px.density_contour() ignores color labels for negative histfunc results

[avant1]

Consider the following example:

import plotly.express as px
import pandas as pd
import numpy as np
import plotly


np.random.seed(0)

n_samples = 1000
df = pd.DataFrame({
    'x': np.random.randint(low=1, high=5, size=n_samples),
    'y': np.random.randint(low=1, high=4, size=n_samples),
    'z': np.random.normal(loc=-40, scale=20, size=n_samples),
})

df['z'] = df['z'] * df['x']

# uncomment to make some values above 0
# df['z'] = df['z'] + 80

# uncomment to make all values above 0
# df['z'] = df['z'].abs()

fig = px.density_contour(df, x='x', y='y', z='z', histfunc='avg', height=800, width=1200, title=f'Plotly version: {plotly.__version__}')
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()

It calculates average of mostly negative values, which results in the following plot:

Color bar has correct color range, but all data points use same color - yellow, which is not particularly useful. Similar situation happens when only part of data is negative (uncomment first commented line in the example):

Left (positive) section looks fine, while right (negative) is colored with only one color.

There are workarounds, that involve changing of input data, but this makes plot reading much harder.