How to plot discrete colours with plotly

Question

My code so far:

fig2 = plotly.subplots.make_subplots(rows=3, cols=1, shared_xaxes=True)
fig2.append_trace(
        go.Scatter(x=df['Date_Time'], y=df["N2O_rSig"]), row=1, col=1)
                   
fig2.append_trace(                   
        go.Scatter(x=df['Date_Time'], y=df["Flow_rSig"]), row=2, col=1)
        
fig2.append_trace(   
        go.Scatter(x=df['Date_Time'], y=df["O2_rSig"]), row=3, col=1)
   
fig2.update_layout(title_text="Stacked Subplots")

fig2.write_html("test_plotly.html")

For each trace I want to have a discrete color governed by Valve_ai but I just can't seem to find the right way. Is there a way without having to rebuild that way my data is sent to Plotly.graph_objects.

I noticed Plotly Express has the ability to split long data based on the variable "color". Cufflinks also manages this with categories. However I in order to manage Legends across multiple subplots Plotly.go seems like the only option.

Here is the example data

,Unnamed: 0,Date_Time,N,Valve_ai,N2O_rSig,NO_rSig,O2_rSig,CO2_rSig,Flow_rSig
48,57,2020-07-15 00:00:57,58,Bio1 G1,6.33,16.69,20.61,1.0,1.02
49,58,2020-07-15 00:00:58,59,Bio1 G1,6.13,16.62,20.61,1.0,0.96
50,59,2020-07-15 00:00:59,60,Bio1 G1,6.15,16.56,20.6,1.0,0.98
51,60,2020-07-15 00:01:00,61,Bio1 G1,6.12,16.55,20.59,1.0,0.86
52,61,2020-07-15 00:01:01,62,Bio1 G1,6.44,16.68,20.6,1.0,1.07
53,62,2020-07-15 00:01:02,63,Bio1 G1,6.69,16.63,20.59,1.0,0.94
54,64,2020-07-15 00:01:04,65,Bio1 G2,7.28,16.69,20.57,1.0,0.98
55,65,2020-07-15 00:01:05,66,Bio1 G2,7.98,17.06,20.49,1.0,1.05
56,66,2020-07-15 00:01:06,67,Bio1 G2,8.82,17.37,20.4,1.0,0.98
57,67,2020-07-15 00:01:07,68,Bio1 G2,10.03,17.78,20.26,1.0,0.9
58,68,2020-07-15 00:01:08,69,Bio1 G2,13.4,19.36,19.94,1.0,1.02
59,69,2020-07-15 00:01:09,70,Bio1 G2,15.55,20.68,19.77,1.0,0.85

Answer 1

So after a bit of looking around I found a direct comparison with bar graphs on the plotly website

The following code allowed me to split up my Data Frame based on Valve and then iterate through each DF to create individual traces.

for valve, group in df.groupby("Valve_ai"):
        fig.add_trace(go.Scatter(x=group["Date_Time"], y=group["N2O_rSig"], name=valve)