Time travel and time series analysis with pandas + statsmodels
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Time Travel and Time Series Analysis with pandas & statsmodels
Alexander C. S. Hendorf @hendorf
PYCON SETTE, Florence, 2016
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Alexander C. S. Hendorf
CTO Königsweg GmbH
15+ years in software development
Always love data and new ideas
Mannheim mongoDB meet-up Organizer
mongoDB master, certified mongoDB DBA
EuroPython organizer + program chair
speaker mongoDB world NYC, CEBIT,…
Hobbies: see above
@hendorf
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Pandas • data analysis library • simple interface • making data analysis fast, efficient and easy • started by Wes McKinney in 2008 • DataFrame object for data manipulation with integrated indexing (R) • Reshape & pivot data • Merge & join data • high-level building block for doing practical, real world data analysis in Python • database like operations • import pandas as pd
Statsmodels
is a Python module that allows users to explore data, estimate statistical models, and perform statistical tests
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pd.Series
index
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pd.DataFrame
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pandas Q'n'D
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Year
12 Months
February
90% of March
31 31
31 31 31
31 31
30
30
30 30
28
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RomanyearusedtostartinMarchandhad10months
2monthstherewas"no"month
solar|topicalyear
quick&funnyexplanation:https://www.youtube.com/watch?v=AgKaHTh-_Gs
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?
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use case
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salesreport27.11.11-01.01.2012
5weeks
4weeks
4weeks
typicalquarter
November
January
DBstoredonlyassoldinmajoritymonth
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2nd Call for Proposals reserved for Hot Topics
~first week of June
ADVERTISEMENT
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Data Mangling with mongoDB the Right Way
tomorrow@17:15
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Alexander C. S. Hendorf
@hendorf