I am trying to regularize an uneven time series with Pandas as in this example https://stackoverflow.com/a/39730730/10005441.
However, my process gets killed with exit code 137 ("(interrupted by signal 9: SIGKILL)") when I try to do so on my own dataset. From what I've read online so far, it seems like the error is caused by a memory leak. This is confirmed by the fact that the process seems to take over all the available application memory.
But since I am not defining any variables that I could later dereference to free up space (I only call built-in methods in my code), I don't know how I can fix the memory leak.
I thought at first that my dataset might be too large for this process (about 1500 rows), but I kept restricting to fewer and fewer rows until I got down to just 70, and it still seems to cause the same problem – only now the process does not interrupt by itself, it freezes and I need to kill it manually.
import io
import pandas as pd
data = io.StringIO('''\
Values
1900-2-18 00:00:00, 2.4061398208006928\n1900-4-6 00:00:00, 4.190919536676638
1900-5-20 00:00:00, 7.154316563897394\n1900-6-28 00:00:00, 8.511064948122844
1900-7-29 00:00:00, 12.948325882041525\n1900-9-3 00:00:00, 14.874496695573287
1900-10-18 00:00:00, 11.275824547647606\n1900-12-13 00:00:00, 3.7065864698234683
1901-3-3 00:00:00, 7.2656643017780995\n1901-8-11 00:00:00, 3.132476380916307
1901-12-31 00:00:00, 3.255504055908594\n1902-3-6 00:00:00, 2.558366292009146
1902-5-11 00:00:00, 4.3928567952933095\n1902-7-16 00:00:00, 5.697896757357601
1902-10-2 00:00:00, 7.002936719421891\n1902-12-9 00:00:00, 5.736406393587798
1903-1-20 00:00:00, 9.328924220179179\n1903-2-27 00:00:00, 8.587849660274507
1903-4-15 00:00:00, 7.392418135961156\n1903-6-3 00:00:00, 9.917996320293712
1903-7-15 00:00:00, 6.590267808814529\n1903-8-26 00:00:00, 3.2153378869541758
1903-10-7 00:00:00, 2.996751882107189\n1903-11-15 00:00:00, 2.712339561397424
1903-12-20 00:00:00, 1.319131420500554\n1904-1-19 00:00:00, 0.8865938043571631
1904-2-14 00:00:00, 1.9964471435094566\n1904-3-9 00:00:00, 3.083502456213582
1904-4-4 00:00:00, 4.170557768917708\n1904-4-28 00:00:00, 3.831315100660575
1904-5-23 00:00:00, 2.309160171614012\n1904-6-19 00:00:00, 3.694378817767278
1904-7-14 00:00:00, 5.6004781490273725\n1904-8-2 00:00:00, 4.816350292831508
1904-9-1 00:00:00, 5.603998055036929\n1904-10-5 00:00:00, 3.631258067715407
1904-11-2 00:00:00, 2.649097889985815\n1904-12-2 00:00:00, 1.608099020817645
1905-1-11 00:00:00, 1.2811050146707985\n1905-3-8 00:00:00, 1.1295258409634243
1905-5-15 00:00:00, 5.369997012480915\n1905-7-18 00:00:00, 6.8334105299517365
1905-9-14 00:00:00, 9.888561079276236\n1905-11-21 00:00:00, 9.820776125433214
1906-2-17 00:00:00, 8.583688873547414\n1906-5-27 00:00:00, 5.669451125498982
1906-9-22 00:00:00, 7.403538545288166\n1907-2-19 00:00:00, 5.589027737652207
1907-6-2 00:00:00, 4.904431053393889\n1907-7-21 00:00:00, 5.383923257816266
1907-9-8 00:00:00, 2.5896575192353657\n1907-10-27 00:00:00, 1.5265738784902498
1907-12-16 00:00:00, 1.4187730996080212\n1908-2-5 00:00:00, 1.6819846618479541
1908-4-4 00:00:00, 3.25556162055965\n1908-6-26 00:00:00, 6.196662536751723
1908-10-2 00:00:00, 6.518587879075245\n1908-12-24 00:00:00, 4.02972145733511
1909-2-22 00:00:00, 4.388033210271457\n1909-4-14 00:00:00, 6.725186739916857
1909-6-9 00:00:00, 9.675973353608608\n1909-8-10 00:00:00, 6.518904258510972
1909-10-27 00:00:00, 3.533225003441829\n1910-1-6 00:00:00, 1.4671928484167065
1910-2-26 00:00:00, 1.1987894438707483\n1910-4-17 00:00:00, 4.4867671003426945
1910-6-8 00:00:00, 6.4285613573922795\n1910-8-6 00:00:00, 5.553929676414903
1910-10-7 00:00:00, 9.853959878865188\n1910-12-2 00:00:00, 2.982478843773414
''')
s = pd.read_csv(data, squeeze=True)
s.index = pd.to_datetime(s.index)
res = s.resample('s').interpolate().resample('1AS').asfreq().dropna()
print(res)
