How can I merge two datasets? I want to merge 2 data sets in one, but don't know the right approach to do it. The datasets are similar, the last column is the same - will or not them buy a product. In the first dataset, users who only will buy, in second - only who won't buy.   

The 1st dataset contains 500 rows and 2nd 10000 rows. What will be the right approach to merge it? How can I normalize them? And to point for an algorithm that the last column is the main sequence on what it should learn? 

Example:

id    income date will_buy

23123 200    10.5 Yes


and second dataset:

id    income date will_buy

2323  100    10.5 No
You can use append function:

final = df1.append(df2, ignore_index=True)


To set the last column as labels, you set them as so by:

labels = np.array(final["will_buy"])


So, when calling the fit method on the model you build, you set labels = labels.