numpy.cov() exception: 'float' object has no attribute 'shape'

Question

I have a dataset for different plant species, and I separated each species into a different np.array.

When trying to generate gaussian models out of these species, I had to calculate the means and covariance matrices for each different label.

The problem is: when using np.cov() in one of the labels, the function raises the error "'float' object has no attribute 'shape'" and I can't really figure out where the problem is coming from. The exact line of code I'm using is the following:

covx = np.cov(label0, rowvar=False) 

Where label0 is a numpy ndarray of shape (50,3), where the columns represent different variables and each row is a different observation.

The exact error trace is:

--------------------------------------------------------------------------- AttributeError                            Traceback (most recent call last) <ipython-input-81-277aa1d02ff0> in <module>()       2        3 # Get the covariances ----> 4 np.cov(label0, rowvar=False)  C:\Users\Matheus\Anaconda3\lib\site-packages\numpy\lib\function_base.py in cov(m, y, rowvar, bias, ddof, fweights, aweights)    3062             w *= aweights    3063  -> 3064     avg, w_sum = average(X, axis=1, weights=w, returned=True)    3065     w_sum = w_sum[0]    3066   C:\Users\Matheus\Anaconda3\lib\site-packages\numpy\lib\function_base.py in average(a, axis, weights, returned)    1143     1144     if returned: -> 1145         if scl.shape != avg.shape:    1146             scl = np.broadcast_to(scl, avg.shape).copy()    1147         return avg, scl  AttributeError: 'float' object has no attribute 'shape' 

Any ideas of what is going wrong?

Answer 1

The error is reproducible if the array is of dtype=object:

import numpy  as np  label0 = np.random.random((50, 3)).astype(object) np.cov(label0, rowvar=False) 

AttributeError: 'float' object has no attribute 'shape'

If possible you should convert it to a numeric type. For example:

np.cov(label0.astype(float), rowvar=False)  # works 

Note: object arrays are rarely useful (they are slow and not all NumPy functions deal gracefully with these - like in this case), so it could make sense to check where it came from and also fix that.