098j, -0.84147098+0.54030231j,
       -0.54030231-0.84147098j])
>>> d.dtype.name
'complex128'

許多一元運(yùn)算，例如計(jì)算數(shù)組中所有元素的總和，都是作為ndarray類的方法實(shí)現(xiàn)的。

>>> a = rg.random((2, 3))
>>> a
array([[0.82770259, 0.40919914, 0.54959369],
       [0.02755911, 0.75351311, 0.53814331]])
>>> a.sum()
3.1057109529998157
>>> a.min()
0.027559113243068367
>>> a.max()
0.8277025938204418

默認(rèn)情況下，這些操作適用于數(shù)組，就好像它是一個(gè)數(shù)字列表，無(wú)論其形狀如何。但是，通過(guò)指定axis?參數(shù)，可以沿?cái)?shù)組的指定軸應(yīng)用操作：

>>> b = np.arange(12).reshape(3, 4)
>>> b
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])
>>>
>>> b.sum(axis=0)     # sum of each column
array([12, 15, 18, 21])
>>>
>>> b.min(axis=1)     # min of each row
array([0, 4, 8])
>>>
>>> b.cumsum(axis=1)  # cumulative sum along each row
array([[ 0,  1,  3,  6],
       [ 4,  9, 15, 22],
       [ 8, 17, 27, 38]])