Compare time series SSE (sum squared error)

time_series_compare.py

import pandas as pd

data_1 = {
    'date': [   '2014-05-01 18:47:05.069722',
                '2014-05-01 18:47:05.119994',
                '2014-05-02 18:47:05.178768',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.280592',
                '2014-05-03 18:47:05.332662',
                '2014-05-03 18:47:05.385109',
                '2014-05-04 18:47:05.436523',
                '2014-05-04 18:47:05.486877'
            ],
    'data': [34, 25, 26, 15, 15, 14, 26, 25, 62, 41]
}
data_2 = {
    'date': [   '2014-05-01 18:47:05.069722',
                '2014-05-01 18:47:05.119994',
                '2014-05-02 18:47:05.178768',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.280592',
                '2014-05-03 18:47:05.332662',
                '2014-05-03 18:47:05.385109',
                '2014-05-04 18:47:05.436523',
                '2014-05-04 18:47:05.486877'
            ],
    'data': [1, 2, 3, 4, 5, 6, 7, 8, 9, 111]
}
data_3 = {
    'date': [   '2014-05-01 18:47:05.069722',
                '2014-05-01 18:47:05.119994',
                '2014-05-02 18:47:05.178768',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.230071',
                '2014-05-02 18:47:05.280592',
                '2014-05-03 18:47:05.332662',
                '2014-05-03 18:47:05.385109',
                '2014-05-04 18:47:05.436523',
                '2014-05-04 18:47:05.486877'
            ],
    'data': [30, 20, 20, 16, 15, 13, 22, 21, 50, 31]
}

df1 = pd.DataFrame(data_1, columns = ['date', 'data'])
df2 = pd.DataFrame(data_2, columns = ['date', 'data'])
df3 = pd.DataFrame(data_3, columns = ['date', 'data'])

print 'Comparison df1 vs df2', ((df2['data'] - df1['data']) ** 2).sum()
print 'Comparison df1 vs df2', ((df3['data'] - df1['data']) ** 2).sum()