The Easy Guide To Python In Excel Using ChatGPT

I’ll never forget the night my Excel file actually begged for mercy.

It was month-end close, and my workbook — “Final _v9 (REAL FINAL) v2.xlsx” — had 12 tabs, 4 linked sheets, and enough circular references to qualify as modern art. Every time I hit F9, the fans on my laptop sounded like a jet taking off.

And yet, like every finance pro, I powered through. Copy, paste, SUMIF, repeat.

Then I heard the news: Excel now runs Python.Shortly after, Microsoft announced a major update: Python integration is now available directly within Microsoft Excel. This collaboration brings Python’s powerful data analysis capabilities right into the familiar Excel environment, transforming how both data scientists and business users approach analytics and automation.

At first, I laughed. Because I pictured some developer with three monitors writing code no one in finance would ever read. But then I saw it — a tiny formula in Excel that started with =PY(… and suddenly I realized something:

This wasn’t a coding tool. It was an automation engine disguised as a spreadsheet.

Now, instead of building 47 formulas to forecast revenue, I can just ask Python inside Excel to do it — in one cell. No macros, no add-ins, no black-screen scripting. Just Excel… upgraded.

This isn’t about turning finance pros into programmers. It’s about letting the tools we already use become smarter — cleaner data, faster calculations, and fewer all-nighters spent chasing broken links.

In this guide, I’ll show you how to:

• Use Python built directly into Excel to automate your financial models,
• Work with DataFrames (the “tables” Python loves),
• Build forecasts, visuals, and sensitivity analyses, and
• Let ChatGPT write the code for you when you don’t have time to learn it.

What Is Python In Excel

For decades, Excel has been the Swiss Army knife of finance — brilliant for formulas, pivot tables, and caffeine-fueled modeling sessions. But it’s always had limits: no real automation, messy data cleaning, and a deep fear of “file too large” errors.

That’s why Python in Excel changes everything. By integrating Python, Excel now brings data science and advanced analytics capabilities directly into the familiar spreadsheet environment, making complex data analytics accessible to finance professionals.

So what exactly is it?

It’s not a plug-in. It’s not an add-in.
It’s Python built directly into Excel.

You can literally type =PY() in a cell, and Excel runs Python calculations right there — safely, in the cloud — then spills the results back into your workbook just like a formula.

Think of it like having a super-charged calculation engine sitting behind your spreadsheet, ready to handle anything formulas can’t.

No installs. No IT tickets. No black screens. Python in Excel does not require a traditional Python installation, so users can get started without any setup or configuration.

What you get out of it

When Microsoft integrated Python into Excel, they also bundled in powerful Python libraries for advanced analytics and data science tasks. These libraries enable users to perform data analysis, visualization, and machine learning directly within Excel:

Library	What It Does for You
pandas	The “data table” engine — think of it as PivotTables that can actually merge, clean, and reshape data automatically.
numpy	Math on steroids — fast, vectorized calculations for huge datasets.
matplotlib	Instant visuals — line charts, histograms, or sensitivity plots, all inside Excel.
statsmodels / scikit-learn	Forecasting and regression — build trendlines or predictions directly in a cell.

These Python libraries are pre-installed — you don’t have to configure anything. You just use them.

Why this matters for finance teams

You already have the front end you love (Excel). Python brings the back end you’ve always needed:

• Data cleanup without tears — drop in your ERP export and fix dates, names, and categories in one command.
• Automated calculations — one formula can handle what used to be ten helper columns.
• Smarter forecasting — linear, exponential, even seasonal models without separate software.
• Dynamic visuals — charts that refresh automatically when your data does.
• Enterprise-safe — runs in a Microsoft-managed sandbox; all Python code and calculations are contained within the Excel workbook, ensuring data integrity and security. Nothing leaves the workbook.

In short: it turns the tool you already trust into the automation platform you always wanted.

Real-world translation

Here’s what this means in plain finance speak:

Instead of building 12 linked sheets to project revenue, you can now perform these calculations within a single Excel sheet using Python:

import pandas as pd
df = xl("tbl_sales")
df.groupby("Month")["Revenue"].sum().rolling(3).mean()

That one cell cleans your data, aggregates it, and calculates a rolling three-month average forecast — all at once.

And yes, it updates automatically when your source table updates.

Getting Started: Access, Setup, and Security

So you’re ready to try Python in Excel — and the good news is, there’s no install marathon or IT approval queue standing in your way.

Currently, Windows users have priority access to Python in Excel, while availability for Mac users will be coming later.

If you’ve got Microsoft 365, you’re halfway there.

Who Has Access (Right Now)

As of today, Python in Excel is available for:

• 🧠 Microsoft 365 Insiders — Beta Channel users get early access.
• 💼 Enterprise & Business Plans — gradually rolling out across 2024–2025.
• 👨‍💻 Eventually, everyone — it’ll ship to all Microsoft 365 subscribers once it’s stable.

This feature is designed to benefit all Excel users, making advanced analytics and data analysis accessible within the familiar Excel environment.

If you’re not in the Insider Program yet, you can join for free in your Microsoft 365 account settings (takes about two minutes).

Once you’ve got the right version, you’ll see a new button on your Formulas tab called “Insert Python” — or you can simply type =PY() in any cell.

Enabling Python in Excel

You don’t need to install Python, Anaconda, or any third-party software—there’s no installing additional components required, making the process seamless. Here’s how it works:

1. Open Excel (Microsoft 365 desktop app).
2. Go to the Formulas tab → Click Insert Python.
3. Or, in any cell, type =PY(“Hello world”).
4. Excel will run your code instantly and return “Hello world” as if it were a normal formula.

You just ran your first Python command in Excel — no setup, no admin rights, no IT guy breathing down your neck.

How Security Works

This is the part your IT team actually likes.

Python in Excel doesn’t run locally — it runs inside a Microsoft-hosted, secure sandbox. That means:

• ✅ Your company data stays inside Microsoft’s cloud — not sent to random servers. All Python code execution and data processing occur within the Microsoft cloud, ensuring enterprise-grade security and compliance.
• ✅ Python can’t access your hard drive or network drives.
• ✅ No risk of malware or rogue scripts.
• ✅ Every workbook runs in isolation.

It’s basically a virtual lab inside Excel: your formulas call Python, Python does the math in the cloud, and the clean results spill right back into your sheet.

In short — it’s enterprise-safe, finance-friendly, and IT-approved.

Quick Reality Check

If you’ve ever struggled to get IT to approve Power BI Gateway connections or VBA scripts, you’ll appreciate how smooth this is. You don’t need admin rights. You don’t need to install Python. You don’t even need to leave Excel.

Adding Python to Excel is now as simple as writing a formula—just write =PY() and go, with no extra steps required.

DataFrames: The Finance Pro’s New Best Friend

If Excel cells are like Lego bricks, a DataFrame is the fully assembled Death Star.

It’s the structure that powers everything you do with Python in Excel — and once you understand it, you’ll see why it’s the single biggest upgrade to your workflow since the SUMIFS function. DataFrames are especially ideal for working with large datasets, enabling efficient data processing and analysis within Excel.

What the heck is a DataFrame?

A DataFrame is basically a table inside Python — rows and columns, just like Excel. But here’s the twist: it’s smarter.

Think of it as a dynamic mini-database that lives inside your workbook.

It can:

• Store millions of rows (without freezing your laptop)
• Combine multiple datasets in seconds, letting you access and consolidate all the data you need for analysis in one place
• Clean and reshape data automatically
• Handle calculations across entire columns instantly

If PivotTables, Power Query, and helper formulas had a baby… it’d be a DataFrame.

How it works inside Excel

When you use Python in Excel, you can pull your spreadsheet data into a DataFrame with one line of code. You can also import external data into Excel, such as from web APIs or databases, and analyze it using Python DataFrames.

Example:

import pandas as pd

df = xl("tbl_sales")  # Reads an Excel Table into a DataFrame
df.head()             # Displays the first few rows

Boom. You just pulled your table into Python’s memory — and now you can do practically anything with it.

Cleaning and transforming data (without tears)

Let’s say you have sales data exported from your ERP — thousands of rows, inconsistent dates, maybe even a few blank product names (because of course there are). With Python in Excel, you can leverage advanced data manipulation to easily clean and transform your data for analysis.

Here’s how you’d clean it in Python — inside Excel:

df["Date"] = pd.to_datetime(df["Date"], errors="coerce")
df["Revenue"] = pd.to_numeric(df["Revenue"], errors="coerce")
df = df.dropna(subset=["Product", "Revenue"])  # Drop blanks
df = df.groupby("Month")["Revenue"].sum().reset_index()
df

In five lines, you: ✅ Fixed your dates ✅ Converted numbers ✅ Removed bad rows ✅ Summed revenue by month ✅ Output a clean table back into Excel

Try doing that with formulas and you’ll still be debugging in Q4.

Why finance teams love DataFrames

Finance lives in data chaos: CSVs from ERP, budgets from planning tools, actuals from accounting, and 17 tabs of “last version” Excel files.

DataFrames give you structure. They can:

• Merge multiple sources (Actuals + Budget + Forecast)
• Calculate metrics (Variance, YoY growth, margins)
• Reshape tables from wide to long (for Power BI or dashboards)
• Aggregate by dimension (Entity, Cost Center, Department)

DataFrames also make it easier to analyze complex financial data and generate actionable insights.

And the best part? Every time your source data updates, the Python cell just re-runs automatically — no macros, no refresh buttons, no coffee-fueled “why is this off by $0.02” hunts.

Quick Finance Example

Here are examples of how to use Python in Excel for finance:

import pandas as pd
df = xl("tbl_PnL")  # Table with Revenue, COGS, Opex columns

df["Gross Margin %"] = (df["Revenue"] - df["COGS"]) / df["Revenue"]
df["EBITDA"] = df["Revenue"] - df["COGS"] - df["Opex"]

summary = df.groupby("Department")[["Revenue", "EBITDA"]].sum().reset_index()
summary

That’s a department-level P&L in one line.
Excel formulas? About 12 columns and a small headache.

Capabilities: What You Can Actually Do

Alright, you’ve got Python running in Excel, and you’ve met your new best friend — the DataFrame.
Now let’s talk about what you can actually do with it.

This is where the fun begins.
Because you’re not just building cleaner spreadsheets — you’re automating entire workflows, running forecasts, and building visualizations that would normally take multiple tools (or multiple analysts).

Here’s what Python in Excel unlocks for finance pros like us.

Forecasting: Build Smart Models That Update Themselves

Excel formulas can predict trends… kind of. But Python lets you create actual models that learn from your data. With Python in Excel, you can also build machine learning models for more accurate and sophisticated predictions.

Let’s say you’ve got three years of monthly revenue. You can forecast the next six months in a single cell:

import pandas as pd
import statsmodels.api as sm

df = xl("tbl_revenue")  # Date, Revenue
df["Date"] = pd.to_datetime(df["Date"])
df = df.set_index("Date").asfreq("M")

model = sm.tsa.SimpleExpSmoothing(df["Revenue"]).fit()
forecast = model.forecast(6)
forecast

In plain English: ✅ It reads your data, ✅ Recognizes time intervals, ✅ Builds a model, and ✅ Predicts your next periods.

No add-ins, no scripts, no IT dependency — just Excel and one formula.

Want to change the assumption? Update your table and hit Enter. Python recalculates instantly, just like a regular cell.

Visualization: Automate Your Charts (and Make Them Beautiful)

Forget rebuilding charts every time numbers change. Python’s Matplotlib library can generate clean, dynamic visuals right inside Excel. While traditional Excel charts are often static and limited in customization, using Python libraries enables advanced visualizations, including interactive and dynamic charts that provide deeper insights and more engaging reports.

Example:

import matplotlib.pyplot as plt
df = xl("tbl_forecast")

plt.plot(df["Month"], df["Revenue"], label="Actual")
plt.plot(df["Month"], df["Forecast"], label="Forecast", linestyle="--")
plt.title("Monthly Revenue Forecast")
plt.legend()
plt.show()

Boom — you just created a chart that updates whenever your data does.

This means your monthly reporting dashboards can actually be self-refreshing — no more reformatting chart labels or copy-pasting screenshots into PowerPoint.

Sensitivity Analysis: One Model, Infinite Scenarios

Python in Excel makes sensitivity analysis effortless. Instead of building 10 what-if tabs, you can use one DataFrame and a loop.

Example: testing how COGS % impacts EBITDA:

import pandas as pd
df = xl("tbl_inputs")

for cogs in [0.50, 0.55, 0.60]:
    df["COGS_%"] = cogs
    df["EBITDA"] = df["Revenue"] - (df["Revenue"] * df["COGS_%"]) - df["Opex"]
    print(f"COGS {cogs*100:.0f}% → EBITDA = ${df['EBITDA'].sum():,.0f}")

Python functions can also be used to automate complex scenario modeling and sensitivity analysis in Excel, making it easy to extend beyond standard formulas.

Now you can instantly compare multiple scenarios without creating new sheets. Want a quick tornado chart or a scenario summary table? Python’s got you.

Data Cleanup: Stop Fighting Your ERP Exports

Python’s pandas library is built for messy finance data. Python libraries like pandas offer robust support for cleaning and preparing finance data in Excel, including handling errors and ensuring compatibility with various Excel functionalities.
Instead of fixing errors by hand, you can automate cleanup like this:

df = xl("tbl_GL")
df["Amount"] = pd.to_numeric(df["Amount"], errors="coerce")
df = df.dropna(subset=["Account", "Amount"])
df = df.groupby("Department")["Amount"].sum().reset_index()
df

That’s your GL rolled up by department — cleaned and aggregated, ready for reporting.

Real-Time Dashboards: Your Model Becomes a System

Because Python lives inside Excel, every change in your inputs instantly flows through your calculations, visuals, and outputs. With Python integration, your Excel spreadsheet becomes a dynamic, cloud-powered analytics platform.

• Change assumptions → outputs refresh.
• Swap a source table → analysis updates.
• Add a new region → forecasts and charts rebuild themselves.

It’s not a “report” anymore — it’s a live model.

Building Your First Python-in-Excel Financial Model (Step-by-Step)

You’ve seen the power — now let’s actually use it. In this section, I’ll walk you through building your first financial model entirely inside Excel, powered by Python.

You will learn how to run Python directly within Excel to build and automate your financial models.

We’ll keep it simple: a clean, 5-year forecast that automatically updates when you tweak assumptions.

No macros. No extra files. No duct tape formulas.

Just Excel + Python.

Step 1: Set Up Your Inputs

Start by creating a small assumptions table. You can put this anywhere in your workbook, including any worksheet, making it easy to organize your model and keep related data together. Format it as an Excel Table (Ctrl + T).

Year	Units	Price	COGS %	Opex %	Tax Rate
2025	1000	50	0.55	0.25	0.25
2026	1050	52	0.54	0.25	0.25
2027	1100	54	0.53	0.24	0.25
2028	1160	56	0.52	0.24	0.25
2029	1220	58	0.51	0.23	0.25

Name this table tbl_inputs.

Step 2: Insert a Python Cell

Click an empty cell below your table and type this formula:

=PY(
"""
import pandas as pd

df = xl("tbl_inputs")

# Core calculations
df["Revenue"] = df["Units"] * df["Price"]
df["COGS"] = -df["Revenue"] * df["COGS %"]
df["Gross Profit"] = df["Revenue"] + df["COGS"]
df["Opex"] = -df["Revenue"] * df["Opex %"]
df["EBITDA"] = df["Gross Profit"] + df["Opex"]
df["Tax"] = -df["EBITDA"] * df["Tax Rate"]
df["Net Income"] = df["EBITDA"] + df["Tax"]

# Round and reorder
df = df[["Year", "Revenue", "COGS", "Gross Profit", "Opex", "EBITDA", "Tax", "Net Income"]].round(2)
df
"""
)

Python cells in Excel allow you to embed and execute Python code directly within your worksheet, using special syntax like xl() to reference Excel data.

Hit Enter.

If everything’s set up right, Excel will instantly display your model — a fully calculated 5-year P&L.

You just built a forecast in a single cell.

Step 3: Visualize It Instantly

Want to see it as a chart? Drop in another Python cell and add:

import matplotlib.pyplot as plt
df = xl("tbl_inputs")
df["Revenue"] = df["Units"] * df["Price"]

plt.plot(df["Year"], df["Revenue"], label="Revenue", marker="o")
plt.title("Revenue Forecast")
plt.ylabel("USD")
plt.legend()
plt.show()

Python-generated visuals appear directly within the Excel grid, making it easy to integrate analytics and reporting alongside your data.

Now you’ve got a live chart that updates whenever your assumptions change.

No reformatting. No refreshing. No “why is this number different in the graph?” nonsense.

Step 4: Add a Scenario Lever

Let’s say you want to see what happens if prices grow 10% faster. Add a new input cell called Price Adjustment and set it to 0.10.

Then tweak your Python formula slightly:

price_adj = float(xl("Price_Adjustment").iloc[0,0])
df["Price"] = df["Price"] * (1 + price_adj)

By changing the input value in the Price Adjustment cell, all related outputs in your model update instantly, reflecting the new value and demonstrating the power of dynamic calculations.

Now you can instantly re-run your model with a single assumption change — no rebuilds, no copy-paste.

Step 5: Explain It to Your Future Self

One of the best parts of Python in Excel is how readable it is. You can add comments like:

# Calculate gross profit
df["Gross Profit"] = df["Revenue"] + df["COGS"]

You can also include additional information in your Python cells to help future users understand the logic and calculations.

Which means when you open this workbook six months from now (or share it with someone else), you’ll actually remember what’s happening. Try doing that with a nested IF formula.

How to Use ChatGPT to Write the Code for You

Let’s be honest — the number-one thing that stops finance pros from trying Python is this:

“I don’t know how to code.”

Good news: you don’t have to.

Because you’ve already got the world’s best coding assistant — ChatGPT — and it can literally write your Python in Excel code for you.

Writing Python code in Excel can feel a lot like working in a Jupyter notebook, offering an interactive and flexible environment for experimenting and running code.

All you need to do is tell it what you want to happen in plain English.

Step 1: Describe the outcome, not the syntax

When prompting ChatGPT, you don’t need to know function names or libraries.
You just need to describe your problem like you would to an analyst on your team.

Example prompt:

“Write Python in Excel code that reads an Excel table called tbl_inputs with columns Year, Units, Price, and COGS %.
Calculate Revenue, COGS, and EBITDA.
Then output a clean table with those columns rounded to two decimals.”

Within seconds, ChatGPT will give you the full code block, properly formatted and ready to paste between triple quotes inside =PY(“””…”””).

You just built your automation without writing a single formula.

Step 2: Copy it into Excel

Take the code ChatGPT gives you and paste it directly into a Python cell in Excel:

=PY(
"""
# Paste ChatGPT-generated code here
"""
)

Hit Enter. Boom — your model runs. The output may appear as a Python object in the cell, which you can further manipulate or reference in your analysis.

If it errors, no panic. Just copy the error message and ask ChatGPT:

“Explain this error and fix the code.”

It’ll debug it for you and tell you what went wrong (in English, not programmer-speak).

Step 3: Ask ChatGPT to explain each line

Once your code works, ask ChatGPT to break it down for you:

“Explain each line of this Python code in simple finance terms.”

This is how you learn — quickly. You’ll start seeing patterns, like:

• df[“Column”] means “create or update a column.”
• xl(“tbl_name”) means “grab data from Excel.”
• groupby() means “summarize or aggregate.”

Understanding how to reference Excel tables and ranges in your Python code is key to effective integration, as correct references ensure your formulas interact with the right worksheet objects.

Before long, you’ll start reading Python like formulas — intuitive, logical, and repeatable.

Step 4: Use ChatGPT to iterate faster than ever

Once you’ve got a working model, your real power move is iteration.

Ask things like:

• “Add a tax calculation and Net Income line.”
• “Include a 10% growth scenario and plot it on a chart.”
• “Change the output table to show Gross Margin %.”

ChatGPT will update your code instantly. You copy, paste, refresh — done.

You can also reuse the same code across multiple workbooks or scenarios, making updates and maintenance much easier.

This is how you scale from “I built a forecast” to “I built an entire model template.”

Pro Tip: Save your best prompts

When you find a ChatGPT prompt that works well, save it in a “Finance Automation Prompts” sheet.
Over time, you’ll build your own library of reusable automation prompts — everything from variance analysis to cash flow forecasting.

That’s your new internal IP.
Not just the model — the method.

Best Practices for Finance Teams

Here’s the thing about automation: it’s only magic if it actually keeps working.

Most finance teams don’t have a problem with automation itself — they have a problem with maintenance.Somebody builds a “cool” Python model, it works once, then it dies the second someone renames a column or adds a new tab called “Final_FINAL_v3_UseThisOne.xlsx.”

Maintaining reliable Excel calculations is essential for successful automation and long-term model performance.

Let’s make sure that’s not you.

1. Use Excel Tables — Always

If you take one thing away from this section, make it this: Turn every data range into a Table (Ctrl + T).

Organizing your data within an Excel worksheet as tables improves reliability and readability. Python in Excel can read data with the xl(“TableName”) function, but if you’re referencing a random range like A2:D1000, it’s only a matter of time before someone adds a row and breaks your model.

Tables automatically expand, keep names consistent, and make your code read like this:

df = xl("tbl_inputs")

That’s clean. Reusable. Understandable.

2. Keep Code Blocks Small

Don’t try to automate the entire world in one Python cell.

Think modular — one task per cell:

• One cell for data cleaning
• One cell for calculations
• One cell for outputs or charts

You can also use Python in Excel to write Excel files or export results as needed, keeping your workflow organized.

This makes debugging (and collaboration) way easier. If something breaks, you know exactly where to look.

3. Comment Everything

Future You will not remember what you were thinking. And neither will anyone else on your team.

Use comments to explain logic in plain English:

# Calculate Gross Profit before Opex
df["Gross Profit"] = df["Revenue"] - df["COGS"]

In Excel, the formula bar allows you to view and edit Python code with comments, making your code easier to understand and maintain.

It takes five extra seconds and saves hours of confusion later.

4. Validate Before You Celebrate

Automation doesn’t mean accuracy — it means speed. And fast mistakes are still mistakes.

Every time you build a new model: ✅ Recalculate your numbers manually once. ✅ Cross-check a few random lines. ✅ Make sure totals tie back to your source data. Verifying your calculated values is essential to ensure your model’s accuracy and reliability.

Nothing destroys credibility faster than an automated report that’s confidently wrong.

5. Version Control Is Your Friend

If you’re iterating on your model, name your versions clearly (e.g., ForecastModel_v1.1_Python.xlsx). While I may not be a big fan of manual versioning, using SharePoint or OneDrive with version history enabled makes it much easier to manage changes.

Python code inside Excel isn’t like VBA — it doesn’t have a “macro editor” where you can roll back changes. So, save snapshots as you go.

6. Build for Non-Developers

Your automation isn’t successful when you can run it.
It’s successful when someone else can.

Design your workbooks so others can:

• See inputs clearly (green cells, labeled tables)
• Understand outputs instantly
• Run everything by hitting “Recalculate” — no hidden steps

If your teammate needs a PhD in pandas just to update a tax rate, your automation is a liability, not an asset.

7. Start Small, Scale Fast

Don’t start with a 20-tab consolidation model. Start with one recurring pain point — variance analysis, forecasting, KPI cleanup — and automate that.

Python in Excel can help connect different data sources and workflows, streamlining your processes and making automation more effective.

Get a quick win, show the time savings, then build from there. Momentum builds trust. Trust gets buy-in. Buy-in unlocks bigger automation budgets.