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Jignesh-Ponamwar
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data-analysis

Analyze tabular datasets with pandas to uncover insights and data-quality issues.

5|4|Updated Apr 26, 2026
Search Tags:#insights#pandas#statistics#data-analysis#csv#eda#data-cleaning
One-click install
npx skills add https://github.com/Jignesh-Ponamwar/skills-mcp --skill data-analysis-jignesh-ponamwar
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/Jignesh-Ponamwar/skills-mcp/tree/main/skill_mcp/skills_data/data-analysis
Command: npx skills add https://github.com/Jignesh-Ponamwar/skills-mcp --skill data-analysis-jignesh-ponamwar

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Structured exploratory data analysis on tabular datasets to uncover insights, data quality issues, and patterns without manual scripting.

Core Features & Use Cases

  • Load and profile datasets from CSV, Excel, JSON, or other tabular formats.
  • Clean data: handle missing values, fix data types, trim whitespace, and prepare data for analysis.
  • Compute statistics and distributions: descriptive stats, percentiles, correlations, and outlier detection.
  • Group, pivot, and summarize: aggregate by categories and produce multi-dimensional views.
  • Communicate findings with clear summaries and ready-to-share results.

Quick Start

Analyze the attached dataset to produce a concise exploratory data analysis report.

Frequently Asked Questions about data-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I perform exploratory data analysis on a CSV file without writing scripts?▼

Exploratory data analysis on a CSV file can be performed automatically to profile datasets, compute descriptive statistics, and identify data quality issues without manual scripting. The Skill loads tabular formats and generates concise summaries of distributions and correlations.

Can I clean data and handle missing values in Excel datasets?▼

Yes, you can clean Excel datasets by handling missing values, fixing data types, and trimming whitespace. The Skill applies structured data cleaning to prepare tabular data from formats like Excel, CSV, and JSON for analysis.

What is the best way to detect outliers and compute statistics in pandas?▼

The best way to detect outliers and compute statistics in pandas is to apply structured profiling that calculates descriptive stats, percentiles, and correlations. The Skill uses Python with pandas and numpy to identify outliers and generate statistical summaries.

Does this data analysis approach work with JSON formats and database extracts?▼

Yes, this data analysis approach works with JSON, CSV, Excel, and database extracts. It loads tabular datasets from these formats to apply grouping, pivoting, and summarization tasks for multi-dimensional views.

Do I need Python and pandas to generate data profiling summaries?▼

Yes, you need Python with pandas and numpy to generate data profiling summaries. Optional visualization libraries are also supported to compute statistics, identify outliers, and produce visual summaries from your tabular datasets.

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