Suppose I have a numpy array from which I want to remove a specific element. # data = np.array([ 97 32 98 32 99 32 100 32 101]) # collect indices where th
I'm trying to reshape an array of bitmap images that has a shape of (50,50,90000). How can I modify it so that I can get an array of (90000,50,50)? - I tried ar
I have dataframe where new columns need to be added based on existing column values conditions and I am looking for an efficient way of doing. For Ex: df = pd.D
You can see my dataframe below, x values are different value, but other values are same with left values, for example, column 15 and column 16 are same value. I
I have an array of indices like a = [2, 4, 1, 0, 3] and I want to transform it into np.argsort(a) = [3, 2, 0, 4, 1]. The problem is that argsort has O(n*log(n))
I have a numpy array of agents positions: positions = np.array([[row_0, col_0], [row_1, col_1], [row_2, col_2]]) I
Most similar questions relating to calculating this involve a single correlation value for each feature column, showing how the features in a dataset correlate
I expose the problem quickly: I have to create 3 classes. X Y and Z, inside X y and z are initialized as variables of class x but not as objects Y and Z. What i
i find an example in this Link which show 2-D array in pyqtgraph.ImageItem. import matplotlib.pyplot as plt import numpy as np import pyqtgraph as pg def main(
I have (a lot of) data like below y = [1, 3, 4, 5] which corresponds to the grid points x = [1, 2, 3, 4] On the other hand, I have a standard grid X = [1, 3]
Can someone explain these two formulas? Do they have any relationship? def _cosine_distance(a, b, data_is_normalized=False): if not data_is_normalized:
I'm having trouble with the formatting of my array when it it saved to a csv file, each time I run the program I get a different result, sometimes the string "c
My current code functions and produces a graph if there is only 1 sensor, i.e. if col2, and col3 are deleted in the example data provided below, leaving one col
Trying to understand what kind of output do I get from the histogram function. phase = mod(phase,Nper*2*pi) cl_phase = arange(0,Nper*2*pi+step,step) c,p = histo
import numpy as np import math as mt import matplotlib.pyplot as plt import mpl_toolkits.mplot3d.axes3d as axes3d #############################################
I have a data frame that has 3 columns and I want to plot a line graph based on some thresholds. Here is the data frame date income ratio 0 2022-0
Let's say I have a numpy array my_array = [0.2, 0.3, nan, nan, nan, 0.1, nan, 0.5, nan] For each nan value, I want to extract the two non-nan values to the lef
I am setting up a tunnel with gcloud from a remote machine with: gcloud compute start-iap-tunnel ... Everything used to be fine until a few days ago when it st
I want to try and recreate this functions from scratch (without using sklearn): # The matrix is M which is 1000x10 matrix. from sklearn.preprocessing import Po
Let's say that I have an array like this: array([[ 1, 2], [-1, -2], [ 0, 0], [-1, 2], [ 2, -1]]) I want to filter out all rows t