Time Travel and Time Series Analysis with pandas & statsmodels

Transcript

1. Time Travel and Time Series Analysis with pandas & statsmodels Alexander C. S. Hendorf @hendorf PYCON SETTE, Florence, 2016
2. 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
3. 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
4. 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 pd.Series index 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 1 2 3 4 5 6 7 8 9 pd.DataFrame 1 2 3 4 5 6 7 8 9
5. pandas Q'n'D
6. Year 12 Months February 90% of March 31 31 31 31 31 31 31 30 30 30 30 28
7. Roman year used to start in March and had 10 months 2 months there was "no" month solar | topical year quick & funny explanation: https://www.youtube.com/watch?v=AgKaHTh-_Gs
8. ?
9. use case
11. sales report 27.11.11 - 01.01.2012 5 weeks 4 weeks 4 weeks typical quarter November January DB stored only as sold in majority month
13. 2nd Call for Proposals reserved for Hot Topics ~first week of June ADVERTISEMENT
14. Data Mangling with mongoDB the Right Way tomorrow@17:15
15. Alexander C. S. Hendorf @hendorf LinkedIn