visualize-data

# Data Visualization Skill You are helping create visualizations from analyzed data. Follow these steps: ## 1. Setup Environment and Visualization Library **IMPORTANT: Always use a Python virtual environment for this task.** Before running any Python scripts, set up a virtual environment: ```bash # Create virtual environment (if not already created) python3 -m venv venv # Activate virtual environment source venv/bin/activate # On macOS/Linux # OR venv\Scripts\activate # On Windows # Install required packages pip install pandas matplotlib seaborn numpy ``` Use matplotlib and seaborn (Python) for creating charts: ```python import matplotlib.pyplot as plt import seaborn as sns import pandas as pd ``` ## 2. Required Visualizations ### Bar Chart: Average Salary by Department - X-axis: Department names - Y-axis: Average salary - Add value labels on top of bars - Use appropriate colors - Save as: `output/salary_by_dept.png` ### Bar Chart: Average Performance Score by Department - X-axis: Department names - Y-axis: Average performance score (0-5 scale) - Add value labels on top of bars - Use different color scheme - Save as: `output/performance_by_dept.png` ### Scatter Plot: Experience vs. Salary - X-axis: Years of experience - Y-axis: Salary - Add trend line (linear regression) - Show correlation coefficient in title - Color points by department (optional) - Save as: `output/experience_vs_salary.png` ### Pie Chart: Employee Distribution by Location - Show percentage for each location - Use distinct colors - Add labels with counts and percentages - Save as: `output/location_distribution.png` ## 3. Visualization Best Practices - Set figure size appropriately (e.g., 10x6 inches) - Use clear, readable fonts - Add titles, axis labels, and legends - Use color palettes that are colorblind-friendly - Save with high DPI (at least 300) - Close figures after saving to free memory ## 4. Complete Script Template ```python import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np import os # Set style sns.set_style("whitegrid") plt.rcParams['figure.figsize'] = (10, 6) # Read the CSV data df = pd.read_csv('../shared/sample-data.csv') # Create output directory os.makedirs('output', exist_ok=True) # 1. Bar Chart: Average Salary by Department plt.figure(figsize=(10, 6)) dept_salary = df.groupby('department')['salary'].mean().sort_values(ascending=False) bars = plt.bar(dept_salary.index, dept_salary.values, color='steelblue', alpha=0.8) plt.title('Average Salary by Department', fontsize=16, fontweight='bold') plt.xlabel('Department', fontsize=12) plt.ylabel('Average Salary ($)', fontsize=12) plt.xticks(rotation=45, ha='right') # Add value labels on bars for bar in bars: height = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2., height, f'${height:,.0f}', ha='center', va='bottom', fontsize=10) plt.tight_layout() plt.savefig('output/salary_by_dept.png', dpi=300, bbox_inches='tight') plt.close() print("✓ Created salary_by_dept.png") # 2. Bar Chart: Average Performance Score by Department plt.figure(figsize=(10, 6)) dept_perf = df.groupby('department')['performance_score'].mean().sort_values(ascending=False) bars = plt.bar(dept_perf.index, dept_perf.values, color='coral', alpha=0.8) plt.title('Average Performance Score by Department', fontsize=16, fontweight='bold') plt.xlabel('Department', fontsize=12) plt.ylabel('Average Performance Score', fontsize=12) plt.ylim(0, 5) plt.xticks(rotation=45, ha='right') # Add value labels on bars for bar in bars: height = bar.get_height() plt.text(bar.get_x() + bar.get_width()/2., height, f'{height:.2f}', ha='center', va='bottom', fontsize=10) plt.tight_layout() plt.savefig('output/performance_by_dept.png', dpi=300, bbox_inches='tight') plt.close() print("✓ Created performance_by_dept.png") # 3. Scatter Plot: Experience vs. Salary plt.figure(figsize=(10, 6)) correlation = df['years_experience'].corr(df['salary']) # Create scatter plot with different colors for departments for dept in df['department'].unique(): dept_data = df[df['department'] == dept] plt.scatter(dept_data['years_experience'], dept_data['salary'], label=dept, alpha=0.6, s=100) # Add trend line z = np.polyfit(df['years_experience'], df['salary'], 1) p = np.poly1d(z) plt.plot(df['years_experience'].sort_values(), p(df['years_experience'].sort_values()), "k--", alpha=0.5, linewidth=2, label='Trend Line') plt.title(f'Years of Experience vs. Salary (Correlation: {correlation:.3f})', fontsize=16, fontweight='bold') plt.xlabel('Years of Experience', fontsize=12) plt.ylabel('Salary ($)', fontsize=12) plt.legend() plt.grid(True, alpha=0.3) plt.tight_layout() plt.savefig('output/experience_vs_salary.png', dpi=300, bbox_inches='tight') plt.close() print("✓ Created experience_vs_salary.png") # 4. Pie Chart: Employee Distribution by Location plt.figure(figsize=(10, 8)) location_counts = df['location'].value_counts() colors = sns.color_palette('Set3', len(location_counts)) plt.pie(location_counts.values, labels=location_counts.index, autopct='%1.1f%%', colors=colors, startangle=90, textprops={'fontsize': 11}) plt.title('Employee Distribution by Location', fontsize=16, fontweight='bold', pad=20) plt.axis('equal') plt.tight_layout() plt.savefig('output/location_distribution.png', dpi=300, bbox_inches='tight') plt.close() print("✓ Created location_distribution.png") print("\n✓ All visualizations created successfully in output/ directory") ``` ## 5. Dependencies Required Ensure these packages are installed: ```bash pip install pandas matplotlib seaborn numpy ``` ## 6. Output Verification After running the script, verify: - All 4 PNG files are created in `output/` directory - Images are high quality (300 DPI) - Charts are readable and properly labeled - No warnings or errors in execution