EDIT: fixed typo.

This works great and is even more useful with flipSTAR's clarification regarding adding a global layout to the combined fig. However, sometimes a global layout doesn't cover everything. For example, in my case (a stacked bar and two single scatter plot lines), my global layout caused me to lose my scatter plot legends. Fortunately, you can add additional arguments to the combined fig by targeting the specific figures. e.g., given a hypothetical:

fig1 = px.bar(...)
fig2 = px.line(...)
fig3 = px.line(...)
all_fig = go.Figure(data=fig1.data + fig2.data + fig3.data, layout = fig1.layout)

Which is a bar chart and two scatter plots each with a single line, you can add the legends for each line with:

all_fig['data'][1]['showlegend']=True
all_fig['data'][1]['name']='Line 1 Name'
all_fig['data'][1]['hovertemplate']='Line 1 Name<br>x=%{x}<br>y=%{y}<extra></extra>'
all_fig['data'][2]['showlegend']=True
all_fig['data'][2]['name']='Line 2 Name'
all_fig['data'][2]['hovertemplate']='Line 2 Name<br>x=%{x}<br>y=%{y}<extra></extra>'

(the bar is all_fig['data'][0]).

For some reason, the name won't show up on hover unless you explicitly add it to 'hovertemplate.'

This is probably related to this Github issue - your example plot has on the order of 1000 data points, which is the point at which plotly-express alters its plotting strategy.

The solution is to manually specify the renderer as "svg":

fig = px.scatter(
    result, 
    x='time', 
    y='y_value', 
    <other args>,
    render_mode='svg'
)

As far as I understand your question, I would try to answer it.

The parameter 'color' only accepts the column names.
In your case, you can consider using update_traces()

import plotly.express as px
df = px.data.iris() # iris is a pandas DataFrame
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.update_traces(marker=dict(
        color='red'))
fig.show()

Reference: https://plotly.com/python/marker-style/