Build a 2025 Stock Dashboard in less than 40 lines of Python code!🤓

Building interactive data dashboards can seem intimidating.

Especially if you're unfamiliar with frontend technologies like HTML/CSS/ JS.

But what if you could create a fully functional, production-ready data science dashboard using just Python?

Enter Taipy, an open-source library that simplifies the process of creating data apps.

Star ⭐ Taipy repo
 

In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle.

Our app will dynamically filter data, display graphs, and handle user inputs—all from scratch.

Ready to dive in? Let’s get started!

Step 1: Setting Up Your Environment

First, we need to create a new Python environment. If you use Conda, you can set it up as follows:

conda create -n ds_env python=3.11 conda activate ds_env pip install taipy pandas plotly 

Clone the resources for this project:

git clone https://github.com/MariyaSha/data_science_dashboard.git cd data_science_dashboard/starter_files 

This will serve as our project root directory. Inside, you’ll find images, a wireframe, and a Python file (main.py) to start.

Step 2: Designing the GUI with Taipy

Let’s add a header and a logo to our app. Open main.py and start coding:

import taipy.gui as tgb with tgb.page("Stock Dashboard"): # Add a logo  tgb.image("images/icons/logo.png", width="10vw") # Add a title  tgb.text("# S&P 500 Stock Value Over Time", mode="md") 

Run your app:

taipy run main.py 

Navigate to http://localhost:5000, and you’ll see your basic app!

Step 3: Adding User Inputs

To filter data by date, add a date range selector:

import datetime dates = [datetime.date(2023, 1, 1), datetime.date(2024, 1, 1)] with tgb.page("Stock Dashboard"): # Existing elements...  # Add date range selector  tgb.date_range( value="{dates}", label_start="Start Date", label_end="End Date", ) 

Step 4: Dynamic Data Handling with Taipy

Let’s load our dataset and filter it dynamically based on user inputs.

import pandas as pd # Load the stock data stock_data = pd.read_csv("data/sp500_stocks.csv") def filter_data(state, name, value): if name == "dates": start, end = state.dates filtered_data = stock_data[ (stock_data["Date"] >= str(start)) & (stock_data["Date"] <= str(end)) ] state.filtered_data = filtered_data tgb.add_callback("filter_data", filter_data) 

Step 5: Visualizing the Data

Finally, let’s plot the data with Plotly:

import plotly.graph_objects as go def create_chart(data): fig = go.Figure() fig.add_trace( go.Scatter( x=data["Date"], y=data["High"], name="Stock Value", mode="lines" ) ) return fig with tgb.page("Stock Dashboard"): # Existing elements...  # Display the chart  tgb.chart(figure="{create_chart(filtered_data)}") 

Final Thoughts

And voilà!
You’ve built a stock dashboard with Taipy, handling dynamic user inputs and data visualization—all without writing a single line of HTML, CSS, or JavaScript.

Want to take it further?

Explore Taipy Scenarios to enable even more dynamic backend interactions. Check out the official Taipy GitHub repository and contribute to their open-source initiatives!

PS: you can watch the video tutorial here.