# Data Analysis

> Data analysis workflows and patterns for processing, analyzing, and visualizing data using Python data science libraries.

- Skill: `howmany-zeta/data-analysis` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add howmany-zeta/data-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/howmany-zeta/data-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Howmany-Zeta (https://skillmd.com/u/howmany-zeta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/howmany-zeta/data-analysis

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# Data Analysis Skill

This skill provides guidance and tools for data analysis workflows using Python's data science ecosystem.

## When to Use This Skill

Use this skill when you need to:

- Load and explore datasets from various file formats
- Clean and preprocess data for analysis
- Perform statistical analysis and compute metrics
- Create visualizations to understand data patterns
- Transform and aggregate data for reporting

## Supported Data Formats

This skill supports the following data formats:

| Format | Extension | Library |
|--------|-----------|---------|
| CSV | `.csv` | pandas |
| JSON | `.json` | pandas |
| Parquet | `.parquet` | pandas + pyarrow |
| Excel | `.xlsx`, `.xls` | pandas + openpyxl |
| SQL | Database connection | pandas + sqlalchemy |

## Analysis Workflow Overview

A typical data analysis workflow follows these steps:

1. **Data Loading**: Read data from files or databases into pandas DataFrames
2. **Data Inspection**: Explore structure, types, and basic statistics
3. **Data Cleaning**: Handle missing values, duplicates, and outliers
4. **Data Transformation**: Reshape, aggregate, and derive new features
5. **Statistical Analysis**: Compute descriptive and inferential statistics
6. **Visualization**: Create charts and plots to communicate insights
7. **Export Results**: Save processed data and analysis outputs

## Quick Start

```python
import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data.csv')

# Explore
print(df.info())
print(df.describe())

# Visualize
df.plot(kind='hist')
plt.show()
```

## Available Scripts

- **validate-data**: Validates data file format and structure before analysis