不需要额外的操作就能交换两个变量的值。 def swap(a, b): return b, a a, b = -1, 14 swap(a, b) # (14, -1) spread([1,2,3,[4,5,6],[7],8,9]) # [1,2,3,4,5,6,7,8,9] 1. 2. 3. 4. 5. 30. 字典默认值 通过Key 取对应的 Value 值,可以通过以下方式设置默认值。...
元素求e的幂:np.exp(array);元素开根号:np.sqrt(array) 3.几个基本的函数:array.max(),array.min(),array.mean(),array.sum() array.floor()向下取整;array.ravel()平坦化; 其中参数axis=1为行,axis=0为列 4.数组复制: 浅复制:.view() # The view method creates a new array object that looks...
array = [['a', 'b'], ['c', 'd'], ['e', 'f']] transposed = zip(*array) print(transposed) # [('a', 'c', 'e'), ('b', 'd', 'f')] 10. 链式比较 以下代码可对各种运算符进行多次比较。 a = 3 print( 2 < a < 8) # True print(1 == a < 2) # False 11. 逗...
self.Y_type=Y_type def__call__(self,X,Y):# swap color axis because # numpy img_shape:HxWxC# torch img_shape:CXHXWX=X.transpose((2,0,1))Y=Y.transpose((2,0,1))# convert to tensorX=torch.from_numpy(X)Y=torch.from_numpy(Y)ifself.X_type is not None:X=X.type(self.X_type...
# Returning multiple values (with tuple assignments) def swap(x, y): return y, x # Return multiple values as a tuple without the parenthesis. # (Note: parenthesis have been excluded but can be included) x = 1 y = 2 x, y = swap(x, y) # => x = 2, y = 1 ...
类似Array.le DataFrame.ge(other[, axis, level]) 类似Array.ge DataFrame.ne(other[, axis, level]) 类似Array.ne DataFrame.eq(other[, axis, level]) 类似Array.eq DataFrame.combine(other, func[, fill_value, …]) Add two DataFrame objects and do not propagate NaN values, so if for a ...
array = [['a', 'b'], ['c', 'd'], ['e', 'f']] transposed = zip(*array) print(transposed) # [('a', 'c', 'e'), ('b', 'd', 'f')] 10. 链式对比 我们可以在一行代码中使用不同的运算符对比多个不同的元素。 a = 3 ...
swap.split(',')]ifargs.gpu:caffe.set_mode_gpu()print("GPU mode")else:caffe.set_mode_cpu()print("CPU mode")# Make detector.detector = caffe.Detector(args.model_def, args.pretrained_model, mean=mean,input_scale=args.input_scale, raw_scale=args.raw_scale,channel_swap=channel_swap,...
>>> np.array_equal(s.values, s.values, equal_nan=True) True >>> len(s.compare(s)) == 0 True 这里,compare函数返回一个差异列表(实际上是一个DataFrame), array_equal则直接返回一个布尔值。 当比较混合类型的DataFrames时,NumPy比较失败(issue #19205),而Pandas工作得很好。如下所示: ...
array= [['a','b'], ['c','d'], ['e','f']]transposed = zip(*array)print(transposed)# [('a','c','e'), ('b','d','f')] 10 链式对比 我们可以在一行代码中使用不同的运算符对比多个不同的元素。 a = 3print( 2 < a < 8)# Trueprint(1 == a < 2)# False ...