numpy 使用详解

numpy.arange([start, ]stop, [step, ]dtype=None)

• 返回数值均匀分布的数组

>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5]

ndarray.reshape(shape, order=‘C‘)

• 返回形状调整后的数组，原数组不变
• newshape 中可以有一个维度为-1，表明这个维度的大小会根据数组长度和其他维度大小进行推断

>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])>>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1, 2],
       [3, 4],
       [5, 6]])

ndarray.transpose(*axes)

numpy.ndarray.T

• 返回转置后的数组，原数组不变

>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
       [3, 4]])
>>> a.transpose()
array([[1, 3],
       [2, 4]])
>>> x = np.arange(24).reshape((2,3,4))
>>> x.shape
(2,3,4)
>>> x.transpose(1,0,2).shape
(3,2,4)

• 更改数组的数据类型

>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. ,  2. ,  2.5])
>>> x.astype(int)
array([1, 2, 2])

• 拼接数组

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])...>>> x = [np.arange(5) for i in range(5)]>>> x[array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4]), array([0, 1, 2, 3, 4])]>>> np.concatenate(x)array([0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4, 0, 1, 2, 3, 4])

• Return indices that are non-zero in the flattened version of a

>>> x = np.arange(-2, 3)
>>> x
array([-2, -1,  0,  1,  2])
>>> np.flatnonzero(x)
array([0, 1, 3, 4])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html#numpy.random.choice

• 返回随机数

>>> np.random.choice(5, 3)
array([0, 3, 4])
>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0])
>>> np.random.choice(5, 3, replace=False)
array([3,1,0])...>>> a = np.arange(5)>>> np.random.choice(a,10)array([3, 4, 2, 0, 3, 2, 4, 2, 0, 2])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html#numpy.argsort

• Perform an indirect sort along the given axis using the algorithm specified by the kind keyword. It returns an array of indices of the same shape as a that index data along the given axis in sorted order.

>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])

>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
       [2, 2]])
>>> np.argsort(x, axis=0)
array([[0, 1],
       [1, 0]])
>>> np.argsort(x, axis=1)
array([[0, 1],
       [0, 1]])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.argmax.html#numpy.argmax

• Returns the indices of the maximum values along an axis.

>>> a = np.arange(6).reshape(2,3)
>>> a
array([[0, 1, 2],
       [3, 4, 5]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.bincount.html#numpy.bincount

• Count number of occurrences of each value in array of non-negative ints.

>>> np.bincount(np.arange(5))
array([1, 1, 1, 1, 1])
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
array([1, 3, 1, 1, 0, 0, 0, 1])

https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html#numpy.sum