Day 35 of 50 Days of Python: Streamlit for Building Simple Data Apps

Part of Week 5: Data Analysis and Visualisation

Welcome to Day 35 and the final stop of this week’s journey!

Yesterday, we explored how to communicate insights using Jupyter Notebooks. Today, we’re taking it a step further by transforming our static notebooks into interactive web applications using Streamlit.

Streamlit is a Python-based framework that lets you build data dashboards and interactive tools with minimal code. No need to be a front-end engineer. If you know Python and Pandas, you’re already most of the way there in all fairness.

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Getting Started with Streamlit

Installing Streamlit is easy within your Python environment:

pip install streamlit

Running Your First “App”

Create a Python file, for example app.py and then run the following code:

streamlit run app.py

What this is launch your app in a brower window automatically, none of that madness with trying to figure out how to launch flask apps.

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Streamlit Basics

Streamlit works by chaining Python scripts with UI widgets, think sliders, dropdowns, text inputs and every time you change something, the app reruns from top to bottom (Full refresh).

Core Functionality

import streamlit as st

st.title("My First App")
st.write("Hello, Streamlit!")

Adding Interactivity

name = st.text_input("Enter your name")
st.write(f"Hello, {name}!")

Visualise Data

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    'x': [1, 2, 3],
    'y': [10, 20, 30]
})

fig, ax = plt.subplots()
ax.plot(df['x'], df['y'])
st.pyplot(fig)

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Real-World Example: Interactive Sales Dashboard

A simple, “sales” dashboard.

Sample Dataset

import pandas as pd

data = {
    'Region': ['North', 'South', 'East', 'West'],
    'Sales': [30500, 35600, 28300, 33900]
}
df = pd.DataFrame(data)

App Code

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

st.title("

Regional Sales Dashboard")

# Data
data = {
    'Region': ['North', 'South', 'East', 'West'],
    'Sales': [30500, 35600, 28300, 33900]
}
df = pd.DataFrame(data)

# Selectbox
selected_region = st.selectbox("Choose a region:", df['Region'])

# Filtered View
filtered_sales = df[df['Region'] == selected_region]
st.write(f"Sales in {selected_region}: ${int(filtered_sales['Sales'])}")

# Visualisation
fig, ax = plt.subplots()
ax.bar(df['Region'], df['Sales'], color='skyblue')
ax.set_title("Sales by Region")
st.pyplot(fig)

Now you’ve built a mini-dashboard where users can filter and view data dynamically, all with less than 30 lines of Python! Granted it’s not “real” data but they’re real metrics that BI and Insight analysts report on.

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Next Up: Week 6 - Advanced Topics

We begin with Day 36: Introduction to Machine Learning Concepts, where we’ll demystify how machines learn from data, explore key concepts like features, labels, overfitting, and model evaluation, and get our hands clean with some real-world ML examples. As well as, plug some pieces I’ve already covered over at: Zero to Neural: Exploring AI | Jonathon Kindred

So see you for the next one and… Happy coding.