this probably has some duplicate threads but I honestly couldn't find a suitable solution. Pandas will be an easy solution but in the project I am working on will not include pandas as a dependency. I am trying to add column names to a Numpy array, basically turning it into structured array even though the data type are all the same. I tried: signal = np.array([[1,2,3],[1,2,3],[1,2,3]]) col_names = ('left','right','center') signal = np.array(signal, dtype = [(n, 'int16') for n in col_names]) but this returns: array([[(1, 1, 1), (2, 2, 2), (3, 3, 3)], [(1, 1, 1), (2, 2, 2), (3, 3, 3)], [(1, 1, 1), (2, 2, 2), (3, 3, 3)]], dtype=[('left', '<i2'), ('right', '<i2'), ('center', '<i2')]) Basically, I have a Numpy array representing a multi-channel signal. I want to be able to subset the channels using column names: signal['left'] == signal[:,0] # True signal[['left','center']] == signal[:,[0,2]] # True I also saw a post someone advised against using structured array. Is there a potential downside to it? Say it makes the array slower to access? Continue reading...
