Startup Trends Analysis with Preswald

We're thrilled to feature Rahul Nahar in this edition of the Preswald Community Showcase! 🎉 Rahul has built an impressive Preswald app that analyzes and visualizes startup trends using interactive charts and tables.

🌟 Project Overview

📊 Preswald and Analytics

This blog explores how Preswald utilizes analytics to track startup trends, providing insights into funding patterns, industry growth, and investment distributions through dynamic visualizations.

Project Name: Startup Trends Explorer
Built by: Rahul Nahar
Dataset: Startup trends dataset

Check out the project on GitHub

📊 Description

Rahul's project provides an interactive dashboard for exploring startup trends. The app allows users to:

• Filter startups by industry, funding stage, and investment size.
• Visualize trends using Plotly charts to analyze funding distributions and industry growth.
• Compare startup funding rounds dynamically in an interactive table.
• Gain insights into the factors influencing startup success and investor interest.

With Preswald, Rahul has created a seamless user experience, where users can dynamically adjust inputs and get instant updates without writing frontend code.

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🚀 Code Snippets

Here’s a sneak peek at how Rahul's project is structured:

Loading the Data

from preswald import connect, get_df, text

connect()  # Establish connection to data

# Load startup dataset
startup_df = get_df("startup_trends")

Creating an Interactive Funding Trend Chart

from preswald import plotly
import plotly.express as px

fig = px.bar(startup_df, x='year', y='funding_amount', color='industry',
             title='Startup Funding Trends Over Time')
plotly(fig)

Displaying Startup Data in a Table

from preswald import table

table(startup_df[['startup_name', 'industry', 'funding_stage', 'funding_amount']])

With just a few lines of Python, Rahul has built a fully interactive data app powered by Preswald! 🚀

What is Preswald?

Preswald is an open-source framework for building data apps, dashboards, and internal tools with just Python. It provides pre-built UI components like tables, charts, and forms, so you don't have to write frontend code. Users can interact with your app, changing inputs, running queries, and updating visualizations, without you needing to manage the UI manually.

Preswald tracks state and dependencies, making computations efficient by updating only when necessary. It uses a workflow DAG to manage execution order, ensuring performance and predictability. Preswald allows you to turn Python scripts into shareable, production-ready applications easily.

Key Features

• Add UI components to Python scripts: user-interactive buttons, text inputs, tables, and charts.
• Stateful execution with automatic state tracking and updates.
• Structured computation using a DAG-based execution model.
• Deploy with a single command.
• Query and display live data from various sources.
• Build interactive reports and dashboards.
• Easy local or cloud hosting.
• Shareable via a simple link.

🚀 Getting Started

Installation

First, install Preswald via pip: https://pypi.org/project/preswald/

pip install preswald

👩‍💻 Quick Start

1. Initialize a New Project

To start using Preswald, initialize a new project:

preswald init my_project
cd my_project

This creates a folder my_project with essential files:

• hello.py: Your first Preswald app.
• preswald.toml: Settings for your app.
• secrets.toml: Secure sensitive information.
• .gitignore: Keep secrets.toml safe from version control.

2. Write Your First App

Open hello.py and write:

from preswald import text, plotly, connect, get_df, table
import pandas as pd
import plotly.express as px

text("# Welcome to Preswald!")
text("This is your first app. 🎉")

# Load CSV data
connect() # loads default sample CSV
df = get_df('sample_csv')

# Create a scatter plot
fig = px.scatter(df, x='quantity', y='value', text='item',
                 title='Quantity vs. Value')
fig.update_traces(textposition='top center')
fig.update_layout(template='plotly_white')

# Show plot and data
table(df)

3. Run Your App

Launch the app with:

preswald run

4. Deploy Your App to the Cloud

Deploy your app using:

preswald deploy --target structured

Your app will be built and accessible online.

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Huge Thanks to Rahul Nahar!

A big thank you to Rahul Nahar for sharing their work and inspiring the Preswald community! Want to see more of their work? Check them out on GitHub | LinkedIn.

Want to Contribute?

Got a cool idea for a Preswald app? We'd love to see it! Get started here: https://github.com/StructuredLabs/preswald and get featured in our next Community Showcase.

Happy building! 🚀