slice

问题 I have following byte slice which from which i need to extract bits and place them in a []int as i intend to fetch individual bit values later. I am having a hard time figuring out how to do that. below is my code data := []byte{3 255}//binary representation is for 3 and 255 is 00000011 11111111 what i need is a slice of bits -- > [0,0,0,0,0,0,1,1,1,1,1,1,1,1,1,1] What i tried I tried converting byte slice to Uint16 with BigEndian and then tried to use strconv.FormatUint but that fails with

问题 How can I index the last axis of a Numpy array if I don't know its rank in advance? Here is what I want to do: Let a be a Numpy array of unknown rank. I want the slice of the last k elements of the last axis. If a is 1D, I want b = a[-k:] If a is 2D, I want b = a[:, -k:] If a is 3D, I want b = a[:, :, -k:] and so on. I want this to work regardless of the rank of a (as long as the rank is at least 1). The fact that I want the last k elements in the example is irrelevant of course, the point is

问题 How can I index the last axis of a Numpy array if I don't know its rank in advance? Here is what I want to do: Let a be a Numpy array of unknown rank. I want the slice of the last k elements of the last axis. If a is 1D, I want b = a[-k:] If a is 2D, I want b = a[:, -k:] If a is 3D, I want b = a[:, :, -k:] and so on. I want this to work regardless of the rank of a (as long as the rank is at least 1). The fact that I want the last k elements in the example is irrelevant of course, the point is

问题 I have a list that looks like this: lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] I'd like to generate a filtered list that looks like this: filtered_lst = [2, 6, 7, 9, 10, 13] Does Python provide a convention for custom slicing. Something such as: lst[1, 5, 6, 8, 9, 12] # slice a list by index 回答1: Use operator.itemgetter(): from operator import itemgetter itemgetter(1, 5, 6, 8, 9, 12)(lst) Demo: >>> from operator import itemgetter >>> lst = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13] >>

问题 I have some experience in C and I am totally new to golang. func learnArraySlice() { intarr := [5]int{12, 34, 55, 66, 43} slice := intarr[:] fmt.Printf("the len is %d and cap is %d \n", len(slice), cap(slice)) fmt.Printf("address of slice 0x%x add of Arr 0x%x \n", &slice, &intarr) } Now in golang slice is a reference of array which contains the pointer to an array len of slice and cap of slice but this slice will also be allocated in memory and i want to print the address of that memory. But

问题 I have some experience in C and I am totally new to golang. func learnArraySlice() { intarr := [5]int{12, 34, 55, 66, 43} slice := intarr[:] fmt.Printf("the len is %d and cap is %d \n", len(slice), cap(slice)) fmt.Printf("address of slice 0x%x add of Arr 0x%x \n", &slice, &intarr) } Now in golang slice is a reference of array which contains the pointer to an array len of slice and cap of slice but this slice will also be allocated in memory and i want to print the address of that memory. But

问题 I have some experience in C and I am totally new to golang. func learnArraySlice() { intarr := [5]int{12, 34, 55, 66, 43} slice := intarr[:] fmt.Printf("the len is %d and cap is %d \n", len(slice), cap(slice)) fmt.Printf("address of slice 0x%x add of Arr 0x%x \n", &slice, &intarr) } Now in golang slice is a reference of array which contains the pointer to an array len of slice and cap of slice but this slice will also be allocated in memory and i want to print the address of that memory. But

问题 I don't really understand why regular indexing can't be used for np.delete. What makes np.s_ so special? For example with this code, used to delete the some of the rows of this array.. inlet_names = np.delete(inlet_names, np.s_[1:9], axis = 0) Why can't I simply use regular indexing and do.. inlet_names = np.delete(inlet_names, [1:9], axis = 0) or inlet_names = np.delete(inlet_names, inlet_names[1:9], axis = 0) From what I can gather, np.s_ is the same as np.index_exp except it doesn't return

问题 I don't really understand why regular indexing can't be used for np.delete. What makes np.s_ so special? For example with this code, used to delete the some of the rows of this array.. inlet_names = np.delete(inlet_names, np.s_[1:9], axis = 0) Why can't I simply use regular indexing and do.. inlet_names = np.delete(inlet_names, [1:9], axis = 0) or inlet_names = np.delete(inlet_names, inlet_names[1:9], axis = 0) From what I can gather, np.s_ is the same as np.index_exp except it doesn't return

问题 For some reason, the following 2 calls to iloc / loc produce different behavior: >>> import pandas as pd >>> df = pd.DataFrame(dict(A=range(3), B=range(3))) >>> df.iloc[:1] A B 0 0 0 >>> df.loc[:1] A B 0 0 0 1 1 1 I understand that loc considers the row labels, while iloc considers the integer-based indices of the rows. But why is the upper bound for the loc call considered inclusive, while the iloc bound is considered exclusive? 回答1: Quick answer: It often makes more sense to do end