Since I am creating a dataframe, I don't understand why I am getting an array error.
M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack()
M2 = np.maximum(-1, (M - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores)
M2.head(2)
AttributeError: 'numpy.ndarray' object has no attribute 'fillna'
(M - 3) is getting interpreted as a numpy.ndarray. This implies that M is defined somewhere as a numpy.ndarray. Test it out by running:
print type(M)
You are calling the .fillna() method on a numpy array. And numpy arrays don't have that method defined.
You can probably convert the numpy array to a pandas.DataFrame and then apply the .fillna() method.
Your code is not complete at the moment, so it is hard to pin point why M is causing an error. There could be a couple reasons:
You have a typo and (M - 3) should be (M2 - 3)
M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack()
M2 = np.maximum(-1, (M2 - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores)
M2.head(2)
You need to define/convert M as pandas.DataFrame somewhere else in your code
# With out seeing this part of the code, no one can really help you
M = pd.DataFrame(...)
# ...
# ...
M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack()
M2 = np.maximum(-1, (M - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores)
M2.head(2)
You could convert it to a pandas.DataFrame right before you use it.
M2 = df.groupby(['song_id', 'user_id']).rating.mean().unstack()
M2 = np.maximum(-1, (pd.DataFrame(M) - 3).fillna(0) / 2.) # scale to -1..+1 (treat "0" scores as "1" scores)
M2.head(2)
Mdefined as?