Implement Series.plot.kde #767
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@@ Coverage Diff @@
## master #767 +/- ##
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- Coverage 93.9% 93.83% -0.07%
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Files 32 32
Lines 5691 5744 +53
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+ Hits 5344 5390 +46
- Misses 347 354 +7
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Let me merge this to move forward. |
ueshin
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Sep 25, 2019
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| pax = pdf['a'].plot('kde', bw_method=0.3) | ||
| kax = kdf['a'].plot('kde', bw_method=0.3) | ||
| self.compare_plots(pax, kax) |
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@HyukjinKwon Why does this still work even though the values are slightly different?
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This PR implements kde in DataFrame. It reuses Series' implementation. at #767 like Series's kde plot, Since DataFrame's also uses MLlib's KernelDensity API to calculate KDE , so slightly different from pandas but seems good enough either: <img width="884" alt="스크린샷 2019-09-17 오후 2 24 34" src="https://user-images.githubusercontent.com/44108233/65013645-e3933100-d956-11e9-9166-be6d534046bd.png"> <img width="882" alt="스크린샷 2019-09-17 오후 2 25 18" src="https://user-images.githubusercontent.com/44108233/65013684-ff96d280-d956-11e9-8714-6c860c4e2c13.png"> And also kde is an alias of 'density', you can get exactly same result when you use 'density' rather than 'kde' like below: <img width="886" alt="스크린샷 2019-09-17 오후 2 27 30" src="https://user-images.githubusercontent.com/44108233/65013769-66b48700-d957-11e9-84f0-5b5989ed2d49.png"> <img width="882" alt="스크린샷 2019-09-17 오후 2 27 09" src="https://user-images.githubusercontent.com/44108233/65013770-67e5b400-d957-11e9-875c-0fd73b32fa5f.png"> **Multiple columns examples:** <img width="819" alt="스크린샷 2019-09-17 오후 2 55 35" src="https://user-images.githubusercontent.com/44108233/65015007-45ee3080-d95b-11e9-9c31-a4b85631e404.png"> and for each row as Series.plot.kde looks same like below: <img width="739" alt="스크린샷 2019-09-17 오후 2 57 57" src="https://user-images.githubusercontent.com/44108233/65015172-b9903d80-d95b-11e9-8245-8b47190c38b6.png">
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This PR takes over #710
KDE, unlike other plots like line or area, it needs to calculates values via Spark so that we can compute it in a distributed manner.
This PR uses MLlib's KernelDensity API to calculate KDE. Since Spark only support scalar bandwidth, unlike SciPy that pandas' uses, Koalas will currently only support fixed scalar bandwidth only.
Implementation is different so the values are slightly different but seems good enough: