Preprocessing in scikit learn - single sample - Depreciation warning

Just listen to what the warning is telling you:

Reshape your data either X.reshape(-1, 1) if your data has a single feature/column and X.reshape(1, -1) if it contains a single sample.

For your example type(if you have more than one feature/column):

temp = temp.reshape(1,-1) 

For one feature/column:

temp = temp.reshape(-1,1)

Well, it actually looks like the warning is telling you what to do.

As part of sklearn.pipeline stages' uniform interfaces, as a rule of thumb:

• when you see X, it should be an np.array with two dimensions

• when you see y, it should be an np.array with a single dimension.

Here, therefore, you should consider the following:

temp = [1,2,3,4,5,5,6,....................,7]
# This makes it into a 2d array
temp = np.array(temp).reshape((len(temp), 1))
temp = scaler.transform(temp)

.values.reshape(-1,1) will be accepted without alerts/warnings

.reshape(-1,1) will be accepted, but with deprecation war

This might help

temp = ([[1,2,3,4,5,6,.....,7]])