PERF: improve plotting performance by not stringifying all x data

[jorisvandenbossche]

Closes #18236

Currently when plotting all x / index data are converted to strings, while you typically only need a few tick labels. So when you have a lot of data, this cause the pandas plotter to be hugely slower than needed (and than a pure matplotlib one)

On master:

[ 33.33%] ··· Running plotting.Plotting.time_frame_plot                        4.43s
[ 50.00%] ··· Running plotting.Plotting.time_series_plot                       4.19s
[ 66.67%] ··· Running plotting.TimeseriesPlotting.time_plot_irregular          72.2±0.2ms
[ 83.33%] ··· Running plotting.TimeseriesPlotting.time_plot_regular            108±0.5ms
[100.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular_compat     71.5±0.8ms

with this branch:

[ 33.33%] ··· Running plotting.Plotting.time_frame_plot                        132±20ms
[ 50.00%] ··· Running plotting.Plotting.time_series_plot                       71.4±30ms
[ 66.67%] ··· Running plotting.TimeseriesPlotting.time_plot_irregular          58.7±0.5ms
[ 83.33%] ··· Running plotting.TimeseriesPlotting.time_plot_regular            96.9±2ms
[100.00%] ··· Running plotting.TimeseriesPlotting.time_plot_regular_compat     57.4±0.8ms

So for very simple plot when from 4s to ca 100ms (which is much closer to the pure matplotlib performance)

[codecov]

Codecov Report

Merging #18373 into master will decrease coverage by 0.01%.
The diff coverage is 85.71%.

@@            Coverage Diff             @@
##           master   #18373      +/-   ##
==========================================
- Coverage   91.38%   91.36%   -0.02%     
==========================================
  Files         164      164              
  Lines       49797    49800       +3     
==========================================
- Hits        45508    45502       -6     
- Misses       4289     4298       +9

Flag	Coverage Δ
#multiple	89.17% <85.71%> (ø)	⬆️
#single	39.55% <0%> (-0.07%)	⬇️

Impacted Files	Coverage Δ
pandas/plotting/_core.py	82.49% <85.71%> (+0.03%)	⬆️
pandas/io/gbq.py	25% <0%> (-58.34%)	⬇️
pandas/core/frame.py	97.8% <0%> (-0.1%)	⬇️

---

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[codecov]

Codecov Report

Merging #18373 into master will decrease coverage by 0.03%.
The diff coverage is 85.71%.

@@            Coverage Diff             @@
##           master   #18373      +/-   ##
==========================================
- Coverage   91.38%   91.34%   -0.04%     
==========================================
  Files         164      164              
  Lines       49797    49721      -76     
==========================================
- Hits        45508    45420      -88     
- Misses       4289     4301      +12

Flag	Coverage Δ
#multiple	89.14% <85.71%> (-0.03%)	⬇️
#single	39.61% <0%> (-0.01%)	⬇️

Impacted Files	Coverage Δ
pandas/plotting/_core.py	82.49% <85.71%> (+0.03%)	⬆️
pandas/io/gbq.py	25% <0%> (-58.34%)	⬇️
pandas/core/indexes/interval.py	92.52% <0%> (-0.34%)	⬇️
pandas/core/indexes/category.py	97.2% <0%> (-0.26%)	⬇️
pandas/tseries/offsets.py	96.7% <0%> (-0.23%)	⬇️
pandas/core/frame.py	97.8% <0%> (-0.1%)	⬇️
pandas/core/indexes/base.py	96.42% <0%> (ø)	⬆️
pandas/core/indexes/multi.py	96.4% <0%> (+0.02%)	⬆️

---

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[jreback]

lgtm. needs a whatsnew, prob ok for 0.21.1

[jreback]

is there an associated issue?

[TomAugspurger]

#18236

[jorisvandenbossche]

Ah, yes, didn't see that one, but from the description this PR is exactly fixing that :-)

[jorisvandenbossche]

@TomAugspurger any comments on the content of the PR?

[TomAugspurger]

Looks good.