reshape is a methode under numpy's library and as the error printed in your terminal the object list has no methode defined as reshape

import numpy as np 
w_train=np.array(x_train)

then you can simply use the reshape function without any error :

w_train=x_train.reshape((2404,28,224,224,3))

you can check the reshape characteristics in this link for more understanding of the methode

You first list has one more item (5 vs 4 for the others). This makes it an object array with python lists as elements (and you get a VisibleDeprecationWarning warning).

Thus cal becomes a concatenation of the lists ([56.0, 0.0, 4, 4, 68.0, 1.2, 104.0, 52.0, 8.0, 1.8, 135.0, 99.0, 0.9]) instead of a numpy array, triggering your error.

I imagine this might be a typo and changing 4,4 to 4.4 gives the following output:

A = np.array([[56.0, 0.0, 4.4, 68.0],
              [1.2, 104.0, 52.0, 8.0],
              [1.8, 135.0, 99.0, 0.9],])

cal = A.sum(axis=0)
# array([ 59. , 239. , 155.4,  76.9])

percentage = 100*A/cal.reshape(1,4)
print(percentage)

output:

[[94.91525424  0.          2.83140283 88.42652796]
 [ 2.03389831 43.51464435 33.46203346 10.40312094]
 [ 3.05084746 56.48535565 63.70656371  1.17035111]]

Alternatively, you can use np.shape(...)

For instance:

import numpy as np
a=[1,2,3]

and np.shape(a) will give an output of (3,)

The solution is indeed to do:

Y.values.reshape(-1,1)

This extracts a numpy array with the values of your pandas Series object and then reshapes it to a 2D array.

The reason you need to do this is that pandas Series objects are by design one dimensional. Another solution if you would like to stay within the pandas library would be to convert the Series to a DataFrame which would then be 2D:

Y = pd.Series([1,2,3,1,2,3,4,32,2,3,42,3])

scaler = StandardScaler()

Ys = scaler.fit_transform(pd.DataFrame(Y))