Building Powerful Explainer Dashboards in Python: A Step-by-Step Guide
In today‘s data-driven world, being able to effectively communicate insights from data is a critical skill. Dashboards are an excellent way to visually present key findings in an interactive format that enables exploration and facilitates data-driven decision making. With the rise of powerful open source libraries, Python has become a leading platform for building sophisticated dashboards.
In this guide, we‘ll walk through the process of creating an explainer dashboard in Python that allows users to interactively explore a dataset, view key statistics and visualizations, and even analyze the outputs of machine learning models. Whether you‘re a data scientist, analyst, or business user, the tips and examples covered will help you build insightful dashboards to unlock the full value of your data.
What Are Explainer Dashboards?
An explainer dashboard is an interactive interface that enables users to explore and understand a dataset or machine learning model. Beyond simply displaying visualizations, explainer dashboards allow users to dig into the data, ask questions, and test scenarios.
Some key features of explainer dashboards include:
- Interactive filters to slice and dice data
- Visualizations of high-level statistics and distributions
- Drilldowns into individual records or cohorts
- What-if tools to adjust inputs and see predicted outcomes
- Interpretability metrics to explain model outputs
The goal is to make data accessible and actionable for a broad audience, not just technical experts. Explainer dashboards act as a window into the data, enabling more people to leverage data to inform decisions.
Why Build Dashboards in Python?
While BI tools like Tableau and PowerBI are popular for building dashboards, Python offers some compelling advantages:
- Free and open source with extensive library ecosystem
- Flexibility to create fully custom dashboards
- Ability to pull in data from any source
- Integration with data science and machine learning workflows
- Potential to productionize dashboards as standalone web apps
For data scientists and analysts already working in Python, building dashboards in the same environment allows seamless integration. Dashboard code can be checked into version control alongside analysis and modeling notebooks.
Python‘s rich ecosystem of charting and dashboarding libraries make it easy to quickly build powerful interfaces with minimal coding. Let‘s take a look at some of the leading options.
Python Libraries for Building Dashboards
There are a number of excellent open source Python libraries for building interactive web apps and dashboards. Some of the most popular include:
Dash
Released by Plotly, Dash is one of the most full-featured Python frameworks for building analytical web applications. Dash provides a declarative approach to defining UI elements and uses reactive programming to tie the UI to Python callbacks that update the data. Dash ships with interactive graph and table components and can be extended with third-party plugins.
Panel
Panel is a framework for creating web-based dashboards and apps in Python. It allows you to create custom user interfaces by connecting UI elements like sliders, drop-downs, and text boxes to Python functions and can display output ranging from static text and tables to interactive Bokeh and Plotly charts. Panel works well in Jupyter notebooks for quick prototyping, but can also be used to develop standalone web apps.
Streamlit
Designed to turn Python scripts into shareable web apps, Streamlit has gained popularity for its simplicity. With Streamlit, you don‘t have to worry about web development; you just focus on writing the Python code and Streamlit turns it into an interactive app. This makes it very easy to get a basic dashboard up and running quickly. Streamlit supports a variety of charting libraries and can pull in data from multiple sources.
While there are tradeoffs between the different options, they all allow building interactive dashboards in pure Python without needing to write HTML/CSS/Javascript. In the next section, we‘ll walk through an example of building a dashboard using Dash.
Step-by-Step Example: Building a Dashboard with Dash
To illustrate the process of building an explainer dashboard in Python, we‘ll walk through a basic example using a public dataset of airline flight delays and the Dash library. Our goal will be to create a dashboard that allows users to explore patterns and causes of delayed flights.
Step 1: Prepare your data
The first step is to load and prepare your data. In our case, we‘ll use a dataset of flight delays from Kaggle. We can load this data into a Pandas dataframe:
import pandas as pd
df = pd.read_csv(‘flights.csv‘)
df.head()Before building our dashboard, we may want to do some light preprocessing and add any useful derived fields. For example, let‘s map the ‘DEP_DELAY‘ field to a boolean indicating if the flight was delayed:
df[‘DELAYED‘] = (df.DEP_DELAY > 0).astype(int)Step 2: Set up your Dash app
Next we initialize our Dash app and create a basic layout with placeholders for the content we‘ll add:
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
external_stylesheets = [‘https://codepen.io/chriddyp/pen/bWLwgP.css‘]
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)
app.layout = html.Div(children=[
html.H1(children=‘Flight Delay Dashboard‘),
html.Div([
dcc.Dropdown(
id=‘airline‘,
options=[{‘label‘: i, ‘value‘: i} for i in df.AIRLINE.unique()],
value=‘UA‘
)
],
style={‘width‘: ‘49%‘, ‘display‘: ‘inline-block‘}),
dcc.Graph(id=‘delay-graph‘),
])This sets up a basic Dash app with an H1 heading, dropdown to select an airline, and placeholder for a graph. The external_stylesheets argument allows us to import CSS to style the layout.
Step 3: Define reactive callbacks
Now we will define Python functions that will retrieve the data needed for our visualizations and create the graphs. We connect these to our components using reactive @app callbacks. Here we filter the data based on the selected airline and create a bar chart counting delayed vs on-time flights:
from dash.dependencies import Input, Output
@app.callback(
Output(‘delay-graph‘, ‘figure‘),
Input(‘airline‘, ‘value‘))
def update_graph(airline):
df_airline = df[df.AIRLINE == airline]
delayed = df_airline[df_airline.DELAYED == 1].shape[0]
on_time = df_airline[df_airline.DELAYED == 0].shape[0]
return {
‘data‘: [
{‘x‘: [‘On Time‘, ‘Delayed‘], ‘y‘: [on_time, delayed], ‘type‘: ‘bar‘, ‘name‘: airline},
],
‘layout‘: {
‘title‘: f‘Flights Delayed vs On Time - {airline}‘,
‘yaxis‘: {‘title‘: ‘Count‘},
‘xaxis‘: {‘title‘: ‘Flight Status‘},
}
}Whenever a user selects a new airline from the dropdown, this function will be called to filter the data and regenerate the bar chart. In a real dashboard we would have many more of these callbacks to enable the interactive elements.
Step 4: Launch the Dash server
Finally, we add this line to the end of our script to launch the web server:
if __name__ == ‘__main__‘:
app.run_server(debug=True)Now we can run the Python file and navigate to the URL where the app is being served (e.g. http://localhost:8050/). We should see our interactive dashboard!
As you can see, with just a small amount of Python code we were able to create an interactive dashboard to explore flight delay patterns. This merely scratches the surface of what‘s possible with Dash and other Python dashboarding libraries.
Dashboard Design Tips
Building an effective dashboard requires more than just technical skills – it also involves visual design and communication. Here are a few tips to keep in mind:
- Keep it simple and focused. Don‘t try to cram too much into one dashboard.
- Use clear and concise labels and legends.
- Choose appropriate visualizations for the type of data.
- Provide interactivity to enable exploration.
- Use color purposefully to highlight key insights.
- Consider accessibility for diverse users.
Like any skill, dashboard design takes practice. Studying examples of well-designed dashboards and getting feedback from users is a great way to improve. With Python‘s powerful libraries, you‘re limited only by your imagination!
Integrating Machine Learning Model Explainability
For data science teams building machine learning models, explainer dashboards can be a great way to provide transparency around how models are making predictions. There are a number of model-agnostic Python libraries for generating feature importance scores, partial dependence plots, individual conditional expectation (ICE) plots, etc.
Integrating these into a dashboard alongside business metrics can help stakeholders understand model behavior. Some examples of explainability libraries that could be incorporated into dashboards include:
- SHAP (SHapley Additive exPlanations)
- LIME (Local Interpretable Model-agnostic Explanations)
- Skater
- ELI5
Most of these work by analyzing model inputs and outputs to identify key drivers of predictions. They can help answer questions like:
- Which features have the biggest impact on model output overall (global importance)?
- For an individual example, which features were most important to the output (local importance)?
- How does the model output change as a single feature varies (partial dependence)?
Incorporating explainability elements into dashboards focused on machine learning deployments can build trust with business stakeholders and help catch potential issues early. They‘re an important aspect of responsible AI practices.
Additional Resources
To learn more about building dashboards in Python, check out these resources:
- Dash User Guide: https://dash.plotly.com/
- Panel Reference Gallery: https://panel.holoviz.org/reference/index.html
- Streamlit Docs: https://docs.streamlit.io/
- Bokeh Visualization Library: https://docs.bokeh.org/
- Plotly Python Open Source Graphing Library: https://plotly.com/python/
There are also many good tutorials available for each library walking through example dashboards.
Conclusion
We‘ve covered a lot of ground in this guide to building explainer dashboards in Python. The main takeaways are:
- Dashboards are a powerful way to communicate data and enable exploration.
- Python provides a robust ecosystem for building custom dashboards integrated with data science workflows.
- Libraries like Dash, Panel, and Streamlit make it easy to create interactive web apps with Python.
- Explainer dashboards can democratize access to data and provide transparency around machine learning models.
- Effective dashboard design requires a user-centric approach and iteration.
The skills involved in building dashboards – data manipulation, visualization design, web development – are highly transferable and are sure to serve you well in your data career. Don‘t be afraid to experiment and let your creativity run wild!
Happy dashboarding!
