I have an array A with the shape (3,3) which can be thought of as the sliding window view of an unkown array with the shape (5,). I want to compute the inverse of windowing the array with the shape (5,). The adjoint operation of this will be summation. What I mean is that I want to accumulate the values
Tag: vectorization
Vectorize else-if statement function using numpy
I have an array of 3 dimensional vectors vec and I want to find a perpendicular vector res_vec to each of those vectors respectively. Using other methods I got some numerically unstable behaviour so I just check for the smallest component of that vector and set it to zero, while exchanging the two components that are left and negating one
Python dataframe vectorizing for loop
I would like to vectorize this piece of python code with for loop conditioned on current state for speed and efficiency. values for df_B are computed based on current-state (state) AND corresponding df_A value. Any ideas would be appreciated. Answer This seems overkill. Your state variable basically is the previous value in df_A[‘a’]*10. So we can just use shift:
How to vectorize groupby and apply in pandas?
I’m trying to calculate (x-x.mean()) / (x.std +0.01) on several columns of a dataframe based on groups. My original dataframe is very large. Although I’ve splitted the original file into several chunks and I’m using multiprocessing to run the script on each chunk of the file, but still every chunk of the dataframe is very large and this process never
Numpy assignment of 3D array values using 2D index array
I have a 3D numpy array of zeros, with dimensions CxHxW (in this example, C=4, H=2, and W=3): I also have a 2D array of indices, with dimensions HxW, such that every value in the array is a valid index between [0, C-1] Is there a fast way, using vectorization, to modify array A such that A[B[i][j]][i][j] = 1, for
bootstrap numpy 2D array
I am trying to sample with replacement a base 2D numpy array with shape of (4,2) by rows, say 10 times. The final output should be a 3D numpy array. Have tried the code below, it works. But is there a way to do it without the for loop? Answer Here’s one vectorized approach – Basic idea is that we
Convert a 2d matrix to a 3d one hot matrix numpy
I have np matrix and I want to convert it to a 3d array with one hot encoding of the elements as third dimension. Is there a way to do with without looping over each row eg should be made into Answer Approach #1 Here’s a cheeky one-liner that abuses broadcasted comparison – Sample run – For 0-based indexing, it
Python pandas calculate rolling stock beta using rolling apply to groupby object in vectorized fashion
I have a large data frame, df, containing 4 columns: etc. I am attempting to calculate a common financial measure, known as beta, using a function, that takes two of the columns, ret_1m, the monthly stock_return, and ret_1m_mkt, the market 1 month return for the same period (period_id). I want to apply a function (calc_beta) to calculate the 12-month result
Difference between map, applymap and apply methods in Pandas
Can you tell me when to use these vectorization methods with basic examples? I see that map is a Series method whereas the rest are DataFrame methods. I got confused about apply and applymap methods though. Why do we have two methods for applying a function to a DataFrame? Again, simple examples which illustrate the usage would be great! Answer
Vectorize over the rows of an array
I have an array X and I want to apply a function f to all the rows of X: Now, y should be array([15,30], ‘i’). Which method or slicing magic will implement rows in the most efficient way? Answer NumPy implements the concept of “action over a particular axis”. The general function is numpy.apply_along_axis(): (where sum can of course be