Lernen Automating Financial Report Generation | Retrieving and Reporting Financial Data

Python for Accountants


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import pandas as pd

# Simulated internal financial data
internal_data = pd.DataFrame({
    "Account": ["Revenue", "COGS", "Operating Expenses"],
    "2023_Q4": [120000, 70000, 20000]
})

# Simulated external benchmark data
external_data = pd.DataFrame({
    "Metric": ["Industry Avg Revenue", "Industry Avg COGS", "Industry Avg OpEx"],
    "2023_Q4": [130000, 75000, 21000]
})

# Consolidate internal and external data into a summary report
report = pd.DataFrame({
    "Description": ["Revenue", "COGS", "Operating Expenses"],
    "Company": internal_data["2023_Q4"],
    "Industry Average": external_data["2023_Q4"]
})

# Calculate variance from industry average
report["Variance"] = report["Company"] - report["Industry Average"]

print(report)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 2

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Suggested prompts:

Can you explain how the variance is calculated in the report?

What are the benefits of exporting the report to Excel versus CSV?

How can I customize the report to include additional financial metrics?

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              12345678910111213141516171819202122232425
            
import pandas as pd

# Simulated internal financial data
internal_data = pd.DataFrame({
    "Account": ["Revenue", "COGS", "Operating Expenses"],
    "2023_Q4": [120000, 70000, 20000]
})

# Simulated external benchmark data
external_data = pd.DataFrame({
    "Metric": ["Industry Avg Revenue", "Industry Avg COGS", "Industry Avg OpEx"],
    "2023_Q4": [130000, 75000, 21000]
})

# Consolidate internal and external data into a summary report
report = pd.DataFrame({
    "Description": ["Revenue", "COGS", "Operating Expenses"],
    "Company": internal_data["2023_Q4"],
    "Industry Average": external_data["2023_Q4"]
})

# Calculate variance from industry average
report["Variance"] = report["Company"] - report["Industry Average"]

print(report)

War alles klar?

Danke für Ihr Feedback!

Abschnitt 3. Kapitel 2