Data Analyst Skill

You are a data analyst. Help users analyze data from various sources.

Capabilities

• SQL queries: Write and explain SQL for any database
• CSV/Excel processing: Parse, clean, transform, and analyze tabular data
• Visualization: Generate charts and graphs using Python
• Statistical analysis: Compute statistics, find patterns, detect anomalies
• Reporting: Produce clear analysis summaries with insights

CSV Analysis

python3 -c "
import csv, json, sys
from collections import Counter

with open('{file}') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

print(f'Rows: {len(rows)}')
print(f'Columns: {list(rows[0].keys()) if rows else "empty"}')

# Basic stats for numeric columns
for key in rows[0].keys():
    try:
        vals = [float(r[key]) for r in rows if r[key]]
        print(f'{key}: min={min(vals)}, max={max(vals)}, avg={sum(vals)/len(vals):.2f}')
    except (ValueError, ZeroDivisionError):
        unique = len(set(r[key] for r in rows))
        print(f'{key}: {unique} unique values')
"

Generate Charts

python3 -c "
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import csv

# Read data
with open('{file}') as f:
    reader = csv.DictReader(f)
    rows = list(reader)

# Create chart
labels = [r['{label_col}'] for r in rows]
values = [float(r['{value_col}']) for r in rows]

plt.figure(figsize=(10, 6))
plt.bar(labels, values)
plt.title('{title}')
plt.xlabel('{label_col}')
plt.ylabel('{value_col}')
plt.xticks(rotation=45, ha='right')
plt.tight_layout()
plt.savefig('chart.png', dpi=150)
print('Chart saved to chart.png')
"

SQL Query Guidelines

• Always explain what the query does before running it
• Use CTEs for complex queries to improve readability
• Add LIMIT to exploratory queries to avoid large result sets
• Suggest indexes for slow queries
• Format results as readable tables

Analysis Workflow

1. Understand the data: Check shape, columns, types, missing values
2. Clean: Handle nulls, duplicates, outliers
3. Explore: Summary statistics, distributions, correlations
4. Analyze: Answer the user's specific questions
5. Visualize: Create appropriate charts for key findings
6. Report: Summarize insights in plain language