How to plot multiple lines on the same Y-axis in Python Plotly?

Plotly is an open-source plotting library in Python that creates interactive web-based visualizations. In this tutorial, we will show how to plot multiple lines on the same Y-axis using plotly.express and pandas.

When working with time-series data or comparing multiple metrics, plotting multiple lines on the same chart helps visualize trends and relationships between different datasets.

Method 1: Using add_scatter()

First, create a line plot and then add another line using add_scatter() method ?

import plotly.express as px
import pandas as pd

# Create dataset
data = {
    'year': [2015, 2016, 2017, 2018, 2019],
    'lifeexp': [75, 74, 72, 70, 69],
    'state': ['kerala', 'punjab', 'karnataka', 'andhra', 'odisha'],
    'ratio': [74, 73.9, 71.5, 69.8, 69]
}

df = pd.DataFrame(data)

# Create line plot
fig = px.line(df, x='year', y='lifeexp', title='Multiple Lines on Same Y-axis')

# Add second line using scatter with line mode
fig.add_scatter(x=df['year'], y=df['ratio'], mode='lines', name='Ratio')

# Show the plot
fig.show()

Method 2: Using Multiple Columns with Melt

Transform the DataFrame to have multiple lines in a single plot using pd.melt() ?

import plotly.express as px
import pandas as pd

# Create dataset
data = {
    'year': [2015, 2016, 2017, 2018, 2019],
    'lifeexp': [75, 74, 72, 70, 69],
    'ratio': [74, 73.9, 71.5, 69.8, 69]
}

df = pd.DataFrame(data)

# Melt the dataframe to create multiple lines
df_melted = pd.melt(df, id_vars=['year'], value_vars=['lifeexp', 'ratio'], 
                    var_name='metric', value_name='value')

# Create line plot with color parameter
fig = px.line(df_melted, x='year', y='value', color='metric', 
              title='Multiple Lines Using Melt')

fig.show()

Method 3: Using Graph Objects

For more control over individual lines, use plotly.graph_objects ?

import plotly.graph_objects as go
import pandas as pd

# Create dataset
data = {
    'year': [2015, 2016, 2017, 2018, 2019],
    'lifeexp': [75, 74, 72, 70, 69],
    'ratio': [74, 73.9, 71.5, 69.8, 69]
}

df = pd.DataFrame(data)

# Create figure
fig = go.Figure()

# Add first line
fig.add_trace(go.Scatter(x=df['year'], y=df['lifeexp'], 
                        mode='lines', name='Life Expectancy'))

# Add second line
fig.add_trace(go.Scatter(x=df['year'], y=df['ratio'], 
                        mode='lines', name='Ratio'))

# Update layout
fig.update_layout(title='Multiple Lines with Graph Objects',
                  xaxis_title='Year',
                  yaxis_title='Values')

fig.show()

Comparison

Key Points

Use name parameter to add legends for multiple lines

The color parameter in px.line() automatically creates multiple lines

mode='lines' ensures only lines are drawn without markers

All methods share the same Y-axis scale automatically

Conclusion

Use add_scatter() for simple multi-line plots, pd.melt() with px.line() for clean data structure, or graph_objects for full customization control. All methods effectively plot multiple lines on the same Y-axis.