What Python libraries are required for this skill?

This skill utilizes pandas for data manipulation, and matplotlib and seaborn for generating high-quality visualizations.

Does it handle missing or messy data?

Yes, the skill includes missing data analysis and is built to handle common data quality issues gracefully using pandas' robust processing capabilities.

What kind of visualizations does it create?

It generates plots based on your data structure, including time-series charts for date columns, correlation heatmaps for numeric data, and distribution plots for categorical data.

Do I need to tell the skill which analysis to run?

No, the skill is designed to be fully autonomous. It inspects your data and automatically runs the most relevant analyses and visualizations without requiring additional user input.

CSV Data Summarizer

Name: CSV Data Summarizer
Author: prof-ramos

byprof-ramos

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Data Science & ML

Analyzes CSV files automatically to generate comprehensive statistical summaries and tailored visualizations using Python and pandas.

About

This skill transforms raw CSV data into actionable insights by automatically detecting data structures and performing domain-specific analyses without requiring user prompts. It intelligently identifies whether a dataset relates to sales, customers, finance, or operations, then applies the most relevant statistical tests and creates high-quality visualizations—such as time-series trends, correlation heatmaps, and category distributions—to provide a complete picture of data health and patterns in a single step.

Key Features

Comprehensive statistical summaries including missing data analysis
1 GitHub stars
Dynamic visualization mapping based on numeric, categorical, and temporal data
Zero-prompt automated analysis and visualization generation
Built-in support for industry-standard Python libraries like pandas and seaborn
Intelligent data type detection for domain-specific insights

Use Cases

Quickly auditing new datasets for data quality and missing values
Generating instant sales or financial reports from raw transaction logs
Visualizing customer demographic distributions and behavioral segments

About

Key Features

Comprehensive statistical summaries including missing data analysis
1 GitHub stars
Dynamic visualization mapping based on numeric, categorical, and temporal data
Zero-prompt automated analysis and visualization generation
Built-in support for industry-standard Python libraries like pandas and seaborn
Intelligent data type detection for domain-specific insights

Use Cases

Quickly auditing new datasets for data quality and missing values
Generating instant sales or financial reports from raw transaction logs
Visualizing customer demographic distributions and behavioral segments