statistical_analysis

Compute mean, median, range, and count for numeric columns in tabular data.

1|Updated Mar 23, 2026

Search Tags:#statistics #data-analysis #numerical #data-cleaning #units #descriptive

Ready to run

npx skills add https://github.com/hellonish/singularity --skill statistical-analysis-hellonish

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical_analysis
Source: https://github.com/hellonish/singularity/tree/main/SKILLS/tier2_analysis/statistical_analysis
Command: npx skills add https://github.com/hellonish/singularity --skill statistical-analysis-hellonish

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill computes essential descriptive statistics for numerical data, enabling you to summarize central tendency and dispersion without guessing.

Core Features & Use Cases

Compute mean, median, range, and count for every numeric column.
Preserve units when provided and exclude non-numeric values.
Accept inputs as a list of dictionaries or a pandas DataFrame and return a structured summary.

Quick Start

Provide a tabular dataset (list of dicts or DataFrame) and request descriptive statistics for numeric columns.

Frequently Asked Questions about statistical_analysis

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

FAQPage Schema

How do I compute descriptive statistics for numerical columns in a pandas DataFrame?▼

To compute descriptive statistics, provide a pandas DataFrame or list of dictionaries to calculate mean, median, range, and count for numeric columns. The process handles type coercion and excludes missing values automatically.

Can I calculate mean and median for survey data with missing values?▼

Yes, you can calculate mean and median for survey data because the computation excludes missing values by default. It applies type coercion to ensure only valid numeric data is processed for the final statistical summary.

Does statistical analysis preserve units when computing numerical data summaries?▼

Statistical analysis preserves units when computing numerical data summaries if the units are present in the input. The output includes mean, median, range, and count alongside the original measurement units.

What's the best way to summarize central tendency for sensor logs?▼

The best way to summarize central tendency for sensor logs is computing mean and median across numeric variables. This process handles type coercion and excludes missing values, outputting a structured summary without fabricating data.

How do I get a structured summary of range and count from tabular data?▼

You get a structured summary of range and count by inputting tabular data as a list of dictionaries or pandas DataFrame. The computation validates numeric types, excludes non-numeric values, and returns the statistical output.

Can I use this for data cleaning before computing descriptive statistics?▼

You can use this after data preparation or data extraction to compute descriptive statistics. It performs type coercion and excludes missing values, serving as a numerical data summarization step rather than a primary data cleaning tool.