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data-analysis

Analyze and visualize data with pandas, numpy, and matplotlib/plotly.

Updated Aug 23, 2026
Search Tags:#data analysis#visualization#pandas#statistics#data cleaning#eda
One-click install
npx skills add https://github.com/RedBeret/agent-skill-catalog --skill data-analysis-redberet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/RedBeret/agent-skill-catalog/tree/main/skills/data-analysis
Command: npx skills add https://github.com/RedBeret/agent-skill-catalog --skill data-analysis-redberet

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of extracting meaningful insights from raw data, transforming it into actionable knowledge through cleaning, exploration, and visualization.

Core Features & Use Cases

  • Data Loading & Inspection: Handles various file formats (CSV, Excel, JSON, Parquet, SQL) and performs initial data quality checks.
  • Data Cleaning: Implements strategies for handling missing values, correcting data types, and removing duplicates.
  • Exploratory Data Analysis (EDA): Generates summary statistics, identifies correlations, and performs aggregations.
  • Visualization: Creates informative charts (histograms, line charts, scatter plots, bar charts, box plots) using Matplotlib and Plotly.
  • Statistical Basics: Covers descriptive statistics and outlier detection methods.
  • Reporting: Structures findings clearly with a defined report format.
  • Use Case: Analyze customer purchase data to identify top-selling products, understand sales trends over time, and visualize regional performance differences.

Quick Start

Analyze the attached 'sales_data.csv' file to find the total revenue per product category.

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 using pandas?▼

Exploratory data analysis using pandas involves loading your CSV file, handling missing values, generating summary statistics, and identifying correlations to inspect initial data quality and distributions.

What is the best way to visualize data trends and correlations in Python?▼

To visualize data trends and correlations, you can generate informative charts like histograms, scatter plots, and box plots using matplotlib and plotly libraries from a cleaned pandas DataFrame.

Can I load and analyze Excel, JSON, and Parquet files with this data analysis approach?▼

Yes, data analysis can load and inspect various file formats including Excel, JSON, Parquet, and SQL, performing initial data quality checks before cleaning and statistical analysis.

How do I clean missing values and correct data types for statistical analysis?▼

Data cleaning for statistical analysis implements strategies to handle missing values, correct data types, and remove duplicates, ensuring accurate descriptive statistics and outlier detection.

Does data analysis with matplotlib and plotly support business intelligence reporting?▼

Yes, data analysis supports business intelligence by structuring findings clearly with a defined report format, transforming raw data into actionable knowledge for research purposes.

Why do I need numpy for data manipulation and visualization tasks?▼

You need numpy for numerical operations during data manipulation because it supports the underlying mathematical calculations required for statistical basics and aggregations within pandas.

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