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name: data-analysis description: Data analysis workflows and patterns for processing, analyzing, and visualizing data using Python data science libraries. version: 1.0.0 author: AIECS Team tags:

• data
• analysis
• pandas
• visualization
• statistics dependencies: [] recommended_tools:
• python
• pandas
• numpy
• matplotlib scripts: validate-data: path: scripts/validate-data.py mode: native description: Validates data file format and structure parameters: file_path: type: string required: true description: Path to the data file to validate format: type: string required: false description: Expected data format (csv, json, parquet)

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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

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