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How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

Published

1 week ago

June 27, 2025

The Editors

How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

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Introduction

Creating interactive web-based data dashboards in Python is easier than ever when you combine the strengths of Streamlit, Pandas, and Plotly. These three libraries work seamlessly together to transform static datasets into responsive, visually engaging applications — all without needing a background in web development.

However, there’s an important architectural difference to understand before we begin. Unlike libraries such as matplotlib or seaborn that work directly in Jupyter notebooks, Streamlit creates standalone web applications that must be run from the command line. You’ll write your code in a text-based IDE like VS Code, save it as a .py file, and run it using streamlit run filename.py. This shift from the notebook environment to script-based development opens up new possibilities for sharing and deploying your data applications.

In this hands-on tutorial, you’ll learn how to build a complete sales dashboard in two clear steps. We’ll start with core functionality using just Streamlit and Pandas, then enhance the dashboard with interactive visualizations using Plotly.

Setup

Install the required packages:

pip install streamlit pandas plotly

Create a new folder for your project and open it in VS Code (or your preferred text editor).

Step 1: Streamlit + Pandas Dashboard

Let’s start by building a functional dashboard using just Streamlit and Pandas. This demonstrates how Streamlit creates interactive web interfaces and how Pandas handles data filtering.

Create a file called step1_dashboard_basic.py:

import streamlit as st
import pandas as pd
import numpy as np

# Page config
st.set_page_config(page_title="Basic Sales Dashboard", layout="wide")

# Generate sample data
np.random.seed(42)
df = pd.DataFrame({
    'Date': pd.date_range('2024-01-01', periods=100),
    'Sales': np.random.randint(500, 2000, size=100),
    'Region': np.random.choice(['North', 'South', 'East', 'West'], size=100),
    'Product': np.random.choice(['Product A', 'Product B', 'Product C'], size=100)
})

# Sidebar filters
st.sidebar.title('Filters')
regions = st.sidebar.multiselect('Select Region', df['Region'].unique(), default=df['Region'].unique())
products = st.sidebar.multiselect('Select Product', df['Product'].unique(), default=df['Product'].unique())

# Filter data
filtered_df = df[(df['Region'].isin(regions)) & (df['Product'].isin(products))]

# Display metrics
col1, col2, col3 = st.columns(3)
col1.metric("Total Sales", f"${filtered_df['Sales'].sum():,}")
col2.metric("Average Sales", f"${filtered_df['Sales'].mean():.0f}")
col3.metric("Records", len(filtered_df))

# Display filtered data
st.subheader("Filtered Data")
st.dataframe(filtered_df)

Let’s break down the key Streamlit methods used here:

st.set_page_config() configures the browser tab title and layout
st.sidebar creates the left navigation panel for filters
st.multiselect() generates dropdown menus for user selections
st.columns() creates side-by-side layout sections
st.metric() displays large numbers with labels
st.dataframe() renders interactive data tables

These methods automatically handle user interactions and trigger app updates when selections change.

Run this from your terminal (or VS Code’s integrated terminal):

streamlit run step1_dashboard_basic.py

Your browser will open to http://localhost:8501 showing an interactive dashboard.

How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

Try changing the filters in the sidebar — watch how the metrics and data table update automatically! This demonstrates the reactive nature of Streamlit combined with Pandas’ data manipulation capabilities.

Step 2: Add Plotly for Interactive Visualizations

Now let’s enhance our dashboard by adding Plotly’s interactive charts. This shows how all three libraries work together seamlessly. Let’s create a new file and call it step2_dashboard_plotly.py:

import streamlit as st
import pandas as pd
import plotly.express as px
import numpy as np

# Page config
st.set_page_config(page_title="Sales Dashboard with Plotly", layout="wide")

# Generate data
np.random.seed(42)
df = pd.DataFrame({
    'Date': pd.date_range('2024-01-01', periods=100),
    'Sales': np.random.randint(500, 2000, size=100),
    'Region': np.random.choice(['North', 'South', 'East', 'West'], size=100),
    'Product': np.random.choice(['Product A', 'Product B', 'Product C'], size=100)
})

# Sidebar filters
st.sidebar.title('Filters')
regions = st.sidebar.multiselect('Select Region', df['Region'].unique(), default=df['Region'].unique())
products = st.sidebar.multiselect('Select Product', df['Product'].unique(), default=df['Product'].unique())

# Filter data
filtered_df = df[(df['Region'].isin(regions)) & (df['Product'].isin(products))]

# Metrics
col1, col2, col3 = st.columns(3)
col1.metric("Total Sales", f"${filtered_df['Sales'].sum():,}")
col2.metric("Average Sales", f"${filtered_df['Sales'].mean():.0f}")
col3.metric("Records", len(filtered_df))

# Charts
col1, col2 = st.columns(2)

with col1:
    fig_line = px.line(filtered_df, x='Date', y='Sales', color="Region", title="Sales Over Time")
    st.plotly_chart(fig_line, use_container_width=True)

with col2:
    region_sales = filtered_df.groupby('Region')['Sales'].sum().reset_index()
    fig_bar = px.bar(region_sales, x='Region', y='Sales', title="Total Sales by Region")
    st.plotly_chart(fig_bar, use_container_width=True)

# Data table
st.subheader("Filtered Data")
st.dataframe(filtered_df)

Run this from your terminal (or VS Code’s integrated terminal):

streamlit run step2_dashboard_plotly.py

Now you have a complete interactive dashboard!

How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

The Plotly charts are fully interactive — you can hover over data points, zoom in on specific time periods, and even click legend items to show/hide data series.

How the Three Libraries Work Together

This combination is powerful because each library handles what it does best:

Pandas manages all data operations:

Creating and loading datasets
Filtering data based on user selections
Aggregating data for visualizations
Handling data transformations

Streamlit provides the web interface:

Creates interactive widgets (multiselect, sliders, etc.)
Automatically reruns the entire app when users interact with widgets
Handles the reactive programming model
Manages layout with columns and containers

Plotly creates rich, interactive visualizations:

Charts that users can hover, zoom, and explore
Professional-looking graphs with minimal code
Automatic integration with Streamlit’s reactivity

Key Development Workflow

The development process follows a straightforward pattern. Start by writing your code in VS Code or any text editor, saving it as a .py file. Next, run the application from your terminal using streamlit run filename.py, which opens your dashboard in a browser at http://localhost:8501. As you edit and save your code, Streamlit automatically detects changes and offers to rerun the application. Once you’re satisfied with your dashboard, you can deploy it using Streamlit Community Cloud to share with others.

Next Steps

Try these enhancements:

Add real data:

# Replace sample data with CSV upload
uploaded_file = st.sidebar.file_uploader("Upload CSV", type="csv")
if uploaded_file:
    df = pd.read_csv(uploaded_file)

Keep in mind that real datasets will require preprocessing steps specific to your data structure. You’ll need to adjust column names, handle missing values, and modify the filter options to match your actual data fields. The sample code provides a template, but each dataset will have unique requirements for cleaning and preparation.

More chart types:

# Pie chart for product distribution
fig_pie = px.pie(filtered_df, values="Sales", names="Product", title="Sales by Product")
st.plotly_chart(fig_pie)

You can leverage an entire gallery of Plotly’s graphing capabilities.

Deploying Your Dashboard

Once your dashboard is working locally, sharing it with others is straightforward through Streamlit Community Cloud. First, push your code to a public GitHub repository, making sure to include a requirements.txt file listing your dependencies (streamlit, pandas, plotly). Then visit https://streamlit.io/cloud, sign in with your GitHub account, and select your repository. Streamlit will automatically build and deploy your app, providing a public URL that anyone can access. The free tier supports multiple apps and handles reasonable traffic loads, making it perfect for sharing dashboards with colleagues or showcasing your work in a portfolio.

Conclusion

The combination of Streamlit, Pandas, and Plotly transforms data analysis from static reports into interactive web applications. With just two Python files and a handful of methods, you’ve built a complete dashboard that rivals expensive business intelligence tools.

This tutorial demonstrates a significant shift in how data scientists can share their work. Instead of sending static charts or requiring colleagues to run Jupyter notebooks, you can now create web applications that anyone can use through a browser. The transition from notebook-based analysis to script-based applications opens new opportunities for data professionals to make their insights more accessible and impactful.

As you continue building with these tools, consider how interactive dashboards can replace traditional reporting in your organization. The same principles you’ve learned here scale to handle real datasets, complex calculations, and sophisticated visualizations. Whether you’re creating executive dashboards, exploratory data tools, or client-facing applications, this three-library combination provides a solid foundation for professional data applications.

Born in India and raised in Japan, Vinod brings a global perspective to data science and machine learning education. He bridges the gap between emerging AI technologies and practical implementation for working professionals. Vinod focuses on creating accessible learning pathways for complex topics like agentic AI, performance optimization, and AI engineering. He focuses on practical machine learning implementations and mentoring the next generation of data professionals through live sessions and personalized guidance.

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Canva Partners With NCERT to Launch AI-Powered Teacher Training

Published

35 minutes ago

July 7, 2025

Ankush Das

Canva has signed a memorandum of understanding (MoU) with the National Council of Educational Research and Training (NCERT) to launch free teacher training and certification programs hosted on the education ministry’s DIKSHA platform.

The initiative aims to enhance digital literacy, creativity, and AI proficiency among educators across India, in alignment with the objectives of the National Education Policy (NEP) 2020.

As part of the agreement, Canva will offer Indian teachers free access to its education platform and provide learning materials tailored for visual and collaborative instruction. NCERT will ensure that the course content aligns with the national curriculum and is made regionally accessible. Available in multiple Indian languages, the course will also be broadcast via PM e-Vidya DTH channels to extend its reach beyond internet-enabled classrooms.

The certification program includes training on using Canva’s design tools to create engaging lesson plans, infographics, and presentations. Teachers will also learn to co-create content with students and apply AI tools to improve classroom outcomes. Upon completion, participants will receive a joint certificate from NCERT and Canva.

“This partnership is a powerful step toward equipping educators with practical digital skills that not only save time but spark imagination in every classroom,” Jason Wilmot, head of education at Canva, said in a press statement.

Chandrika Deb, country manager for India at Canva stated, “By delivering this program free of cost, in multiple languages, and through a trusted national platform like NCERT, we are not only advancing digital fluency and creative confidence in classrooms across the country, but also deepening Canva’s long-term commitment to India, which plays a pivotal role in our vision to democratize design and creativity at scale.”

Moreover, the company shared some interesting figures. Canva has seen significant global momentum, with over 100 million students and teachers using its platform. In 2024, over 1 billion designs were created, many powered by Canva’s AI tools like Dream Lab, which enables teachers to generate custom visuals instantly. Teacher usage of AI tools has increased by 50% over the past year, with student engagement rising by 107%.

We may see further developments in this partnership as the training program for teachers progresses over time.

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Capgemini to Acquire WNS for $3.3 Billion with Focus on Agentic AI

Published

1 hour ago

July 7, 2025

Mohit Pandey

Capgemini has announced a definitive agreement to acquire WNS, a mid-sized Indian IT firm, for $3.3 billion in cash. This marks a significant step towards establishing a global leadership position in agentic AI.

The deal, unanimously approved by the boards of both companies, values WNS at $76.50 per share—a premium of 28% over the 90-day average and 17% above the July 3 closing price.

The acquisition is expected to immediately boost Capgemini’s revenue growth and operating margin, with normalised EPS accretion of 4% by 2026, increasing to 7% post-synergies in 2027.

“Enterprises are rapidly adopting generative AI and agentic AI to transform their operations end-to-end. Business process services (BPS) will be the showcase for agentic AI,” Aiman Ezzat, CEO of Capgemini, said.

“Capgemini’s acquisition of WNS will provide the group with the scale and vertical sector expertise to capture that rapidly emerging strategic opportunity created by the paradigm shift from traditional BPS to agentic AI-powered intelligent operations.”

Pending regulatory approvals, the transaction is expected to close by the end of 2025.

WNS’ integration is expected to strengthen Capgemini’s presence in the US market while unlocking immediate cross-selling opportunities through its combined offerings and clientele.

WNS, which reported $1.27 billion in revenue for FY25 with an 18.7% operating margin, has consistently delivered a revenue growth of around 9% over the past three fiscal years.

“As a recognised leader in the digital BPS space, we see the next wave of transformation being driven by intelligent, domain-centric operations that unlock strategic value for our clients,” Keshav R Murugesh, CEO of WNS, said. “Organisations that have already digitised are now seeking to reimagine their operating models by embedding AI at the core—shifting from automation to autonomy.”

The companies expect to drive additional revenue synergies between €100 million and €140 million, with cost synergies of up to €70 million annually by the end of 2027.

“WNS and Capgemini share a bold, future-focused vision for Intelligent Operations. I’m confident that Capgemini is the ideal partner at the right time in WNS’ journey,” Timothy L Main, chairman of WNS’ board of directors, said.

Capgemini, already a major player with over €900 million in GenAI bookings in 2024 and strategic partnerships with Microsoft, Google, AWS, Mistral AI, and NVIDIA, aims to solidify its position as a transformation partner for businesses looking to embed agentic AI at scale.

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Piyush Goyal Announces Second Tranche of INR 10,000 Cr Deep Tech Fund

Published

21 hours ago

July 6, 2025

Siddharth Jindal

IIT Madras and its alumni association (IITMAA) held the sixth edition of their global innovation and alumni summit, ‘Sangam 2025’, in Bengaluru on 4 and 5 July. The event brought together over 500 participants, including faculty, alumni, entrepreneurs, investors and students.

Union Commerce and Industry Minister Shri Piyush Goyal, addressing the summit, announced a second tranche of ₹10,000 crore under the government’s ‘Fund of Funds’, this time focused on supporting India’s deep tech ecosystem. “This money goes to promote innovation, absorption of newer technologies and development of contemporary fields,” he said.

The Minister added that guidelines for the fund are currently being finalised, to direct capital to strengthen the entire technology lifecycle — from early-stage research through to commercial deployment, not just startups..

He also referred to the recent Cabinet decision approving $12 billion (₹1 lakh crore) for the Department of Science and Technology in the form of a zero-interest 50-year loan. “It gives us more flexibility to provide equity support, grant support, low-cost support and roll that support forward as technologies get fine-tuned,” he said.

Goyal said the government’s push for indigenous innovation stems from cost advantages as well. “When we work on new technologies in India, our cost is nearly one-sixth, one-seventh of what it would cost in Switzerland or America,” he said.

The Minister underlined the government’s focus on emerging technologies such as artificial intelligence, machine learning, and data analytics. “Today, our policies are structured around a future-ready India… an India that is at the forefront of Artificial Intelligence, Machine Learning, computing and data analytics,” he said.

He also laid out a growth trajectory for the Indian economy. “From the 11th largest GDP in the world, we are today the fifth largest. By the end of Calendar year 2025, or maybe anytime during the year, we will be the fourth-largest GDP in the world. By 2027, we will be the third largest,” Goyal said.

Sangam 2025 featured a pitch fest that saw 20 deep tech and AI startups present to over 250 investors and venture capitalists. Selected startups will also receive institutional support from the IIT Madras Innovation Ecosystem, which has incubated over 500 ventures in the last decade.

Key speakers included Aparna Chennapragada (Chief Product Officer, Microsoft), Srinivas Narayanan (VP Engineering, OpenAI), and Tarun Mehta (Co-founder and CEO, Ather Energy), all IIT Madras alumni. The summit also hosted Kris Gopalakrishnan (Axilor Ventures, Infosys), Dr S. Somanath (former ISRO Chairman) and Bengaluru South MP Tejasvi Surya.

Prof. V. Kamakoti, Director, IIT Madras, said, “IIT Madras is committed to playing a pivotal role in shaping ‘Viksit Bharat 2047’. At the forefront of its agenda are innovation and entrepreneurship, which are key drivers for National progress.”

Ms. Shyamala Rajaram, President of IITMAA, said, “Sangam 2025 is a powerful confluence of IIT Madras and its global alumni — sparking bold conversations on innovation and entrepreneurship.”

Prof. Ashwin Mahalingam, Dean (Alumni and Corporate Relations), IIT Madras, added, “None of this would be possible without the unwavering support of our alumni community. Sangam 2025 embodies the strength of that network.”

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