Given a list nested with numpy lists, I want to go through each index in each corresponding list and keep track of the count depending on the element, which would be stored into a single list. The minimum runnable code example below better showcases the problem. I have looked into from collections import defaultdict and from itertools import groupby, but
Tag: numpy
numpy image float array, to int 0..255 value
I got 3 NumPy data arrays r,g,b represented as a 2D float64 array (720×1024) Essentially per row, each channel is a bunch of floats: What I would like to to do is making it a channel that I can use in cv2.merge((r,g,b)) So that the float64 values per row get multiplied by 255 and something that cv2.merge() accepts. I think
Conditional lambda in pandas returns ValueError
In a df comprised of the columns asset_id, event_start_date, event_end_date, I wish to add a forth column datediff that for each asset_id will capture how many days passed between a end_date and the following start_date for the same asset_id, but in case that following start_date is earlier than the current end_date, I would like to capture the difference between the
Numpy: Optimal way to count indexs occurrence in an array
I have an array indexs. It’s very long (>10k), and each int value is rather small (<100). e.g. Now I want to count occurrence of each index value (e.g. 0 for 3 times, 1 for 2 times…), and get counts as np.array([3, 2, 1, 1, 1]). I have tested 4 methods as follows: UPDATE: _test4 is @Ch3steR’s sol: Run for
Reshape Pandas DatafRames by binary columns value
Can’t figure out how to reshape my DataFrame into new one by several binary columns value. Input: I want to reshape by binary values, i.e. column a/b/c, if their value == 1, I need every time new column with all data. Expected output: Stucked here from the morning, will appreciate help very much ! Answer Use DataFrame.melt with filtering 1
How to change binary value to an array with individual states
I have a question. How can I import value like this: to an array like this: and vice versa? Answer np.binary_repr function turn integers into binary representation string, so you could do: Output: Reverse direction (as requested by @sai): Output: Explanation: I build list with desceding powers of 2 (in this case [8,4,2,1]) using list comprehension then multiply arr (i.e.
How can I take the log of a column in Python using Numpy?
I have the the following df: I would like take logs of the whole column, but when I try: ..I get the following error: AttributeError: ‘int’ object has no attribute ‘log’ How can I fix this? I looked up other threads. They seem to explore causes, but don’t provide solutions. Answer This error means that you have used np as
What does np.fft.fftfreq actually do?
I have a monthly time series and I am taking the discrete fourier transform of it. However I am confused as to how numpy converts the time domain into frequency domain? I am using np.fft.fftfreq and my time array is is 708 indices long and each measurement of the data is computed every month. This is the output frequency using
Numpy.Matmul and Numpy.Dot: Accelerating code by using Numpy in-built functions
I am trying to speed up a function within my code. The initial function I had written was: where self.Asolution = odeint(…) , and same for self.Bsolution. self.Matrix is a square matrix of size self.N x self.N and simps is Simpson integration. self.Asolution and self.Bsolution have dimensions (t x N). However, I need to call this function many times, and
A robust way to keep the n-largest elements in rows or colums in the matrix
I would like to make a sparse matrix from the dense one, such that in each row or column only n-largest elements are preserved. I do the following: This approach works as intended but seems to be not the most efficient, and the robust one. What would you recommend to do it an better way? The example of usage: Answer