1、查看np数组的属性与方法 ~~~ # 导入np库 import numpy as np #定义一个python普通列表 a = [1,2,3,4,5,6] # python普通列表转化为成np数组 np_a = np.array(a) # np数组的属性与方法 print(dir(np_a)) ~~~ 结果： ~~~ ['T', '__abs__', '__add__', '__and__', '__array__', '__array_finalize__', '__array_interface__', '__array_prepare__', '__array_priority__', '__array_struct__', '__array_wrap__', '__bool__', '__class__', '__complex__', '__contains__', '__copy__', '__deepcopy__', '__delattr__', '__delitem__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__gt__', '__hash__', '__iadd__', '__iand__', '__ifloordiv__', '__ilshift__', '__imatmul__', '__imod__', '__imul__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__ior__', '__ipow__', '__irshift__', '__isub__', '__iter__', '__itruediv__', '__ixor__', '__le__', '__len__', '__lshift__', '__lt__', '__matmul__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmatmul__', '__rmod__', '__rmul__', '__ror__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__setitem__', '__setstate__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__xor__', 'all', 'any', 'argmax', 'argmin', 'argpartition', 'argsort', 'astype', 'base', 'byteswap', 'choose', 'clip', 'compress', 'conj', 'conjugate', 'copy', 'ctypes', 'cumprod', 'cumsum', 'data', 'diagonal', 'dot', 'dtype', 'dump', 'dumps', 'fill', 'flags', 'flat', 'flatten', 'getfield', 'imag', 'item', 'itemset', 'itemsize', 'max', 'mean', 'min', 'nbytes', 'ndim', 'newbyteorder', 'nonzero', 'partition', 'prod', 'ptp', 'put', 'ravel', 'real', 'repeat', 'reshape', 'resize', 'round', 'searchsorted', 'setfield', 'setflags', 'shape', 'size', 'sort', 'squeeze', 'std', 'strides', 'sum', 'swapaxes', 'take', 'tobytes', 'tofile', 'tolist', 'tostring', 'trace', 'transpose', 'var', 'view'] ~~~ 2、NumPy的数组中比较重要ndarray对象属性 查看一维数组 ~~~ # 数组中数据类型 print(np_a.dtype) # 数组的维数，秩 print(np_a.ndim) # 数组的维度 print(np_a.shape) # 数组元素的总个数 print(np_a.size) ~~~ 结果 ~~~ int32 1 (6,) 6 ~~~ 看看二维数组 ~~~ import numpy as np a = [[1.1,2.3,3.6,4.9],[5.2,6.3,7.5,8.9]] np_a = np.array(a) print(type(np_a)) # 数组中数据类型 print(np_a.dtype) # 数组的维数，秩 print(np_a.ndim) # 数组的维度 print(np_a.shape) # 数组元素的总个数 print(np_a.size) ~~~ 结果 ~~~ <class 'numpy.ndarray'> float64 2 (2, 4) 8 ~~~