Plotly histogram continuous colors based on "count of" column?

Question

I have created a histogram that shows the amount of days hotter than 35° celsius per decade ("Jahrzehnt" in German). I would like to color it based on the amount of hot days (here the "Temperatur, Maximum" column), however as you can see in the picture, the hottest decade (2010) got the fainted color although I want it to be dark red.

my code is

    fig = px.histogram(grouper, x='Jahrzehnt', y='Temperatur, Maximum', color='Temperatur, Maximum', \
        color_discrete_sequence=px.colors.sequential.Reds,
        title='Tage über 35° Celsius ("Wüstentage")',
        hover_data=dict(Jahrzehnt=False)
        )
    fig.update_layout(showlegend=False)
    fig.update_yaxes(title='Anzahl')

How can change the color_discrete_sequence to make it match lowest and highest values with fainted to satured reds?

Thank you for any hints.

Answer 1

• using https://plotly.com/python-api-reference/generated/plotly.express.colors.html sample_colorscale() you can build a discrete sequence from a continuous map
• have used a list comprehension, plus changed color to be x-axis not y-axis column
• full code below

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

grouper = pd.DataFrame(
    {
        "Jahrzehnt": range(1870, 2011, 10),
        "Temperatur, Maximum": np.random.randint(2, 40, 15),
    }
)
grouper["Jahrzehnt"] = grouper["Jahrzehnt"].astype(str) + "er"

fig = px.histogram(
    grouper,
    x="Jahrzehnt",
    y="Temperatur, Maximum",
    color="Jahrzehnt",
    color_discrete_sequence=[
        px.colors.sample_colorscale("reds", v)[0]
        for v in (
            grouper["Temperatur, Maximum"] / grouper["Temperatur, Maximum"].max()
        ).tolist()
    ],
    title='Tage über 35° Celsius ("Wüstentage")',
    hover_data=dict(Jahrzehnt=False),
)
fig.update_layout(showlegend=False)
fig.update_yaxes(title="Anzahl")