InterPareto: Pandas DataFrames to Interactive Pareto Analysis

interpareto is a Python utility for creating interactive Pareto charts from pandas.DataFrame objects. It generates standalone HTML files with dynamic visualizations using Plotly.js—viewable in any browser without Jupyter notebooks, servers, or frameworks.

The result is an interactive chart, where the data column used in the plot can be switched.

Example that uses well known movie dataset.

Movies dataset

📌 Note

InterPareto doesn’t use Plotly for Python or any server to run. Instead, it generates standalone HTML files with embedded interactive charts using Plotly.js (JavaScript). No server, Jupyter, or browser rendering engine is required at runtime.

Live example

This design allows charts to be:

Viewed offline in any modern web browser,
Embedded into web apps if needed.

Installation

pip install interpareto

Quick start

import pandas as pd
import interpareto as ipar
sample_df = ipar.generate_pareto_data(100)
ipar.render(sample_df, title="Sample Data Analysis")

Of course, not all datasets are suitable for this kind of analysis — but many are.

Ideal for analyzing:

Positive numeric values (sales, defects, costs)
Skewed distributions (few large, many small values)
Categorical data with measurable impact
Data w- here the 80/20 principle may apply

Avoid using with:

Normally distributed data
Data with many zero/negative values
Data without clear categories

Features

Converts pandas.DataFrame to interactive standalone HTML Pareto charts
Dynamic column selection: Switch between different data columns in real-time
Smart data processing: Automatically detects and removes index-like columns
Data cleaning: Handles NaN values, negatives, and zeros with detailed reporting
Self-contained HTML files with embedded data—no external dependencies at runtime
Works independently of web servers—viewable offline in any browser
Minimal HTML snippet generation: Generate embeddable HTML content for Flask or other web frameworks

Basic usage

import pandas as pd

import interpareto as ipar

# Simple example with 15 data points
df = pd.DataFrame({
    "Product": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O"],
    "Sales": [5000, 3200, 2800, 2100, 1900, 1500, 1200, 900, 750, 650, 500, 400, 300, 200, 150],
    "Profit": [1200, 800, 333, 222, 40, 111, 100, 90, 12, 60, 60, 55, 45, 12, 2]
})

# Generate chart
ipar.render(df, title="Sales Analysis", to_file="sales_pareto.html")

Pareto analysis is based on the Pareto Principle (80/20 rule), which states that roughly 80% of effects come from 20% of causes. InterPareto automatically:

Sorts data: Orders values from highest to lowest
Calculates cumulative percentages: Shows running totals as percentage of whole
Identifies the 80% threshold: Highlights where 80% of total value is reached
Annotates key insights: Shows what percentage of categories contribute to 80% of value

Main Functions

render

ipar.render(
    df: pd.DataFrame,
    to_file: Optional[str] = None,
    title: str = "Pareto dashboard",
    templ_path: str = TEMPLATE_PATH,
    startfile: bool = True,
    warnings: bool = True
) -> Union[str, file_object]

Parameters:

df: Input pandas DataFrame with numeric columns for analysis
to_file: Output HTML file path. If None, returns HTML string instead of writing file
title: Title for the Pareto dashboard
templ_path: Path to custom HTML template (uses default if not specified)
startfile: If True, automatically opens the generated HTML file in default browser
warnings: If True, displays data processing warnings in the output

Returns:

HTML string if to_file=None
File object if to_file is specified

render_inline

ipar.render_inline(
    df: pd.DataFrame,
    **kwargs
) -> str

Generates minimal HTML content suitable for embedding in Flask or other web framework templates. This function:

Returns only the chart markup and JavaScript
Excludes full HTML document structure (no <html>, <head>, <body> tags)
Important: Requires Plotly.js to be loaded in the host page
Perfect for embedding interactive Pareto analysis in existing web applications

Parameters:

Same as render() except to_file is not allowed (always returns string)

generate_pareto_data

ipar.generate_pareto_data(N: int = 15) -> pd.DataFrame

Generates sample data following various statistical distributions for testing and demonstration purposes.

Parameters:

N: Number of data points to generate

Returns:

DataFrame with sample data including Pareto, normal, uniform, and other distributions

Automatic Data Cleaning

InterPareto automatically processes your data to ensure optimal visualization:

Index-like column detection: Removes columns that appear to be indices or sequential numbers
Duplicate index handling: Removes duplicate row indices, keeping the first occurrence
Numeric column selection: Automatically selects the first 10 numeric columns
Missing value handling: Removes rows with NaN values
Negative and zero filtering: Excludes rows with negative values or zeros (configurable)

Processing Warnings

The library provides detailed feedback about data processing:

# Example processing output
"""
Dropped column 'ID': looks like index
Column 'Revenue': 2 NaNs, (1 negative values, 3 zeros)
Column 'Customers': 0 NaNs, (0 negative values, 1 zeros)
"""

Web Framework Integration

Complete Flask Example

Here's a complete Flask application demonstrating how to embed interactive Pareto charts:

from flask import Flask, render_template_string
import interpareto as ipar

app = Flask(__name__)

@app.route("/")
def home():
    # Generate sample data (or use your own DataFrame)
    df = ipar.generate_pareto_data(50)
    
    df_title = "Pareto Chart Rendered inline in <strong>Flask</strong>"
    
    # Generate the embeddable Pareto chart HTML
    string_pareto = ipar.render_inline(df, title=df_title, warnings=False)
    
    return render_template_string(
        """
        <!DOCTYPE html>
        <html>
        <head>
            <title>Flask Pareto Dashboard</title>
            <meta name="viewport" content="width=device-width, initial-scale=1.0">
            
            <!-- Required: Plotly.js for chart rendering -->
            <script src="https://cdnjs.cloudflare.com/ajax/libs/plotly.js/2.26.0/plotly.min.js"></script>
            
            <style>
                body {
                    font-family: "Segoe UI", Roboto, sans-serif;
                    margin: 0;
                    padding: 20px;
                    background-color: #f8f9fa;
                }
                .container {
                    max-width: 1200px;
                    margin: 0 auto;
                    background: white;
                    padding: 20px;
                    border-radius: 8px;
                    box-shadow: 0 2px 10px rgba(0,0,0,0.1);
                }
                h1 {
                    color: #333;
                    text-align: center;
                    margin-bottom: 30px;
                }
            </style>
        </head>
        <body>
            <div class="container">
                <h1>My Flask Pareto Dashboard</h1>
                {{ inline_pareto | safe }}
            </div>
        </body>
        </html>
        """,
        inline_pareto=string_pareto,
    )

if __name__ == "__main__":
    app.run(debug=True)

Understanding Pareto Analysis

Chart Components

The generated Pareto chart includes:

Horizontal bar chart: Shows individual values for each category
Cumulative line: Displays running percentage total
80% threshold line: Horizontal dashed line at 80%
Intersection annotation: Shows where cumulative line crosses 80%
Dynamic controls: Dropdown to switch between different data columns

Customization Examples

📌 Note Currently, modifying chart colors or settings is possible by editing the built-in HTML/JS template or by using a custom template.

Note that the goal of this module is not to replicate functionality of Plotly for Python when it comes to customization.

# Use custom template
ipar.render(
    df,
    title="Branded Analysis",
    templ_path="custom_pareto_template.html",
    to_file="branded_pareto.html"
)

# Generate sample data for testing
sample_df = ipar.generate_pareto_data(100)
ipar.render(sample_df, title="Sample Data Analysis")

Requirements

Python 3.7+
pandas
numpy

Chart Features

Interactive tooltips: Hover for detailed information
Column switching: Real-time data column selection
Export options: Built-in Plotly export functionality
Zoom and pan: Interactive chart exploration

Template System

Templates use comnt, a minimal markup system:

<!--[title-->
Dashboard Title
<!--title]-->

const data = /*[p_data*/ [...] /*p_data]*/;

Release files for interpareto 0.0.2

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for interpareto 0.0.2
File	Size	Uploaded
interpareto-0.0.2.tar.gz	22.4 kB	Jul 19, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for interpareto 0.0.2
File	Interpreter	ABI	Platform	Reset
interpareto-0.0.2-py2.py3-none-any.whl 24.5 kB Jul 19, 2025	Python 2, Python 3	none	any	Details

Total release size: 46.9 kB

Release files / interpareto-0.0.2.tar.gz

Download URL	interpareto-0.0.2.tar.gz
Size	22.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`8136ac211fb253d9a9e34c9a2be512683c5aee77f335fd34362f6571fcc5928e`
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Upload date	Jul 19, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.3`

Release files / interpareto-0.0.2-py2.py3-none-any.whl

Download URL	interpareto-0.0.2-py2.py3-none-any.whl
Size	24.5 kB
Tags	Python 2 Python 3
SHA-256 checksum How to use checksums	`906fbd1d5d6eb13e7c24fc1dd74510e4e075306aad1b5e3fd6dec2afe8aa1138`
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Upload date	Jul 19, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/6.1.0 CPython/3.12.3`

Release history Release notifications | RSS feed

This release

0.0.2 This release

Jul 19, 2025 2 release files

interpareto 0.0.2

InterPareto: Pandas DataFrames to Interactive Pareto Analysis

Installation

Quick start

Ideal for analyzing:

Avoid using with:

Features

Basic usage

Main Functions

render

render_inline

generate_pareto_data

Automatic Data Cleaning

Processing Warnings

Web Framework Integration

Complete Flask Example

Understanding Pareto Analysis

Chart Components

Customization Examples

Requirements

Chart Features

Template System

Project links

Key dates

1 maintainer

Credits

License expression

Release files for interpareto 0.0.2

Source distribution (sdist)

Built distribution (wheel)

Release files / interpareto-0.0.2.tar.gz

Release files / interpareto-0.0.2-py2.py3-none-any.whl

Release history Release notifications | RSS feed