Excel has long been the go-to tool for financial planning and analysis, but once you try to run massive datasets, build multi-scenario forecasts, or automate repetitive tasks, the cracks start to show.
Formulas get messy, and external scripts slow things down.
That’s why Microsoft’s rollout of Python in Excel in 2023 was such a big deal for FP&A teams. It would give them the ability to analyze larger datasets, model complex scenarios and create advanced visualizations inside Excel.
Since then, Microsoft has made Python in Excel generally available across Excel for Windows, Mac, and Web, expanding accessibility beyond its initial build. It’s also natively integrated with Microsoft Copilot now, letting you describe the analysis you want in plain language while Copilot generates and explains the Python code behind it.
In this article, we’ll cover how to use Python in Excel for FP&A, explore practical use cases and examples, and highlight the Python libraries that make this possible.
Python in Excel allows you to write Python formulas directly in cells, with the code running securely in the Microsoft Cloud and results flowing back into your workbook.
For FP&A professionals, this brings the potential to address long-standing frustrations in reporting cycles, such as the month-end close. ERP and CRM exports, for example, can require hours of manual data cleanup. With Python in Excel, those steps can be automated, freeing time for strategic work like variance analysis and scenario planning.
It also makes complex tasks more practical, such as allocating shared costs like IT, HR, or facilities across business units. Instead of juggling multiple spreadsheets and formulas to apply cost drivers like headcount or square footage, you can automate the entire process and update allocations dynamically as new data comes in.
Plus, you no longer need to juggle external tools like Jupyter Notebooks or Google Colab or waste time moving data back and forth between systems.
Python in Excel also comes with a generally low barrier to entry. Instead of building algorithms from scratch, FP&A teams can tap into pre-built libraries to handle everything from data cleaning and forecasting to scenario testing.
And for FP&A teams without deep coding skills or access to a data science team, the Microsoft Copilot integration automatically generates or explains Python code directly within Excel, helping you build and understand models faster.
Here are six practical ways FP&A teams are already using Python in Excel in their day-to-day operations, based on insights from our team and the wider community:
Translating records from the system they were recorded in to the system they’re stored in—and checking to ensure data from both systems matches—is one of the most tedious parts of finance operations.
Think about reconciling bank transactions with your ERP, where you review hundreds of entries, row by row, only to discover issues like differently typed entries, timing discrepancies, or customer IDs that don’t align.
In Excel, spotting and fixing these mismatches is slow and unreliable. Even with formulas like VLOOKUP or XLOOKUP—or more advanced options like Power Query—the transaction matching process is still clunky, error-prone and time-consuming.
With Python in Excel, you can automate reconciliation in a way that actually scales. For example, using libraries like pandas, you can join and compare your bank feed and ERP exports, flag mismatches instantly, standardize date formats and resolve missing entries. You only need to build the workflow once, then refresh it whenever new data comes in to get a clean, reconciled dataset that’s ready for analysis.
Python in Excel also supports smarter, rule-based matching. You can build logic that reflects real issues finance teams face when reconciling data—such as typos, posting delays, rounding differences, and more—and reuse those rules across multiple datasets.
|
Rule |
Use Case |
|
Fuzzy matching |
Catches entries with minor typos or formatting differences (e.g., “ACME Ltd” vs “ACME Limited”). |
|
Threshold-based matching |
Flags as a match if a set percentage of fields align (e.g., 75%), giving flexibility when exact matches aren’t realistic. |
|
Date range matching |
Treats transactions as matches if dates fall within a certain range, useful when posting times don’t line up exactly. |
|
Amount tolerance |
Considers entries a match if amounts fall within a small deviation (e.g., 5%), accounting for rounding or timing differences. |
|
Customer ID Priority |
Prioritizes customer IDs as the primary field to ensure high accuracy, with other fields checked secondarily. |
|
Hierarchical Matching |
Matches transactions in a set order, starting with key identifiers (like invoice number or ID) before checking secondary fields. |
Once you’ve set up your reconciliation logic, Excel’s new Python Editor makes it even easier to manage and refine your code. It includes syntax highlighting, code completion, and data referencing through the xl() function—all directly in the Formula Bar—so finance teams can edit, test and maintain their automation scripts efficiently.
If you have a range of historical data in Excel, Python lets you forecast with methods that are more accurate, scalable, and easier to update when assumptions change.
Python in Excel supports libraries like statsmodels and scikit-learn, which are widely used for time series forecasting, regression, and machine learning—Microsoft provides access to these libraries through the Anaconda Distribution.
Here’s an overview of some of these libraries and their use cases for forecasting:
|
Library |
Role in Forecasting |
FP&A Use Case Example |
|
statsmodels |
Time series analysis, ARIMA, exponential smoothing, regression |
Build revenue forecasts from historical sales or project demand for upcoming quarters |
|
scikit-learn |
Regression and predictive modeling (machine learning) |
Forecast operating expenses as headcount grows, or estimate costs under different business scenarios to help with expense planning |
|
pandas |
Data preparation and handling time series data |
Clean data exports, align dates, and structure historical datasets before running forecasts |
|
matplotlib / seaborn |
Visualization of forecast results |
Present revenue projections or scenario comparisons with clear charts |
The Python-Excel integration allows you to set up these functions directly within your existing data range in Excel. For example, using the statsmodels library, you can build an ARIMA model to forecast next quarter’s revenue from historical sales trends.
But forecasting isn’t limited to linear models. With Python in Excel, FP&A teams can leverage predictive analytics techniques such as Monte Carlo simulations to evaluate thousands of possible outcomes based on variable inputs.
These simulations help FP&A teams visualize uncertainty—whether it’s around raw material prices, labor costs, or demand shifts—and understand the probability range of possible results so that they can plan for best-, worst-, and most-likely-case outcomes.
At Excelerate Finance 2025, Rishi Grover, Vena Co-Founder and Chief Solutions Architect, showed how FP&A teams can bring these simulations to life using Python in Excel—paired with Vena—to build scenario planning models in Excel, testing assumptions and visualizing results.
Before any forecasting or modeling, finance teams spend a huge amount of time cleaning raw exports from systems such as their ERP, CRM, or payroll systems.
In fact, according to a 2024 FP&A Trends survey, FP&A teams spend about 45% of their time on data collection and validation.
Python in Excel has the potential to automate this kind of work. Using libraries like pandas, you can:
Remove duplicates and outliers
Standardize inconsistent date and currency formats
Fill missing values intelligently using averages or interpolation
Merge multiple data sources into a single clean table for analysis
Because these scripts run right inside Excel, you don’t have to export data to other tools or rebuild formulas each time. Once your workflow is set up, you can just refresh it with new data every month and start analyzing right away.
Anomaly detection helps FP&A teams identify irregularities in financial data, such as errors, timing shifts, or unexpected movements that can throw off month-end reports or variance analysis. It also reveals where performance truly deviates from the norm.
If done manually, financial analysts would have to comb through hundreds of rows in spreadsheets, compare current numbers against historical trends, and calculate percentage changes line by line to spot anything unusual. That’s slow, and it’s easy to miss subtle anomalies this way.
But with Python in Excel, this process becomes much easier. You can use pandas for quick statistical checks or scikit-learn models, such as Isolation Forest, to flag transactions that deviate from past behavior.
For instance, you can detect revenue spikes or expense drops that don’t align with historical patterns, or set up dynamic thresholds that automatically adjust for seasonal peaks like Black Friday for retailers.
Once your models run, you can visualize the results right in Excel using matplotlib or seaborn, making it easy to see which variances are routine and which need a closer look. And because Python supports light data cleanup with machine learning and predictive analytics—like filling in missing values based on historical trends—your team gains more time to spend on meaningful analysis that will guide business decisions.
FP&A teams often need to combine data from completely different systems, such as their HRIS, CRM, ERP, or ops systems to get a complete picture of business performance.
But pairing those datasets in Excel alone is tough.
The integration of Python in Excel allows you to blend and analyze datasets from multiple systems directly inside Excel. This allows you to explore relationships that would have been too complex to uncover with formulas alone.
For example, you could bring in headcount data from your HR system and revenue data from your CRM to explore whether team growth aligns with sales performance. Or analyze support ticket trends from your helpdesk platform alongside departmental size to see where efficiency improves as teams grow.
Native Excel charts work well for simple reporting, but they reach their limits when you need visuals that combine multiple datasets or highlight subtle trends.
With Python in Excel, your options for data visualization open up considerably. For instance, you can create heatmaps to show spending patterns, correlation charts to reveal how cost drivers move together, or time-series plots that track forecast accuracy over time directly in Excel using matplotlib or seaborn.
Because everything runs in your Excel workbook, you can keep using the same filters and pivot tables you rely on for monthly or quarterly reports. And when new data comes in, you can just refresh your Python code to update your visuals, without rebuilding reports from scratch.
Python in Excel gives FP&A professionals more flexibility to analyze, forecast, and visualize data in ways that were once limited to data science tools.
With Vena, you can take full advantage of these innovations without changing how you work. Because Vena is natively integrated with Excel—not a plug-in—you get the full Excel experience supported by a centralized database, real-time collaboration, data synchronization, audit trails and built-in governance.
For example, you can pull live data from your CRM and HR systems into an Excel report with Vena, then use Python in Excel to model revenue drivers or identify anomalies. And when Microsoft releases new features such as Python in Excel or Copilot, you can start using them directly within your Vena environment.
Your models, data, and reports stay connected and secure, while you benefit from everything Excel continues to evolve into—from automation to AI-driven insights—without extra setup or disruption. The result is faster, more reliable analysis that supports better decisions across each planning cycle.