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data-analysis

Analyze data, process CSV/JSON files, compute statistics, and create data visualizations. Use when the user asks about data processing, statistics, or analysis.

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Data
Last commit
Oct 10, 2026

skills/data-analysis/SKILL.md

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Data Analysis Skill

Analyze and process data using Python in the sandbox.

Common Libraries

  • pandas: DataFrames, CSV/JSON/Excel processing
  • numpy: Numerical computing
  • matplotlib: Visualization (use Agg backend)

Install if needed: pip install pandas numpy matplotlib

Common Tasks

Read and analyze CSV

import pandas as pd
df = pd.read_csv('data.csv')
print(df.describe())
print(f"\nShape: {df.shape}")
print(f"\nColumns: {list(df.columns)}")
print(f"\nFirst 5 rows:\n{df.head()}")

Create visualization from data

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pandas as pd
import base64

df = pd.read_csv('data.csv')
df.plot(kind='bar', x='category', y='value', figsize=(10, 6))
plt.title('Data Overview')
plt.tight_layout()
plt.savefig('/tmp/chart.png', dpi=150)

with open('/tmp/chart.png', 'rb') as f:
    print(f'![chart](data:image/png;base64,{base64.b64encode(f.read()).decode()})')

JSON processing

import json
with open('data.json') as f:
    data = json.load(f)
# Process and analyze...

Guidelines

  • Always execute the analysis — don't just show code
  • Show key statistics: shape, dtypes, describe(), null counts
  • For large datasets, show head/tail and summary stats
  • Generate charts when it helps explain the data
  • Use base64 inline images for any visualizations

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