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data-analyst

Analyze datasets with pandas, numpy, matplotlib, seaborn, and SQL.

30|7|Updated Mar 1, 2026
Search Tags:#data analysis#sql#visualization#pandas#statistics#eda
One-click install
npx skills add https://github.com/rfdiosuao/openfang-cn --skill data-analyst-rfdiosuao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analyst
Source: https://github.com/rfdiosuao/openfang-cn/tree/main/crates/openfang-skills/bundled/data-analyst
Command: npx skills add https://github.com/rfdiosuao/openfang-cn --skill data-analyst-rfdiosuao

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill empowers users to analyze datasets, perform statistical calculations, generate visualizations, and derive meaningful insights from data, making complex data exploration accessible.

Core Features & Use Cases

  • Exploratory Data Analysis (EDA): Load, inspect, and understand dataset characteristics.
  • Data Cleaning: Handle missing values, standardize formats, and remove duplicates.
  • Visualization: Create informative charts (bar, line, scatter, histogram) to represent data patterns.
  • Statistical Analysis: Compute descriptive statistics and perform hypothesis testing.
  • Use Case: Analyze customer purchase history to identify top-selling products and customer segments for targeted marketing campaigns.

Quick Start

Analyze the attached dataset 'sales_data.csv' to identify the top 5 selling products by revenue.

Frequently Asked Questions about data-analyst

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

FAQPage Schema
How do I perform exploratory data analysis on a CSV dataset using pandas?▼

Exploratory data analysis using pandas involves loading your CSV file, inspecting dataset characteristics, computing descriptive statistics, and identifying initial patterns or correlations before deeper modeling.

What is the best way to clean data and handle missing values in Python?▼

Cleaning data in Python requires standardizing formats, removing duplicate records, and systematically handling missing values to ensure accurate statistical analysis and reliable visualizations.

How do I create data visualizations like scatter plots and histograms in Python?▼

Data visualizations in Python are created using matplotlib and seaborn to generate bar charts, line graphs, scatter plots, and histograms that effectively communicate data distributions and patterns.

Can I use SQL for data analysis alongside Python libraries?▼

SQL is fully supported for data analysis alongside Python libraries like pandas and numpy, allowing you to query relational databases and then process the extracted data for statistical calculations.

Does Python statistical analysis support hypothesis testing and correlation identification?▼

Python statistical analysis supports computing descriptive statistics, performing hypothesis testing, and identifying correlations to derive meaningful insights and validate assumptions within your dataset.

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