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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'
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Use numpy.array_equiv() function to check whether two arrays are equal or not in Python. This function returns True if both arrays have the same shape and all the elements are equal and return False otherwise.
The array object in NumPy is called ndarray . We can create a NumPy ndarray object by using the array() function.
Check if two arrays are equal or not using SortingSort both the arrays. Then linearly compare elements of both the arrays. If all are equal then return true, else return false.
To convert an array to a dataframe with Python you need to 1) have your NumPy array (e.g., np_array), and 2) use the pd. DataFrame() constructor like this: df = pd. DataFrame(np_array, columns=['Column1', 'Column2']) . Remember, that each column in your NumPy array needs to be named with columns.
(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.
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