Line chart with same color for row groups but different line styles for columns

[Bakira]

I have the dataframe df as shown below. What do I have to change, that each “Run” has its own line (color) and the values from column “Val_B” are dashed and “Val_A” solid?

import pandas as pd
import plotly.express as px

df = pd.DataFrame(
{
“Run”: (
1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6
),
“Point”: (
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3,
),
“Val_A”: (
78, 79, 77, 78, 79, 77, 78, 79, 77, 78, 79, 77, 78, 79, 77, 78, 79, 77,
),
“Val_B”: (
76, 75, 74, 76, 75, 74, 76, 75, 74, 76, 75, 74, 76, 75, 74, 76, 75, 74,
),
}
)

Create the line plot with solid line for Val_A and dashed line for Val_B

fig = px.line(df, x=“Point”, y=[“Val_A”, “Val_B”], color=“Run”,
title=“Line Plot with Solid and Dashed Lines for Each Run”)

Show the plot

fig.show()

[r-beginners]

Extract the data by row category in a data frame and draw a line graph with the data. There are two types of line graph targets, so we specify two types. Specify a different line type for each. Specify the color to be the same color with a loop counter. Also, since the two types of legends are enumerated as they are, they are grouped together and used as a legend for each value. If you do not need the grouping, disable it.

import plotly.graph_objects as go
import plotly.express as px

colors = px.colors.qualitative.Plotly

fig = go.Figure()

for i,r in enumerate(df['Run'].unique()):
    dff = df.query('Run == @r')
    fig.add_trace(go.Scatter(
        x=dff['Point'],
        y=dff['Val_A'],
        mode='lines',
        name=str(r),
        legendgroup='Val_A',
        legendgrouptitle=dict(text='Val_A'),
        line=dict(color=colors[i])))
    fig.add_trace(go.Scatter(
        x=dff['Point'],
        y=dff['Val_B'],
        mode='lines',
        name=str(r),
        legendgroup='Val_B',
        legendgrouptitle=dict(text='Val_B'),
        line=dict(color=colors[i],dash='dash')
                 ))

fig.update_layout(height=500, xaxis_title='Point', yaxis_title='value')
fig.show()