1234567891011121314151617
            
import pandas as pd

# Sample company financial data
company_data = pd.DataFrame({
    "Metric": ["Revenue", "Gross Profit", "Operating Expense"],
    "Value": [500000, 200000, 120000]
})

# Sample industry benchmark data
industry_data = pd.DataFrame({
    "Metric": ["Revenue", "Gross Profit", "Operating Expense"],
    "Industry_Benchmark": [550000, 210000, 100000]
})

# Merge company data with industry benchmarks on 'Metric'
merged = pd.merge(company_data, industry_data, on="Metric")
print(merged)

              
123456789101112131415161718192021
            
import matplotlib.pyplot as plt

# Continuing from the merged DataFrame above
merged["Variance"] = merged["Value"] - merged["Industry_Benchmark"]

# Create a bar chart to visualize company vs. industry performance
labels = merged["Metric"]
company_values = merged["Value"]
industry_values = merged["Industry_Benchmark"]

x = range(len(labels))
width = 0.35

plt.bar(x, company_values, width=width, label="Company")
plt.bar([i + width for i in x], industry_values, width=width, label="Industry Benchmark")
plt.xticks([i + width/2 for i in x], labels)
plt.ylabel("Amount")
plt.title("Company vs. Industry Benchmark")
plt.legend()
plt.tight_layout()
plt.show()