Python for Excel templates

Free technical computing workbooks with Python built in, organized by toolbox categories: curve fitting, statistics, optimization, financial analytics, and practical examples.

Each template is an Excel workbook with scientific Python, sample data, and a live task-pane interface already set up. Open one in Excel, run it on the sample data, then swap in your own data or ask your AI assistant to adapt it. Every template is free to download.

Curve Fitting →

Nonlinear regression, dose-response assays, 4PL curves, and kinetic models.

Statistics →

Method validation, Passing-Bablok, Gage R&R, SPC charts, and hypothesis testing.

Optimization →

LP and MIP solvers powered by SciPy and HiGHS for blending, scheduling, and logistics.

Simulation →

Monte Carlo risk models, Latin Hypercube Sampling, WBS cost risk, and tornado sensitivity.

Financial Analysis →

DCF valuation, bond pricing, dynamic loan amortization, and portfolio risk simulation.

Examples & Utilities →

Data cleaning, fuzzy matching, reshaping, custom worksheet functions, and helpers.

Curve Fitting

Fit nonlinear models to measured experimental data, compare candidate models, and calculate parameter estimates with confidence intervals.

Explore Curve Fitting methods and documentation →

Adsorption isotherm (Langmuir, Freundlich)

Fit nonlinear Langmuir and Freundlich adsorption isotherms to equilibrium batch data, compare models by AICc, and determine capacity and separation factors.

Calibration curve (linear, with unknowns)

Fit a linear instrument calibration curve with optional internal standard ratio, LOD/LOQ estimated with the ICH Q2(R2) approach (3.3 and 10 times the residual standard deviation divided by the slope), and QC recovery for unknown samples.

Curve fitting (any x-y table)

Fit nonlinear and linear models to arbitrary x-y data, evaluate parameter uncertainties and confidence bands, and compare candidate models by AICc.

Degradation kinetics (first-order decay)

Fit single first-order (SFO) degradation kinetics with replicates, half-life DT50, DT90, and log-linear cross-check.

ELISA standard curve (4PL, 5PL)

Fit a 4-parameter or 5-parameter logistic (4PL/5PL) standard curve to ELISA optical densities, evaluate standard recovery and duplicate %CV, and interpolate unknown concentrations.

Enzyme kinetics (Michaelis-Menten)

Fit initial reaction rates to substrate concentrations using the Michaelis-Menten model to estimate Vmax and Km with confidence intervals and Lineweaver-Burk comparison.

IC50 from a dose-response assay

Fit a four-parameter logistic curve to a concentration-indexed replicate series, normalize with plate controls, and report the IC50 with its confidence interval, Hill slope, and validation against published datasets.

Statistics & Quality

Validate analytical methods, evaluate measurement systems, monitor processes with control charts, and run hypothesis tests in Excel.

Explore Statistics methods and documentation →

Control Charts

Shewhart control charts with Western Electric and Nelson run rules, Phase I trial limits, and process capability analysis.

Design of Experiments (DOE)

Two-level full and fractional factorial screening designs, randomized run sheets, alias structure, Pareto and half-normal plots, and Lenth PSE analysis in Excel with Python.

Independent Group Comparison Studio

Compare 2 or more independent groups with Welch-default hypothesis tests, non-parametric rank alternatives, post-hoc procedures, effect sizes, and power analysis in Excel with Python.

Survival Analysis

Kaplan-Meier survival curves, number at risk, log-rank tests, and Cox proportional hazards regression for time-to-event data in Excel with Python.

Optimization

Formulate and solve linear and mixed-integer programs for blending, scheduling, and supply chain allocation using HiGHS and SciPy.

Explore Optimization methods and documentation →

Alloy Blending

Choose which whole steel ingots to buy and how many tonnes of alloy and scrap to blend, so the batch meets its carbon and molybdenum targets at the lowest cost. Edit the Model sheet to test other ingots, prices or targets.

Capital Budgeting Optimization

Maximize total portfolio return by selecting candidate capital investment projects subject to multiple resource and budget constraints.

Cutting Stock Optimization

Cut stock rolls into the ordered widths using the fewest rolls. Enter the roll length and the order on the Model sheet; Python lists the cutting patterns and finds the best mix.

Diet Optimization

Find the cheapest combination of foods that satisfies daily nutrient requirements, framed in physical purchase units of sale.

Feed Ration Optimization

Formulate a least-cost feed blend that meets energy, protein, and amino acid requirements. Edit ingredient costs, nutrient analyses, and diet constraints on the Model sheet.

Fuel Blending Optimization

Optimize refinery gasoline blending by allocating available blend stocks between Regular and Premium products to maximize daily profit while meeting octane, vapor pressure, and benzene limits.

Optimization Starter

Find the most profitable product mix under resource limits and market demand constraints.

Production Planning Optimization

Choose how many boats to build in regular time and in overtime each quarter, and how many to hold in stock, so every quarter's demand is met at the lowest total cost. Change the quarterly data and read the plan, the limits that bind, and the computed value of extra capacity.

Shift Scheduling

Optimize 5-days-on / 2-days-off cyclic shift patterns to meet daily staffing requirements at minimum headcount and payroll cost.

Task Assignment Optimization

Assign each task to one worker at the lowest total cost with each worker taking at most one task.

Transportation Planning

Optimize freight shipments from canning plants to regional distribution centers at minimum total cost within plant capacity and market demand.

Simulation

Model uncertainty with probability distributions, quantify Value at Risk and Expected Shortfall, and evaluate risk drivers with sensitivity tornado charts.

Explore Simulation methods and documentation →

Capital Investment & DCF Risk Simulator

Multi-year capital expenditure and DCF risk analysis using Latin Hypercube Sampling, simulated NPV/IRR, downside tail risk, and tornado sensitivity.

Monte Carlo Profit Risk Simulator

Model business profit uncertainty with Latin Hypercube Sampling (LHS), percentile risk metrics, Value at Risk (VaR), Expected Shortfall, and tornado sensitivity.

Project Cost & Contingency Risk Simulator

Work Breakdown Structure (WBS) cost risk analysis using 3-point Beta-PERT estimates, Latin Hypercube Sampling, and GAO P80 contingency budgeting.

Retirement Wealth & Sequence-of-Returns Simulator

Multi-period retirement wealth decumulation simulation using Latin Hypercube Sampling to model sequence-of-returns risk, portfolio ruin probabilities, and Trinity Study safe withdrawal rates.

Supply Chain Inventory & Safety Stock Simulator

Simulate stochastic demand and supplier replenishment lead times using Latin Hypercube Sampling to optimize safety stock, cycle service levels, and fill rates.

Financial Analytics

Perform DCF valuations, bond pricing, amortization modeling, and simulation-based portfolio risk analysis.

Explore Financial methods and documentation →

Examples & Utilities

Clean, match, reshape, and combine data, and test custom worksheet functions and interactive workbook applications.

Explore all examples and utilities →

Cash Flow Forecast & Liquidity Outlook

Forecast weekly cash receipts, payments, and liquidity in Excel with trend models, scenario assumptions, uncertainty bands, and threshold diagnostics.

Clean Data

Standardize messy customer or transaction exports with automated whitespace, casing, date, number, and mapping rules in Excel.

Combine Files

Stack several monthly exports into one table, with a report of which columns matched, which did not, and which values need a look.

Compare Sheets

Find added, removed and changed rows between two versions of a list, matched by a key column, with the old and new value for each changed cell.

Custom Functions

Write custom business logic in Python and call it directly from worksheet cells with BF.FUNCTION formulas.

Customer Details

Click a customer in your Excel table and see their orders, invoices, tickets and revenue in one place.

Demand & Inventory Planner

Optimize case-pack replenishment orders in Excel with safety stock, demand forecasts, and a shared purchasing budget using SciPy MILP.

Forecast

Forecast the next months from a monthly history, test the method on past months, and draw a range around each forecast from how far those tests were off.

Fuzzy Match

Match messy customer or vendor records against a master reference list with text similarity scoring, review bands, and threshold controls in Excel, plus `fuzzy_match` and `fuzzy_lookup` functions for worksheet formulas.

Intermittent Demand Forecast for Spare Parts

Forecast sparse spare-parts demand in Excel with Croston-style methods, event-aware backtests, empirical lead-time safety stock, and planner model overrides.

Product Classification

Classify products by annual revenue (ABC) and weekly demand variability (XYZ) in Excel, with a Pareto view and a review frequency for each combined class.

Product Details

Click a product to see its stock, demand, open purchase orders and a suggested reorder quantity in Python for Excel.

Regression

Identify store sales drivers and quantify unit effects with multiple linear regression in Python for Excel.

Remove Duplicates

Group rows that name the same company after normalising the name, keep one row per group by a rule you choose, and list the rest for removal.

Reshape Data

Unpivot wide month columns, pivot long rows into a grid, or split delimited lists into columns or rows, and report every row that could not be reshaped cleanly.

Sales Scenario Analysis

Compare baseline and discounted revenue in Excel with pandas using worksheet inputs with a Python for Excel notebook.

Segments

Sort customers into natural groups with k-means clustering in Python for Excel, with segment profiles and a fit-by-number chart.

Text Functions

Pull invoice references, email addresses, phone numbers, numbers and names out of messy free text with one worksheet function per call, using Python regular expressions.