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]
numpy.reshape(a, newshape, order=‘C‘)
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]])
numpy.transpose(a, axes=None)
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)
ndarray.astype(dtype, order=‘K‘, casting=‘unsafe‘, subok=True, copy=True)
- 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])
numpy.concatenate((a1, a2, ...), axis=0)
- 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])
numpy.flatnonzero(a)
- 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])
numpy.random.choice(a, size=None, replace=True, p=None)
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])
numpy.argsort(a, axis=-1, kind=‘quicksort‘, order=None)
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]])
numpy.argmax(a, axis=None, out=None)
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])
numpy.bincount(x, weights=None, minlength=0)
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])
numpy.sum(a, axis=None, dtype=None, out=None, keepdims=<class numpy._globals._NoValue>)
https://docs.scipy.org/doc/numpy/reference/generated/numpy.sum.html#numpy.sum