Krisk

Abstract

1. Introduction

Krisk is a Python charting library and a local MCP research server. It combines the notebook workflow from Chartics with the live-server ideas from Flarisk in one package, without requiring either project as a dependency.

Krisk 0.9.0 is an unpublished, local-first beta. Traditional notebook charting does not require a server, MCP client, PostgreSQL, or even Krisk's internal SQLite database.

See the end-to-end tutorial and typed chart reference for more chart kinds and options.

2. Objective

Statistical Interactive Visualization with pandas+Jupyter integration on top of Echarts.

This project demonstrates how Python, Jupyter Notebook can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/napjon/krisk.git
cd krisk

import pandas as pd
from krisk import Chart, ChartSpec

sales = pd.DataFrame(
    {
        "month": ["Jan", "Feb", "Mar"],
        "revenue": [82, 93, 112],
    }
)

chart = Chart.from_dataframe(
    sales,
    ChartSpec(
        kind="area",
        x="month",
        y="revenue",
        aggregate="sum",
        title="Revenue trend",
        description="Monthly recorded revenue",
        smooth=True,
    ),
)

chart  # rich inline output in Jupyter

chart.to_html("revenue.html")

import krisk.plot as kk

kk.bar(sales, "month", y="revenue", how="sum")
kk.pie(sales, "month", y="revenue", how="sum")
kk.number(sales, "revenue", how="sum")

git clone https://github.com/napjon/krisk.git
cd krisk
uv sync --extra dev
uv run krisk demo

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by napjon and published on GitHub under the BSD 3-Clause "New" or "Revised" License. Please follow the license terms and credit the original author when you use or modify this code.