Inconsistent Results When Filtering Numpy Arrays Based on Conditions
After trying multiple solutions online, I still can't figure this out. I'm working with an scenario when trying to filter a large numpy array based on specific conditions. I have a 2D numpy array of shape (1000, 10) representing some measurements, and I'm trying to extract rows where the value in the first column is greater than 50 and the value in the second column is less than 100. For some reason, the results I'm getting seem inconsistent. Here's the code snippet I'm using: ```python import numpy as np # Simulating some data np.random.seed(42) data = np.random.randint(0, 150, size=(1000, 10)) # Filtering condition filtered_data = data[(data[:, 0] > 50) & (data[:, 1] < 100)] # Output the shape of the filtered data print(filtered_data.shape) ``` When I run this code, I sometimes get an output shape of (0, 10), which is unexpected since I know there are values in the array that meet the conditions. I've verified that the data indeed has values greater than 50 in the first column and less than 100 in the second column, but the filtered result is empty at times. I've also tried printing the rows that meet each individual condition before filtering, and they seem to be accurate: ```python condition1 = data[:, 0] > 50 condition2 = data[:, 1] < 100 print(data[condition1]) # Should show rows where the first column > 50 print(data[condition2]) # Should show rows where the second column < 100 ``` The output from these prints shows that there are indeed matching rows for each condition when checked separately. However, when I combine the conditions with the `&` operator, it fails to return any results. I suspect it might be related to the order of operations or how I'm combining the boolean arrays, but I'm not sure. I'm using numpy version 1.23.0. Any insights on why this might be happening and how to resolve it would be greatly appreciated! What am I doing wrong? Thanks in advance!