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.

Release v6.5.0

Update plotly.py version for docs

Co-authored-by: Cameron DeCoster <cameron.decoster@gmail.com>

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⚡️ Speed up function `validate_gantt` by 58x

…plates

fix: Update GitHub issue templates