Which imputation methods are supported?

The skill supports a variety of methods including Mean, Median, Mode, KNN (K-Nearest Neighbors), Forward Fill, Interpolation, and Constant value imputation depending on the data type and distribution.

Can I use this skill just for visualization?

Yes, if you already have a clean dataset, you can skip the analysis and imputation steps and run the dashboard script directly to generate interactive visualizations.

What visualizations are included in the dashboard?

The generated dashboard includes summary statistics tables, time series plots, distribution histograms, correlation heatmaps, categorical bar charts, and scatter plot matrices.

How does it handle very large datasets?

While it handles standard CSVs well, computationally intensive methods like KNN may be slow on very large files. The skill includes logic to suggest simpler methods or sampling for high-volume data.

CSV Data Analyst

Name: CSV Data Analyst
Author: nguyendinhquocx

bynguyendinhquocx

0•

Data Science & ML

Analyzes CSV datasets, intelligently imputes missing values, and generates interactive Plotly Dash dashboards for exploratory data analysis.

This skill streamlines the end-to-end data analysis workflow by providing automated tools for data quality assessment and visualization. It automatically detects missing value patterns, recommends optimal imputation strategies—such as KNN, median, or forward fill—and applies them based on column distribution. Beyond cleaning, it generates a comprehensive, web-based Plotly Dash dashboard featuring correlation heatmaps, time-series trends, and distribution plots, making it an essential tool for turning raw CSV data into clean, actionable insights.

Key Features

01Interactive Plotly Dash dashboard generation with 6+ visualization types

02Comprehensive data quality reporting with before-and-after statistics

03Intelligent imputation using KNN, statistical, and temporal methods

04Smart handling of high-cardinality and mixed-type data columns

050 GitHub stars

06Automated missing value detection and statistical pattern analysis

Use Cases

01Cleaning messy datasets with missing values before training machine learning models

02Performing data quality audits and automated imputation on large CSV files

03Rapidly generating exploratory data analysis (EDA) dashboards for stakeholders

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nguyendinhquocx/code-ai data-analyst

For use in Claude.ai and ChatGPT

Download Skill