data-analysis

name	data-analysis
description	Analyze datasets to extract insights, identify patterns, and generate reports. Use when exploring data, creating visualizations, or performing statistical analysis. Handles CSV, JSON, SQL queries, and Python pandas operations.
allowed-tools	Read Grep Glob Bash
metadata	{"tags":"data, analysis, pandas, statistics, visualization, csv, sql","platforms":"Claude, ChatGPT, Gemini"}

Data Analysis

When to use this skill

• Data exploration: Understand a new dataset
• Report generation: Derive data-driven insights
• Quality validation: Check data consistency
• Decision support: Make data-driven recommendations

Instructions

Step 1: Load and explore data

Python (Pandas):

import pandas as pd
import numpy as np

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

# Basic info
print(df.info())
print(df.describe())
print(df.head(10))

# Check missing values
print(df.isnull().sum())

# Data types
print(df.dtypes)

SQL:

-- Inspect table schema
DESCRIBE table_name;

-- Sample data
SELECT * FROM table_name LIMIT 10;

-- Basic stats
SELECT
    COUNT(*) as total_rows,
    COUNT(DISTINCT column_name) as unique_values,
    MIN(numeric_column) as min_val,
    MAX(numeric_column) as max_val,
    AVG(numeric_column) as avg_val
FROM table_name;

Step 2: Data cleaning

# Handle missing values
df['column'].fillna(df['column'].mean(), inplace=True)
df.dropna(subset=['required_column'], inplace=True)

# Remove duplicates
df.drop_duplicates(inplace=True)

# Type conversions
df['date'] = pd.to_datetime(df['date'])
df['category'] = df['category'].astype('category')

# Remove outliers (IQR method)
Q1 = df['value'].quantile(0.25)
Q3 = df['value'].quantile(0.75)
IQR = Q3 - Q1
df = df[(df['value'] >= Q1 - 1.5*IQR) & (df['value'] <= Q3 + 1.5*IQR)]

Step 3: Statistical analysis

# Descriptive statistics
print(df['numeric_column'].describe())

# Grouped analysis
grouped = df.groupby('category').agg({
    'value': ['mean', 'sum', 'count'],
    'other': 'nunique'
})
print(grouped)

# Correlation
correlation = df[['col1', 'col2', 'col3']].corr()
print(correlation)

# Pivot table
pivot = pd.pivot_table(df,
    values='sales',
    index='region',
    columns='month',
    aggfunc='sum'
)

Step 4: Visualization

import matplotlib.pyplot as plt
import seaborn as sns

# Histogram
plt.figure(figsize=(10, 6))
df['value'].hist(bins=30)
plt.title('Distribution of Values')
plt.savefig('histogram.png')

# Boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(x='category', y='value', data=df)
plt.title('Value by Category')
plt.savefig('boxplot.png')

# Heatmap (correlation)
plt.figure(figsize=(10, 8))
sns.heatmap(correlation, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.savefig('heatmap.png')

# Time series
plt.figure(figsize=(12, 6))
df.groupby('date')['value'].sum().plot()
plt.title('Time Series of Values')
plt.savefig('timeseries.png')

Step 5: Derive insights

# Top/bottom analysis
top_10 = df.nlargest(10, 'value')
bottom_10 = df.nsmallest(10, 'value')

# Trend analysis
df['month'] = df['date'].dt.to_period('M')
monthly_trend = df.groupby('month')['value'].sum()
growth = monthly_trend.pct_change() * 100

# Segment analysis
segments = df.groupby('segment').agg({
    'revenue': 'sum',
    'customers': 'nunique',
    'orders': 'count'
})
segments['avg_order_value'] = segments['revenue'] / segments['orders']

Output format

Analysis report structure

# Data Analysis Report

## 1. Dataset overview
- Dataset: [name]
- Records: X,XXX
- Columns: XX
- Date range: YYYY-MM-DD ~ YYYY-MM-DD

## 2. Key findings
- Insight 1
- Insight 2
- Insight 3

## 3. Statistical summary
| Metric | Value |
|------|-----|
| Mean | X.XX |
| Median | X.XX |
| Std dev | X.XX |

## 4. Recommendations
1. [Recommendation 1]
2. [Recommendation 2]

Best practices

1. Understand the data first: Learn structure and meaning before analysis
2. Incremental analysis: Move from simple to complex analyses
3. Use visualization: Use a variety of charts to spot patterns
4. Validate assumptions: Always verify assumptions about the data
5. Reproducibility: Document analysis code and results

Constraints

Required rules (MUST)

1. Preserve raw data (work on a copy)
2. Document the analysis process
3. Validate results

Prohibited (MUST NOT)

1. Do not expose sensitive personal data
2. Do not draw unsupported conclusions

References

• Pandas Documentation
• Matplotlib Gallery
• Seaborn Tutorial

Examples

Example 1: Basic usage

Example 2: Advanced usage

Quick Start

Unity3D 성능 데이터 분석 시나리오:

import pandas as pd
import matplotlib.pyplot as plt

# 1. Unity Profiler CSV 데이터 로드
df = pd.read_csv('unity_profiler_output.csv')

# 2. 프레임 타임 분석
print(df[['Frame', 'CPU', 'GPU', 'Memory']].describe())

# 3. 병목 구간 탐지 (16.67ms = 60fps 기준)
slow_frames = df[df['CPU'] > 16.67]
print(f"드랍 프레임 수: {len(slow_frames)} / {len(df)}")

# 4. 가장 무거운 함수 Top 10
top_functions = df.groupby('FunctionName')['CPUTime'].sum()
top_functions = top_functions.nlargest(10)
print(top_functions)

# 5. 메모리 추세 시각화
plt.figure(figsize=(12, 6))
df.groupby('Frame')['Memory'].mean().plot()
plt.title('Unity Memory Usage Over Time')
plt.xlabel('Frame')
plt.ylabel('Memory (MB)')
plt.savefig('unity_memory_trend.png')

분석 결과 → performance-optimization 스킬로 연동