要注意的是reshape(返回?)后的数组不是原数组的复制,reshape前后的数组指向相同的地址(只是维度重新定义了一下) 也可以用flatten函数将高维数组转化为向量,和reshape不同的是,flatten函数会生成原始数组的复制 In [40]: a = np.array([[2,2], [2,3]]) In [41]: a.flatten() Out[41]: array([2, 2...
>>> import numpy as np >>> a = np.arange(15).reshape(3, 5) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> a.shape (3, 5) >>> a.ndim 2 >>> a.dtype.name 'int64' >>> a.itemsize 8 >>> a.size 15 >>> type(a)...
c=b.reshape(4,2,6)c输出：array([[[ 0, 1, 2, 3, 4, 5], [ 6, 7, ...
array,zeros,zeros_like,ones,ones_like,empty,empty_like,arange,linspace,numpy.random.rand,numpy.random.randn,fromfunction,fromfile 打印数组 print函数打印,具体打印方式看代码结果 >>> a = np.arange(6) # 1d array >>> print(a) [0 1 2 3 4 5] >>> >>> b = np.arange(12).reshape(4,3...
Fast vectorized array operations for data munging and clean, subsetting and filtering, transformation, and any other kinds of computaions.(快速的向量化数组运算, 数据整理和清洗, 选取子集和过滤,数据转换,计算等) Common array algorithms(常见的数组算法) like sorting, unique, and set operations. ...
array([[1, 2, 3], [4, 5, 6]]) 1. 2. 3. 4. 5. 6. ndarray一般是要元素类型一致的,不一致会变成以下实例: In [130]: L = [1.2, 3, 5] In [131]: np.array(L) Out[131]: array([ 1.2, 3. , 5. ]) In [132]: np.array(L).astype(np.int32) ...
3、asarray(a, dtype=None, order=None, *, like=None) Convert the input to an array.与array()用法类似,不过是直接转换a defasarray(a: Union[ndarray, Iterable, int, float], dtype: Optional[object] = None, order: Optional[str] = None, ...
>>> import numpy as np >>> a = np.arange(15).reshape(3, 5) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> a.shape (3, 5) >>> a.ndim 2 >>> a.dtype.name 'int64' >>> a.itemsize 8 >>> a.size 15 >>> type(a...
reshape(3,3) >>> i * a # element to element array([[1, 0, 0], [0, 5, 0], [0, 0, 9]], dtype=int16) >>> x = array( [1,2,3], int32 ) >>> x array([1, 2, 3]) >>> y = array( [ [4], [5], [6] ], int32 ) >>> y array([[4], [5], [6]])...
np.tile(a1,4)= [0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4] a3.reshape(5,4)= [[0 1 2 3] [4 0 1 2] [3 4 0 1] [2 3 4 0] [1 2 3 4]] a2.reshape(5,4)= [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11] [12 13 14 15] [16 17 18 19]] the su...