AttributeError: 'numpy.float64' object has no attribute 'log10'

Solution 1:

numpy.log10 is a "ufunc", and the method Series.apply(func) has a special test for numpy ufuncs which makes test.apply(log10) equivalent to np.log10(test). This means test, a Pandas Series instance, is passed to log10. The data type of test is object, which means that the elements in test can be arbitrary Python objects. np.log10 doesn't know how to handle such a collection of objects (it doesn't "know" that those objects are, in fact, all np.float64 instances), so it attempts to dispatch the calculation to the individual elements in the Series. To do that, it expects the elements themselves to have a log10 method. That's when the error occurs: the elements in the Series (in this case, np.float64 instances) do not have a log10 method.

A couple alternative expression that should do what you want are np.log10(test.astype(np.float64)) or test.astype(np.float64).apply(np.log10). The essential part is that test.astype(np.float64) converts the data type of the Series object from object to np.float64.

Solution 2:

I had a similar error message when using the standard deviation (np.std) instead of np.log10:

'AttributeError: 'numpy.float64' object has no attribute 'sqrt',

and this although I had previously converted the Pandas object X to a numpy array via np.asarray(X).

I could solve this problem by applying the above-mentioned solution:

X = pd.read_excel('file.xls')
Y = np.asarray(X).astype(np.float64)
Z = np.std(Y,axis=0)