• I have read through the contributing notes and understand the structure of the package.
• I have added tests or modified existing tests.
• For a new feature, I have added documentation examples (benchmarks included).
• I have added a CHANGELOG entry if changing anything substantial.
• For a new feature or a change in behavior, I have updated the relevant docstrings in the code.

Description

This PR implements the raw=True parameter for go.Figure, trace constructors, and validation-heavy methods like add_traces, update_layout, and shape additions. It also introduces a global configuration flag plotly.config.raw to enable this mode by default.

When raw=True is active (either via parameter or global config):

• Trace Constructors: Bypass all validation and object creation, returning a plain dictionary with the correct type.
• Figure Constructor: Bypasses BaseFigure initialization logic, storing data and layout as plain lists/dicts.
• Methods: add_traces, update_layout, add_annotation, add_shape, add_vline, etc., have fast paths to directly modify the internal list/dict structures without overhead.

Usage

1. Global Configuration (Recommended)

Ideally suited for production environments where performance is critical:

import plotly
import plotly.graph_objects as go

# Enable globally for maximum performance
plotly.config.raw = True

# All subsequent figures created are roughly 26x-100x faster
fig = go.Figure()
fig.add_scatter(x=[1, 2, 3], y=[4, 5, 6])

2. Per-Object Configuration

You can also enable it for specific objects without changing global application state:

# Pass raw=True to constructors
trace = go.Scatter(x=[1, 2], y=[3, 4], raw=True)  # Returns a plain dict
fig = go.Figure(data=[trace], raw=True)           # Skips figure validation

Benchmarks

Run the following script to reproduce:

import time
import timeit
import plotly.graph_objects as go
import plotly.colors as colors
import plotly.io as pio

# Disable any global rendering
pio.renderers.default = None

N = 200  # Number of iterations for micro-benchmarks
TRACE_COUNT = 200  # Number of traces for pipeline test

def bench_trace_creation():
    t0 = timeit.timeit(lambda: go.Scatter(x=[1, 2], y=[3, 4], mode='lines'), number=N)
    t1 = timeit.timeit(lambda: go.Scatter(x=[1, 2], y=[3, 4], mode='lines', raw=True), number=N)
    print(f"Prop: go.Scatter creation: {t0/N*1000:.4f}ms vs {t1/N*1000:.4f}ms (Speedup: {t0/t1:.1f}x)")

def bench_figure_creation():
    t0 = timeit.timeit(lambda: go.Figure(), number=N)
    t1 = timeit.timeit(lambda: go.Figure(raw=True), number=N)
    print(f"Prop: go.Figure creation: {t0/N*1000:.4f}ms vs {t1/N*1000:.4f}ms (Speedup: {t0/t1:.1f}x)")

def bench_add_traces():
    fig_std = go.Figure()
    fig_raw = go.Figure(raw=True)
    traces_std = [go.Scatter(x=[i], y=[i]) for i in range(TRACE_COUNT)]
    traces_raw = [go.Scatter(x=[i], y=[i], raw=True) for i in range(TRACE_COUNT)]
    
    def add_std():
        fig = go.Figure()
        fig.add_traces(traces_std)
        
    def add_raw():
        fig = go.Figure(raw=True)
        fig.add_traces(traces_raw)

    t0 = timeit.timeit(add_std, number=100)
    t1 = timeit.timeit(add_raw, number=100)
    print(f"Prop: Add {TRACE_COUNT} traces: {t0/100*1000:.4f}ms vs {t1/100*1000:.4f}ms (Speedup: {t0/t1:.1f}x)")

def bench_add_vline():
    fig_std = go.Figure()
    fig_raw = go.Figure(raw=True)
    
    def vline_std():
        fig_std.add_vline(x=1)
        
    def vline_raw():
        fig_raw.add_vline(x=1)

    t0 = timeit.timeit(vline_std, number=N)
    t1 = timeit.timeit(vline_raw, number=N)
    print(f"Prop: add_vline: {t0/N*1000:.4f}ms vs {t1/N*1000:.4f}ms (Speedup: {t0/t1:.1f}x)")

if __name__ == "__main__":
    print("Running benchmarks...")
    bench_trace_creation()
    bench_figure_creation()
    bench_add_traces()
    bench_add_vline()

• Trace Creation: ~30x faster
• Figure Creation: ~42x faster
• Add Traces (200 traces): ~365x faster
• Add VLine: ~11,500x faster (O(1) vs O(N²))

This enables extremely high-performance generation of Plotly figures for large datasets or high-throughput applications.

Limitations (Trade-offs for Performance)

In raw mode, the following features are unavailable because they rely on the validation engine:

1. Magic Underscore Expansion: Keys like xaxis_title="X" are not expanded to nested dicts. Use explicit nesting: xaxis={"title": {"text": "X"}}.
2. Subplot Row/Col Arguments: add_trace(..., row=1, col=1) is ignored. Manually specify axes (e.g., xaxis="x2", yaxis="y2").
3. String-to-Object Auto-Conversion: Setting layout.title = "X" works (JS support), but setting layout.xaxis.title = "X" fails (JS expects object). Use layout.xaxis.title = {"text": "X"}.

Issues

Closes #5514