Data Analysis

Automate data analysis, visualization, and HTML/PDF report generation with Python libraries.

Updated Jan 29, 2026

Search Tags:#python #reporting #data analysis #sqlite #visualization #pandas #csvkit

Ready to run

npx skills add https://github.com/balaraj74/F.R.I.D.A.Y --skill data-analysis-balaraj74

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Data Analysis
Source: https://github.com/balaraj74/F.R.I.D.A.Y/tree/main/skills/data-analysis
Command: npx skills add https://github.com/balaraj74/F.R.I.D.A.Y --skill data-analysis-balaraj74

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, matplotlib, seaborn, openpyxl, xlrd, csvkit, reportlab, and includes scripts (resource) components.

What problem does it solve?

This Skill automates the process of analyzing data, generating reports, and creating visualizations, making data-driven decision-making more accessible and efficient.

Core Features & Use Cases

Data Exploration: Load, clean, and explore datasets using Pandas.
Statistical Analysis: Perform aggregations, group-bys, and pivot operations.
Data Visualization: Create various charts (line, bar, histogram, scatter) using Matplotlib and Seaborn.
Report Generation: Automate the creation of HTML and PDF reports.
Command-Line Tools: Utilize csvkit and awk for quick data manipulation and analysis directly from the terminal.
Database Integration: Connect to and query SQLite databases.

Quick Start

Analyze the attached 'sales_data.csv' file and generate a summary report.

Frequently Asked Questions about Data Analysis

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

FAQPage Schema

How do I automate data analysis and report generation from CSV files?▼

Automating data analysis and report generation from CSV files is achieved by loading datasets with Pandas, computing statistical summaries, creating visualizations with Matplotlib, and exporting HTML or PDF reports. This workflow is fully automated using Python libraries.

Can I query an SQLite database and export the results to charts and reports?▼

Yes, you can query an SQLite database and export the results. This Skill integrates with SQLite for data extraction, allowing you to run queries, generate visual charts using Seaborn, and format the outputs into structured PDF or HTML reports.

What's the best way to generate statistical summaries and pivot tables in Python?▼

Generating statistical summaries and pivot tables in Python is best handled by Pandas. This Skill uses Pandas to perform aggregations, group-bys, and pivot operations directly on your loaded CSV or Excel data to produce detailed statistical summaries.

Does this approach support loading and analyzing Excel files alongside CSV data?▼

Yes, this approach supports loading and analyzing Excel files alongside CSV data. It utilizes the openpyxl and xlrd libraries to load Excel spreadsheets, allowing you to clean, filter, and analyze the data using Pandas just like CSV files.

How do I use command-line tools for quick CSV data manipulation?▼

Using command-line tools for quick CSV data manipulation is done through csvkit and awk. This Skill leverages these terminal tools to perform rapid data filtering, transformation, and exploration directly on CSV files without needing to write Python scripts.

What types of charts can I create when visualizing data with Matplotlib and Seaborn?▼

When visualizing data with Matplotlib and Seaborn, you can create line charts, bar charts, histograms, and scatter plots. This Skill automates the creation of these various charts to help you visually unlock insights from your analyzed datasets.