ENH: speed up wide DataFrame.line plots by using a single LineCollection #61764
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EvMossan
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mroeschke
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| threshold = 200 # switch when DataFrame has more than this many columns | ||
| can_use_lc = ( | ||
| not self._is_ts_plot() # not a TS plot | ||
| and not self.stacked # stacking not requested | ||
| and not com.any_not_none(*self.errors.values()) # no error bars | ||
| and len(self.data.columns) > threshold | ||
| ) | ||
| if can_use_lc: |
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I would prefer not to have a special casing like this because it's difficult to maintain parity between a "fast path" and the existing path.
Is there a way to refactor our plotting here to generalize the plotting to this form rather than the iterative approach below?
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Thanks for the suggestion, @mroeschke
Removed the early-return fast path; use_collection now only decides how we draw after the shared loop, so there’s one unified code path.
Let me know if you’d like anything tweaked.
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What does this PR change?
DataFrame.plot(kind="line")when the frame is “wide”.RangeIndexor integer/float values), is not a time-series plot, has no stacking
and no error bars, we now draw everything with a single
matplotlib.collections.LineCollectioninstead of oneLine2Dper column.cases above.
Performance numbers
df.plot(legend=False)Benchmarked on pandas 3.0.0.dev0+2183.g94ff63adb2, matplotlib 3.10.3, NumPy 2.2.6
Notes
DatetimeIndexplots—those remain on the original per-column path. A follow-up could combineLineCollectionwith thex_compat=Trueworkaround (see #61398) to similarly speed up time-series plots.> 200columns) is a heuristic and can be tuned in review.indices still use the original per-column draw, so behaviour there is
unchanged.
DataFrame.plotusingLineCollection#61532pytest pandas/tests/plotting -q)pre-commit run --all-files)doc/source/whatsnew/v3.0.0.rstcc @shadnikn @arthurlw – happy to take any feedback 🙂