Data preprocessing, relative scale problems in features of same type
If want you want is to scale both columns using the maximum of both and the minimum of both. You can do it like this:

df = pd.DataFrame(data=data, columns=['F1', 'F2'])
columns = ['F1', 'F2']

ma = float(np.amax(df[columns].values))
mi = float(np.amin(df[columns].values))

for column in columns:
    df[column] = df[column].apply(lambda e: (e - mi) / (ma - mi))


According to the documentation of sklearn that is what MaxMinScaler does, if you want to use predefined values for max and min, you can just change ma and mi. If you have more columns you just have to change the list of columns. For rescaling to an arbitrary range (max, min) read the sklearn documentation on MaxMinScaler.