Creating Storytelling Dashboards with Plotly

Welcome back, aspiring data scientists! In today’s article, we’re diving into the world of storytelling dashboards with Plotly. Data visualization is a powerful tool, and when used effectively, it allows us to communicate insights and tell compelling stories with our data. Creating dashboards with Plotly can bring your data to life in an interactive and visually appealing way. Let’s explore how to create storytelling dashboards that help people understand the story behind your data.

What Is a Storytelling Dashboard?

A storytelling dashboard is a visual tool that goes beyond just displaying charts and graphs. It helps guide the viewer through a narrative, explaining what the data represents, highlighting key insights, and providing the context needed to understand the story behind the numbers. When done well, a storytelling dashboard turns complex data into a meaningful story that anyone can understand.

Why Plotly for Storytelling Dashboards?

Plotly is an open-source graphing library that is well known for its interactive visualizations. Unlike static charts, Plotly provides users with the ability to zoom in, filter, and interact with the data, which makes it an excellent choice for creating storytelling dashboards. Some of the key features of Plotly include:

• Ease of Use: Plotly is easy to learn and integrates well with Python.
• Interactivity: Users can interact with visualizations, which makes data exploration more engaging.
• Aesthetics: Plotly’s charts look professional and can be easily customized.

Components of a Storytelling Dashboard

When designing a storytelling dashboard, keep in mind the following key components:

1. Narrative Structure: Your dashboard should tell a story. Decide on the flow—is it chronological, comparison-based, or something else?
2. Key Visuals: Use charts that effectively communicate the main insights.
3. Interactivity: Add elements that allow the viewer to explore the data further, such as filters or dropdowns.
4. Annotations: Use text annotations to highlight key points and explain the insights clearly.

Getting Started with Plotly

Installation

To use Plotly in Python, you need to install it first. You can do this easily with the following command:

pip install plotly

Once installed, you can import it in your Python script:

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

Creating a Simple Dashboard

Let’s create a simple storytelling dashboard using Plotly. We’ll use Plotly Express to create some interactive charts, and then use Dash (another tool from Plotly) to bring everything together into a dashboard.

Example: Sales Dashboard

Suppose we have a dataset containing sales data from different regions. We’ll create a dashboard to tell a story about sales performance over time and across regions.

First, let’s start by importing some libraries and creating our sample data:

import pandas as pd
import plotly.express as px
from dash import Dash, dcc, html

# Sample data
data = {
    'Region': ['North', 'South', 'East', 'West'] * 5,
    'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May'] * 4,
    'Sales': [200, 300, 400, 500, 600, 250, 350, 450, 550, 650, 220, 330, 440, 560, 610, 280, 370, 460, 570, 680]
}

# Create DataFrame
df = pd.DataFrame(data)

Now, let’s create a line chart that shows the sales trend for different regions over time:

# Create a line chart to visualize sales trends
fig = px.line(df, x='Month', y='Sales', color='Region', title='Monthly Sales by Region')

# Show the figure
fig.show()

In this chart, each line represents a region, and we can easily see how sales are performing across different regions each month.

Adding Interactivity with Dash

To make our visualization more interactive, we can use Dash, a framework that allows you to create dashboards using Python. Here’s how we can use Dash to combine multiple Plotly charts into a single interactive dashboard:

from dash import Input, Output
import dash_core_components as dcc
import dash_html_components as html

# Initialize the app
app = Dash(__name__)

# Layout of the dashboard
app.layout = html.Div([
    html.H1('Sales Performance Dashboard'),
    dcc.Graph(id='sales-trend', figure=fig),
    html.Label('Select Region:'),
    dcc.Dropdown(
        id='region-dropdown',
        options=[{'label': region, 'value': region} for region in df['Region'].unique()],
        value='North'
    ),
    dcc.Graph(id='filtered-sales')
])

# Callback to update the chart based on dropdown selection
@app.callback(
    Output('filtered-sales', 'figure'),
    Input('region-dropdown', 'value')
)
def update_graph(selected_region):
    filtered_df = df[df['Region'] == selected_region]
    fig = px.bar(filtered_df, x='Month', y='Sales', title=f'Sales in {selected_region} Region')
    return fig

# Run the app
if __name__ == '__main__':
    app.run_server(debug=True)

Explanation

1. Dash App Initialization: We initialize a Dash app and define the layout.
2. Dropdown and Callback: We add a dropdown to allow the user to select a region and update the bar chart accordingly. This makes the dashboard more interactive, allowing users to dive deeper into the sales performance of specific regions.
3. Multiple Charts: We use both a line chart and a bar chart to tell different parts of the sales story—the overall trend and the regional breakdown.

Best Practices for Creating Storytelling Dashboards

Here are some tips to help you create effective storytelling dashboards:

• Keep It Simple: Avoid clutter. Only include visualizations that are essential to the story you are trying to tell.
• Highlight Key Insights: Use annotations or highlights to draw attention to important data points.
• Use Consistent Colors: Keep color schemes consistent across visualizations to make the dashboard look cohesive.
• Provide Context: Always include labels, legends, and titles so that the viewer knows what they are looking at.

Real-Life Application: Retail Dashboard

Imagine you work as a data analyst for a retail company, and your goal is to create a dashboard that provides insights into product sales, customer behavior, and profitability. With Plotly and Dash, you could create:

• Sales Over Time: A line chart showing sales trends by month or quarter.
• Top Products: A bar chart showing which products are the most popular.
• Customer Segmentation: Pie charts showing the breakdown of customer segments by region or spending habits.

Using these visualizations together, you can tell a complete story about how sales are performing, which products are driving revenue, and who your main customers are.

Mini Project: Create Your Own Storytelling Dashboard

Create a dashboard that tells the story of a company’s sales and profit data.

1. Use a dataset with at least three variables (e.g., Sales, Profit, and Region).
2. Create at least two interactive charts (e.g., line chart for sales trends, bar chart for profit by region).
3. Add dropdowns or filters to allow users to interact with the data.

Quiz Time!

1. Which library can you use to create interactive visualizations in Python?

• a) Matplotlib
• b) Seaborn
• c) Plotly

1. What is one of the main benefits of using storytelling dashboards?

• a) They are easier to build
• b) They provide context and narrative for better understanding
• c) They are always static

Answers: 1-c, 2-b

Key Takeaways

• Storytelling Dashboards help convey a narrative, making data insights more accessible.
• Plotly is a powerful tool for creating interactive and visually appealing visualizations.
• Dash helps combine multiple visualizations into an interactive dashboard, providing a holistic view of the data.

Next Steps

Take some time to create your own storytelling dashboards using Plotly and Dash. In the next article, we will dive into Best Practices for Visualizing Data Effectively, where you’ll learn how to make sure your charts and graphs are clear, informative, and easy to understand. Stay tuned!