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Python in Excel: How to simulate IRR with Copilot

Here’s a step‑to‑step guide to using Python inside Excel—powered by Copilot—to simulate Internal Rate of Return (IRR) and explore how small changes in cash flows affect your investment’s performance. By the end, you’ll be running Monte Carlo IRR simulations right within your spreadsheet technology blog.

Why Combine Python with Excel for IRR?

Excel’s built‑in IRR function is handy for a single set of cash flows. But what if you want to:

  • Run many scenarios to see the range of possible IRRs

  • Incorporate randomness (e.g., Monte Carlo) to model uncertainty

  • Automate bulk calculations without writing complicated formulas

That’s where Python—and Copilot’s AI assistance—comes in. Python has libraries like NumPy and pandas that make financial math and data manipulation a breeze. Copilot in Excel helps you generate and refine Python code interactively.

Prerequisites

  1. Microsoft 365 with Python in Excel Preview enabled

  2. Copilot turned on for your Microsoft account

  3. A basic cash‑flow table set up in Excel

If you don’t yet see the Python or Copilot icons on your Formulas or Automate ribbon, ask your admin to enable the preview features in the Microsoft 365 admin center.

Lay Out Your Cash Flows

First, create a simple table of cash flows in Excel. For example:

Year Cash Flow
0 -100 000
1 20 000
2 30 000
3 40 000
4 50 000

Give that range a name (e.g. select A1:B5 and call it CashFlows in the Name Box). You’ll reference it from Python.

Ask Copilot to Generate Your Python IRR Function

Click the Copilot icon on the ribbon and type:

“Create a Python function that takes a list of cash flows and returns IRR, using NumPy.”

Copilot will suggest something like:

python
import numpy as np

def calculate_irr(cashflows):
return np.irr(cashflows)

Insert that into a new Python code cell in Excel by clicking Insert.

Hook Up Excel Data to Python

In the same Python cell, reference your named range:

python
import numpy as np

def calculate_irr(cashflows):
return np.irr(cashflows)

# Read Excel range named "CashFlows" into a pandas DataFrame
import pandas as pd
df = pd.DataFrame(CashFlows)

# Extract the cash‑flow column as a list
flows = df["Cash Flow"].tolist()

# Compute IRR
irr_value = calculate_irr(flows)

# Return the result
irr_value

  1. Excel automatically exposes CashFlows as a pandas-compatible object.

  2. The final line (just irr_value) tells Excel to display that value in the cell where you wrote your Python formula.

Run a Single IRR Calculation

When you confirm, Excel will execute the Python code and deposit the IRR result back into the spreadsheet—no manual copy‑paste. Format that cell as a percentage:

→ IRR: 12.35 %

This matches what you’d get from =IRR(B2:B6) but now lives in Python.

Evolve to a Monte Carlo Simulation

To see how IRR varies if cash flows have uncertainty, let’s run 1 000 simulations where each year’s cash flow is randomly varied by ±10 %.

Replace your Python code cell with:

python
import numpy as np
import pandas as pd

# Read the base cash flows
df = pd.DataFrame(CashFlows)
base_flows = np.array(df["Cash Flow"].tolist())

# Function to run one IRR scenario
def simulate_irr(base, variation=0.1):
# Apply random variation to each cash flow
shocks = 1 + np.random.uniform(-variation, variation, size=base.shape)
scenario = base * shocks
return np.irr(scenario)

# Run Monte Carlo
n_sims = 1000
results = [simulate_irr(base_flows) for _ in range(n_sims)]

# Return an array of IRRs
results

Excel will output a spill range—1 000 IRR values in a column.

Analyze Your Simulation

With those 1 000 IRRs in the sheet, you can:

  • Calculate statistics:

    • =AVERAGE(spill_range)

    • =PERCENTILE.INC(spill_range, 0.05) for the 5th percentile

  • Chart the distribution:

    • Insert a histogram or box plot

  • Filter for best‑ or worst‑case scenarios

Visualize with a Histogram

  1. Select the IRR results range.

  2. On the Insert tab, choose Histogram.

  3. Adjust bins to see where most IRRs cluster.

This visual immediately shows you the probability of meeting a target return.

Let Copilot Help You Refine

If you need to add:

  • Different distributions (e.g., normal instead of uniform)

  • Correlation between years

  • More advanced metrics (e.g., Modified IRR)

Just ask Copilot:

“Modify the simulation to use a normal distribution with standard deviation of 5 %.”

Copilot will generate the updated NumPy code, which you insert and rerun.

Save and Share Your Workbook

Because the Python code lives in the workbook:

  • Colleagues can rerun simulations instantly

  • Auditors can inspect the exact code used

  • You can version control the Excel file with the embedded script

This makes your financial models transparent and reproducible.

Best Practices

  • Name your ranges clearly so Python code reads well.

  • Comment your Python functions for clarity.

  • Limit output size—if you only need summary stats, compute them in Python instead of spitting out 1 000 rows.

  • Pin library versions (via workbook settings) so results don’t change if NumPy updates.

Final Thoughts

Bringing Python into Excel via Copilot marries the best of both worlds: Excel’s familiar grid interface and Python’s computational power. Once you’ve simulated IRR scenarios in minutes, you’ll wonder how you ever built models with formulas alone. Try extending this pattern to NPV distributions, sensitivity analysis, or any other financial metric—and let Copilot guide your code along the way.

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