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The optimization achieves a **58x speedup** by eliminating the major performance bottleneck in pandas DataFrame processing. **Key optimizations:** 1. **Pre-fetch column data as numpy arrays**: The original code used `df.iloc[index][key]` for each cell access, which triggers pandas' slow row-based indexing mechanism. The optimized version extracts all column data upfront using `df[key].values` and stores it in a dictionary, then uses direct numpy array indexing `columns[key][index]` inside the loop. 2. **More efficient key validation**: Replaced the nested loop checking for missing keys with a single list comprehension `missing_keys = [key for key in REQUIRED_GANTT_KEYS if key not in df]`. 3. **Use actual DataFrame columns**: Instead of iterating over the DataFrame object itself (which includes metadata), the code now uses `list(df.columns)` to get only the actual column names. **Why this is dramatically faster:** - `df.iloc[index][key]` creates temporary pandas Series objects and involves complex indexing logic for each cell - Direct numpy array indexing `columns[key][index]` is orders of magnitude faster - The line profiler shows the original `df.iloc` line consumed 96.8% of execution time (523ms), while the optimized dictionary comprehension takes only 44.9% (4.2ms) **Performance characteristics:** - **Large DataFrames see massive gains**: 8000%+ speedup on 1000-row DataFrames - **Small DataFrames**: 40-50% faster - **List inputs**: Slight slowdown (3-13%) due to additional validation overhead, but still microsecond-level performance - **Empty DataFrames**: Some slowdown due to upfront column extraction, but still fast overall This optimization is most beneficial for DataFrame inputs with many rows, where the repeated `iloc` calls created a severe performance bottleneck.
Co-authored-by: Cameron DeCoster <cameron.decoster@gmail.com>
…e_gantt-mhcxyu68 ⚡️ Speed up function `validate_gantt` by 58x
- Check template.data.<trace_type> for marker.color or line.color before falling back to template.layout.colorway - Handle timeline special case (maps to bar trace type) - Use marker colors first, fall back to line colors if no markers found - Fixes issue #5416
- Modify apply_default_cascade to check template.data.<trace_type> for marker.color or line.color - Fallback to template.layout.colorway if trace-specific colors not found - Add comprehensive tests for trace-specific color sequences - Handle timeline special case (maps to bar trace type) - Follow existing patterns for symbol_sequence and line_dash_sequence Fixes #5416
- Modify apply_default_cascade to read colors from template.data.<trace_type> - Prioritize trace-specific colors over layout.colorway - Add special case for timeline constructor (maps to bar trace type) - Add comprehensive tests for trace-specific color sequences - Test trace type isolation, fallback behavior, and timeline special case
…plates fix: Update GitHub issue templates
- Make constructor parameter required (was optional with None default) - Refactor trace-specific color extraction to only assign when colors are found - Improve code clarity by checking for non-empty color list before assignment
- Remove redundant check that was setting color_discrete_sequence to None - The check is unnecessary since we only assign trace_specific_colors when any() is True - Fallback logic to layout.colorway and qualitative.D3 remains intact
Co-authored-by: Emily KL <4672118+emilykl@users.noreply.github.com>
[Fix] Support trace-specific color sequences in Plotly Express via templates
Merge doc prod to main branch
Update example to use geodatasets
write_image() defaulted engine='auto' and passed it to to_image(), which treats any non-None engine value as explicitly user-specified and emits a deprecation warning. By defaulting to None (matching to_image's expected interface), the warning only appears when the user actually passes an engine argument. Fixes #5512
…-warning Fix spurious engine deprecation warning in write_image
Co-authored-by: Emily KL <4672118+emilykl@users.noreply.github.com>
upgrade plotly.js to v3.4.0
Docs for 6.6
Release v6.6.0
Update docs to latest plotly.py version
Update API reference doc version
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