Updated On : Apr-06,2023 Time Investment : ~20 mins

Create Simple Dashboard Faster using Streamlit (<50 Lines of Code)

Welcome to this tutorial on how to create a simple dashboard faster using Streamlit! If you're new to Streamlit, it is a popular Python library that allows you to build beautiful, interactive web applications with minimal effort.

Streamlit makes it easy to create custom data visualizations, interactive charts, and tables and allows you to share your work with others via a web browser. It provides a simple and intuitive way to build your web application without the need for any HTML, CSS, or JavaScript.

What Can You Learn From This Article?

In this tutorial, we'll take a deep dive into how to create a simple dashboard using Streamlit, step by step. We'll create a simple dashboard consisting of just 2 charts and a few widgets to change those charts as a part of this tutorial.

The below image shows the dashboard that we are going to create as a part of this tutorial. We'll be creating a scatter chart using the pandas_bokeh library.

Create Simple Dashboard Faster using Streamlit

This tutorial is a good starting point for someone who is new to "streamlit" and wants to get started with dashboard creation using it. By the end of this tutorial, you'll have a solid understanding of how to create a custom dashboard using Streamlit and will be well on your way to creating your own impressive web applications.

So, whether you're a data scientist looking to showcase your latest findings, a developer wanting to build a quick prototype, or anyone who wants to build an interactive dashboard with ease, this tutorial is for you. Let's get started!

Video Tutorial

Please feel free to check below video tutorial if feel comfortable learning through videos.

Streamlit Dashboard

Below, we have included code for our dashboard.

The code starts by setting the layout of the page to "wide" using the set_page_config() function.

Then, it loads the wine dataset using the load_wine() function from the 'sklearn.datasets' module and converts it into a Pandas DataFrame. The data is then cached using the "@st.cache_data" decorator, which allows for faster loading of data on subsequent runs of the app.

Next, the code creates a sidebar with widgets that allow the user to select the x and y axes for a scatter chart and whether to color-encode the points by wine type. It also includes a multi-select widget to select which ingredients to include in a bar chart of the average ingredient values for each wine type.

The main body of the dashboard is split into two columns using the columns() function from the streamlit.container module.

In the left column, a scatter chart is displayed using the plot_bokeh.scatter() function from the pandas_bokeh module, which generates an interactive scatter plot. The chart is based on the user's selection of the x and y axes, and the points can be color-encoded by wine type if selected.

The right column displays a bar chart of the average ingredient values for each wine type, based on the user's selection of which ingredients to include.

Finally, the code uses the st.bokeh_chart() and st.bar_chart() functions from the Streamlit library to display the charts in the left and right columns, respectively. The streamlit method st.bar_chart() creates chart using data viz library Altair. The use_container_width=True parameter ensures that the charts fill the available space in their respective columns.

import streamlit as st
import pandas as pd
import pandas_bokeh
from sklearn.datasets import load_wine

st.set_page_config(layout="wide") ## Set layout wide to cover whole page.

## Load Data
def load_data():
    wine = load_wine()
    wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
    wine_df["WineType"] = [wine.target_names[t] for t in wine.target]
    return wine_df

wine_df = load_data()
ingredients = wine_df.drop(columns=["WineType"]).columns

avg_wine_df = wine_df.groupby("WineType").mean().reset_index()

## Title
st.title("Wine Dataset :green[Analysis] :tea: :coffee: :chart: :bar_chart:")
st.markdown("Wine Analysis dashboard let us explore relationship between various **ingredients** used in creation of 3 different types of wine (*Class_0, Class_1, & Class_2*)")

## Add Widgets
st.sidebar.markdown("### Scatter Chart: Explore Relationship Between Ingredients :")

x_axis = st.sidebar.selectbox("X-Axis", ingredients, )
y_axis = st.sidebar.selectbox("Y-Axis", ingredients, index=1)
color_encode = st.sidebar.checkbox(label="Color-Encode by WineType")

st.sidebar.markdown("### Bar Chart: Average Ingredients Per Wine Type : ")

bar_multiselect = st.sidebar.multiselect(label="Bar Chart Ingredients", options=ingredients, default=["alcohol", "malic_acid", "ash"])

## Widgets State Change Actions & Layout Adjustments.
container = st.container()
chart1, chart2 = container.columns(2)

with chart1:
    if x_axis and y_axis:
        scatter_fig = wine_df.plot_bokeh.scatter(x=x_axis, y=y_axis, category="WineType" if color_encode else None,
                                                 xlabel=x_axis.capitalize(), ylabel=y_axis.capitalize(),
                                                 title="{} vs {}".format(x_axis.capitalize(), y_axis.capitalize()),
                                                 fontsize_title= 25, fontsize_label=12, show_figure=False)
        st.bokeh_chart(scatter_fig, use_container_width=True)

with chart2:
    if bar_multiselect:
        st.header("Avg Ingredients")
        st.bar_chart(avg_wine_df, x="WineType", y=bar_multiselect, height=500, use_container_width=True)

Create Simple Dashboard Faster using Streamlit

Sunny Solanki  Sunny Solanki

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