Questions tagged [numpy-ndarray]
The Numpy Ndarray is a type of array that represents collections of the same data type within the NumPy Python library. If you have any questions regarding this specific array type, feel free to use this tag.
As I progress through my deep learning course on Coursera, I stumbled upon a piece of code on GitHub while working on an assignment. 1. numpyArr[...,c] 2. numpyArr[:,:,:,c] I'm curious to know what distinguishes these two slicing methods? ...
I have a pandas dataframe with the following structure: pd df However, I am looking to modify the 'KDB' column to look like this (without the index). Can anyone help me achieve this? [9. 3. 3. 2. .....] I attempted to use pd.DataFrame.to_nu ...
Variables: x and y represent arrays of N 2D vectors with sizes (N, 2). Question: Can the dot product be calculated between corresponding vectors in x and y without explicitly listing the elements using list comprehension: [np.dot(x[i], y[i]) for i in ra ...
How can we solve this particular issue? I have an array with 30 rows containing columns (seq, high, low, status). The objective is to merge similar rows only when they are adjacent to each other in the sequence. The resulting row will have the maximum high ...
I am currently working with a 3D NumPy array of size (9,9,200) and a 2D array of size (200,200). My goal is to take each channel of shape (9,9,1), multiply it by 200 scalars in a single row, and then average the result to obtain an array of shape (9,9,1). ...
Having a 3-dimensional Numpy array containing only 0s and 1s as shown below: array([[[ 1, 1, 0, 1], [ 0, 0, 1, 1]], [[ 1, 1, 1, 1], [ 0, 1, 0, 1]]]) The objective is to perform an "addition" operation on each value within a "line" ...
Imagine the scenario: def x_squared(x): result = x * x return result def twice_x(x): result = 2 * x return result def x_cubed(x): result = x * x * x return result x_values = np.array([1, 2, 3]) functions = np.array([x_squared, t ...
If I have an array called "a" with random integer values generated using numpy like this: a = np.random.randint(0,20,10), I want to permute its elements based on a given probability value p. For example, if p = 0.2, each element should have a 20% chance ...
Is there a way to sort an array of arrays based on the values of their elements using numpy without resorting to structured arrays? I want to sort the inner arrays first by the value of the first element, then the third element, and finally the second elem ...