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Downsampling y upmpling

import pandas as pd
import numpy as np

np.random.seed(0)
rng = pd.date_range('2015-02-24', periods=10, freq='T')
df = pd.DataFrame({'Val' : np.random.randn(len(rng))}, index=rng)  
print (df)
                          Val
2015-02-24 00:00:00  1.764052
2015-02-24 00:01:00  0.400157
2015-02-24 00:02:00  0.978738
2015-02-24 00:03:00  2.240893
2015-02-24 00:04:00  1.867558
2015-02-24 00:05:00 -0.977278
2015-02-24 00:06:00  0.950088
2015-02-24 00:07:00 -0.151357
2015-02-24 00:08:00 -0.103219
2015-02-24 00:09:00  0.410599
#downsampling with aggregating sum
print (df.resample('5Min').sum())
                          Val
2015-02-24 00:00:00  7.251399
2015-02-24 00:05:00  0.128833

#5Min is same as 5T
print (df.resample('5T').sum())
                          Val
2015-02-24 00:00:00  7.251399
2015-02-24 00:05:00  0.128833

#upsampling and fill NaN values method forward filling
print (df.resample('30S').ffill())
                          Val
2015-02-24 00:00:00  1.764052
2015-02-24 00:00:30  1.764052
2015-02-24 00:01:00  0.400157
2015-02-24 00:01:30  0.400157
2015-02-24 00:02:00  0.978738
2015-02-24 00:02:30  0.978738
2015-02-24 00:03:00  2.240893
2015-02-24 00:03:30  2.240893
2015-02-24 00:04:00  1.867558
2015-02-24 00:04:30  1.867558
2015-02-24 00:05:00 -0.977278
2015-02-24 00:05:30 -0.977278
2015-02-24 00:06:00  0.950088
2015-02-24 00:06:30  0.950088
2015-02-24 00:07:00 -0.151357
2015-02-24 00:07:30 -0.151357
2015-02-24 00:08:00 -0.103219
2015-02-24 00:08:30 -0.103219
2015-02-24 00:09:00  0.410599


Modified text is an extract of the original Stack Overflow Documentation
Licenciado bajo CC BY-SA 3.0
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