Plotly Dash : Scatter Plot from Iris Dataset

 

Plotly Dash : Scatter Plot using  Iris Dataset 

Scatter Plots

Scatter plots are a valuable tool to visualize relationship between two numerical variables. For instance, they can be used to examine the relationship between weight and height. By analyzing the pattern of dots on the graph, one can identify the relationship as positive, negative, or nonexistent.

Scatter Plot using Plotly Dash

Install Required Libraries

First of all install required libraries using "pip".  Open your terminal or command prompt. Then type the following command and  press Enter:

pip install dash plotly scikit-learn

This will download and install Dash, Plotly, Scikit-learn along with dependencies. Once the installation is complete, you can import the Iris dataset from Scikit-learn in your Python code and use it to create your scatter plot.

Scikit-Learn

As you noted, I am using Scikit-Learn for Iris dataset. Scikit-learn is a powerful Python library designed for machine learning tasks. It provides a simple yet effective interface for building various machine learning models, ranging from simple linear regression to complex neural networks. Since Scikit-learn is frequently used in machine learning tasks, so I decided to use Iris dataset in this library. 

Pandas

Pandas is a versatile and powerful Python tool for data analysis, exploration and visualization. This library is specifically designed to handle and analyze structured data. It provides high-performance data structures and data analysis tools, making it an essential tool for data scientists, researchers, and analysts.

Pandas plays a crucial role in machine learning, including data cleaning and preparation, feature engineering, and data exploration.

Pandas seamlessly integrates with visualization libraries like Matplotlib, allowing for the creation of informative and visually appealing graphs and charts.

Pandas also offers flexibility in customizing visualizations to suit specific needs and preferences.

If you have not already installed pandas library, install it with the following command:

pip install pandas

Create the Dash App

import dash, sklearn

from dash import dcc

from dash import html

import plotly.express as px

import pandas as pd

from sklearn.datasets import load_iris

# Load the Iris dataset

iris = load_iris()

df = pd.DataFrame(data=iris.data, columns=iris.feature_names)

df['species'] = iris.target

# Map numeric labels to species names

species_names = {0: 'setosa', 1: 'versicolor', 2: 'virginica'}

df['species'] = df['species'].map(species_names)

# Initialize the Dash app

app = dash.Dash(__name__)

# Create a scatter plot

fig = px.scatter(df, x='sepal length (cm)', y='petal length (cm)', 

color='species', labels={'species': 'Species'}, title='Scatter Plot of Iris Dataset')

# Define the layout of the app

app.layout = html.Div([

    html.H1("Iris Dataset Scatter Plot"),

    dcc.Graph(figure=fig)

])

# Create the Dash app layout

app = dash.Dash(__name__)

app.layout = html.Div([

    dcc.Graph(figure=fig)

])    

# Run the app

if __name__ == '__main__':

    app.run_server(debug=True)

Run the App

Save the app

Run the app using command:

python app.py

See also: 

Plotly Dash Web App - Simple Charts

Plotly Dash: Framework created for Data Scientists / Analysts (Introduction to Dash)

Plotly Dash Learning Roadmap