change line color in middle of plot - plotly

Question

Creating 12 subplots with plotly from a csv containing rows with 13 values. The last value indicates that the data in that row is estimated from this point until the status changes back in a later row.
Trying to make line graphs that plots a line, changes to red when the status changes to 1, then back to original color when the status changes back to 0. Is this possible?

with lock:
    df = pd.read_csv(OCcsvFile, delimiter=',')

# plotly setup
plot_rows = 4
plot_cols = 3
# Create plot figure
fig = make_subplots(rows=plot_rows, cols=plot_cols, subplot_titles=("Header1", "Header2", "Header3", "Header4", "Header5",
                                                                    "Header6", "Header7", "Header8", "Header9", "Header10",
                                                                    "Header11", "Header12"))

# add traces
x = 1  # column counter
for i in range(1, plot_rows+1):
    for j in range(1, plot_cols+1):
        #print(str(i)+ ', ' + str(j))
        fig.add_trace(go.Scatter(x=df.iloc[:, 0], y=df.iloc[:, x],
                                 name=df.columns[x],
                                 mode='lines'),
                      row=i,
                      col=j)
        x = x+1

Answer 1

• taken approach of reshaping dataframe to be ready for plotly express
• have worked out starting dataframe from description, a sample would be better

import pandas as pd
import plotly.express as px

df = pd.DataFrame(
    np.random.randint(1, 10, [100, 12]), columns=[f"c{i+1}" for i in range(12)]
).assign(status=np.repeat(np.random.randint(0, 2, 20), 5))

# restructure dataframe for px
# 1. preserve status in index
# 2. make columns another level of index
# 3. make index columns and make column names meaningful
dfp = (
    df.set_index("status", append=True)
    .stack()
    .reset_index()
    .rename(columns={"level_0": "x", "level_2": "facet", 0: "value"})
)

# make sure missing values are present as NaN
dfp = dfp.merge(
    pd.DataFrame(index=pd.MultiIndex.from_product(
        [dfp["x"].unique(), dfp["facet"].unique(), dfp["status"].unique()]
    )),
    left_on=["x", "facet", "status"],
    right_index=True,
    how="right"
)

# now it's a very simple plot
px.line(dfp, x="x", y="value", color="status", facet_col="facet", facet_col_wrap=4)

expected structure or df

• 13 columns, last column indicating the status

	c1	c2	c3	c4	c5	c6	c7	c8	c9	c10	c11	c12	status
10	7	3	1	7	2	8	1	3	9	6	3	8	1
11	4	5	8	9	5	4	3	6	3	7	4	8	1
12	6	3	2	6	5	6	4	3	5	3	9	7	1
13	4	2	4	8	6	3	3	5	8	8	1	4	1
14	4	9	9	3	1	8	2	5	1	5	1	4	1
15	4	9	6	2	9	4	1	6	6	1	6	1	0
16	8	5	9	7	7	3	1	1	2	5	2	9	0
17	6	1	4	2	8	5	9	8	2	4	8	4	0
18	1	6	1	3	8	5	5	9	8	9	2	9	0
19	1	4	1	1	7	8	2	3	5	6	6	4	0