The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns
If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.
selected_feat= X.columns[(sel.get_support())]
This will return a list of the columns kept by the feature selector.
If you wanted to see how many features were kept you can just run this:
sel.get_support().sum() # by default this will count 'True' as 1 and 'False' as 0
Answer from Djib2011 on Stack ExchangeThe problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns
If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.
selected_feat= X.columns[(sel.get_support())]
This will return a list of the columns kept by the feature selector.
If you wanted to see how many features were kept you can just run this:
sel.get_support().sum() # by default this will count 'True' as 1 and 'False' as 0
because this :
X = df.iloc[:,:24481].values
y = df.iloc[:, -1].values
you should remove .values or make extra X_col, y_col like that
X_col = df.iloc[:,:24481]
y_col = df.iloc[:, -1]
The solution:
The given dataset was already an array, so I didn’t need to call .values.
The problem lies in the following line:
df = StandardScaler().fit_transform(df)
It returns a NumPy array (see the documentation), which does not have a drop function. You would have to convert it into a pd.DataFrame first!
new_df = pd.DataFrame(StandardScaler().fit_transform(df), columns=df.columns, index=df.index)
'numpy.ndarray' object has no attribute 'numpy'.
AttributeError: 'numpy.ndarray' object has no attribute 'values'
python - AttributeError: 'numpy.ndarray' object has no attribute 'values' - Stack Overflow
Error creating numpy v-stack, 'AttributeError: 'numpy.ndarray' object has no attribute 'np'
Videos
On the line
mlp.fit(X_train, y_train.values.ravel())
y_train is of type numpy.ndarray and, as staded on the error message
has no attribute 'values'
If you have properly encoded this array, you should be able to use it simply as
mlp.fit(X_train, y_train)
I had the same error but this worked
random_forest.fit(X_train,y_train.ravel())
Hi,
I'm trying to create a numpy v-stack and creating 3 np.array's for it, by filling them with a loop:
I get the error: 'AttributeError: 'numpy.ndarray' object has no attribute 'np' . I think I'm using the wrong notation to append to the empty arrays:
neighbor_id = [id_ for id_ in range(1, n_obs) if id_ != user_id]
neighbor_id_arr = np.array(neighbor_id)
similarity = np.array([])
num_interactions = np.array([])
# get similarity and num_interactions
for id_ in neighbor_id:
similarity.np.append(np.dot(user_item.loc[user_id],user_item.loc[id_])) #The issue is here, I think
num_interactions.np.append(user_interactions.loc[id_])
c = numpy.vstack((neighbor_id_arr, similarity,num_interactions))
Thanks!
James
https://imgur.com/gallery/yAdAjdx
Hello,
Linked is the screenshot of the two error messages I keep recieving as well as the code leading up to it. I'm trying to run an uplift on a classification tree. Any help is appreciated, I am still fairly new to python. Thank you!!