---

name: data-analysis description: Skill for data analysis and visualization using Python

Data Analysis Skill

This skill provides guidance and tools for data analysis and visualization tasks using Python.

Overview

This skill includes Python code snippets and scripts for common data analysis tasks:

• Data loading and exploration
• Statistical analysis
• Data visualization
• Data cleaning and preprocessing

Available Scripts

1. Basic Statistics

Use execute_script to run the statistics script:

python /nfs/FM/gongoubo/new_project/Agent-Handbook/mini-agents/Mini_Agents/skills/data-analysis/scripts/basic_stats.py

2. Data Visualization

Use execute_script to run the visualization script:

python /nfs/FM/gongoubo/new_project/Agent-Handbook/mini-agents/Mini_Agents/skills/data-analysis/scripts/visualization.py

3. Quick Code Execution

For quick analysis, use execute_code with inline Python code:

import numpy as np
import pandas as pd

# Create sample data
data = np.random.randn(100)
print(f"Mean: {np.mean(data):.2f}")
print(f"Std: {np.std(data):.2f}")
print(f"Min: {np.min(data):.2f}")
print(f"Max: {np.max(data):.2f}")

Usage Examples

Example 1: Calculate Statistics

Use execute_code tool:

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
mean = sum(data) / len(data)
print(f"Mean: {mean}")

Example 2: Load and Analyze CSV

Use execute_code tool:

import pandas as pd

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

# Display basic info
print(df.info())
print(df.describe())

# Calculate correlations
print(df.corr())

Example 3: Create Visualization

Use execute_code tool:

import matplotlib.pyplot as plt
import numpy as np

# Create sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

# Create plot
plt.figure(figsize=(10, 6))
plt.plot(x, y, label='sin(x)')
plt.xlabel('x')
plt.ylabel('y')
plt.title('Sine Wave')
plt.legend()
plt.grid(True)
plt.savefig('plot.png')
print("Plot saved to plot.png")

Best Practices

1. Always check if required libraries are installed before executing code
2. Use execute_code for quick, one-off analyses
3. Use execute_script for complex, reusable scripts
4. Handle errors gracefully and provide meaningful error messages
5. Clean up temporary files after execution

Required Libraries

• numpy
• pandas
• matplotlib
• scipy

Install with: pip install numpy pandas matplotlib scipy