numpy

问题 If i have dataset like this: id person_name salary 0 [alexander, william, smith] 45000 1 [smith, robert, gates] 65000 2 [bob, alexander] 56000 3 [robert, william] 80000 4 [alexander, gates] 70000 If we sum that salary column then we will get 316000 I really want to know how much person who named 'alexander, smith, etc' (in distinct) makes in salary if we sum all of the salaries from its splitting name in this dataset (that contains same string value). output: group sum_salary alexander 171000

问题 In numpy, we can perform "outer addition" between two vectors a and b like this: a=np.c_[1,2,3] b=np.c_[4,5,6] result=a+b.T # alternatively this can be a.T+b Is it possible to use einsum to make the same calculation? Any other fast alternatives? How about if a equals b ? 回答1: Another fast alternative to this operation is to use: np.add.outer(a,b) 来源： https://stackoverflow.com/questions/17602796/can-numpy-einsum-perform-outer-addition

问题 I have written code using matmul, but I am getting the following error: "ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1 is different from 3)" Code: R = [[0.40348195], [0.38658295], [0.82931052]] V = [0.33452744, 0.33823673, 0.32723583] print("Rt_p: ", R) B = np.matmul(V,np.transpose(R))/pow(LA.norm(R), 2) print("B", B) 回答1: You are transposing a Matrix with 3 rows and 1 column to a Matrix with 3 columns and 1

问题 I have written code using matmul, but I am getting the following error: "ValueError: matmul: Input operand 1 has a mismatch in its core dimension 0, with gufunc signature (n?,k),(k,m?)->(n?,m?) (size 1 is different from 3)" Code: R = [[0.40348195], [0.38658295], [0.82931052]] V = [0.33452744, 0.33823673, 0.32723583] print("Rt_p: ", R) B = np.matmul(V,np.transpose(R))/pow(LA.norm(R), 2) print("B", B) 回答1: You are transposing a Matrix with 3 rows and 1 column to a Matrix with 3 columns and 1

问题 I have two lists, looking like this: a= [[1,2,3,4], [2,3,4,5],[3,4,5,6,7]], b= [[5,6,7,8], [9,1,2,3], [4,5,6,7,8]] which I want to subtract from each other element by element for an Output like this: a-b= [[-4,-4,-4,-4],[7,2,2,2],[-1,-1,-1,-1,-1]] In order to do so I convert each of a and b to arrays and subtract them I use: np.array(a)-np.array(b) The Output just gives me the error: Unsupported Operand type for-: 'list' and 'list' What am I doing wrong? Shouldn't the np.array command ensure

问题 I have two lists, looking like this: a= [[1,2,3,4], [2,3,4,5],[3,4,5,6,7]], b= [[5,6,7,8], [9,1,2,3], [4,5,6,7,8]] which I want to subtract from each other element by element for an Output like this: a-b= [[-4,-4,-4,-4],[7,2,2,2],[-1,-1,-1,-1,-1]] In order to do so I convert each of a and b to arrays and subtract them I use: np.array(a)-np.array(b) The Output just gives me the error: Unsupported Operand type for-: 'list' and 'list' What am I doing wrong? Shouldn't the np.array command ensure

问题 I have an m -by- n numpy array, and I'd like to add 1.0 to all entries [i, j] when (i + j) % 2 == 0 , i.e., "to every other square". I could of course simply iterate over the fields import numpy as np a = np.random.rand(5, 4) for i in range(a.shape[0]): for j in range(a.shape[1]): if (i + j) % 2 == 0: a[i, j] += 1.0 but needless to say this is really slow. Any idea of how to improve on this? 回答1: You can easily do the operation in two steps, like import numpy as np a = np.zeros((5, 14)) #

问题 I have an m -by- n numpy array, and I'd like to add 1.0 to all entries [i, j] when (i + j) % 2 == 0 , i.e., "to every other square". I could of course simply iterate over the fields import numpy as np a = np.random.rand(5, 4) for i in range(a.shape[0]): for j in range(a.shape[1]): if (i + j) % 2 == 0: a[i, j] += 1.0 but needless to say this is really slow. Any idea of how to improve on this? 回答1: You can easily do the operation in two steps, like import numpy as np a = np.zeros((5, 14)) #

问题 Is there a simpler and more memory efficient way to do the following in numpy alone. import numpy as np ar = np.array(a[l:r]) ar += c a = a[0:l] + ar.tolist() + a[r:] It may look primitive but it involves obtaining a subarray copy of the given array, then prepare two more copies of the same to append in left and right direction in addition to the scalar add. I was hoping to find some more optimized way of doing this. I would like a solution that is completely in Python list or NumPy array,

问题 I'm looking for a way to efficiently get an array of booleans, where given two arrays with equal size a and b , each element is true if the corresponding element of a appears in the corresponding element of b . For example, the following program: a = numpy.array([1, 2, 3, 4]) b = numpy.array([[1, 2, 13], [2, 8, 9], [5, 6], [7]]) print(numpy.magic_function(a, b)) Should print [True, True, False, False] Keep in mind this function should be the equivalent of [x in y for x, y in zip(a, b)] Only