wallin 1.0.0

Constraint-based Excel optimizer powered by OR-Tools

Wallin

Wallin is a simple, expressive optimization library that lets you define constraints and objectives in plain English. It’s designed for business analysts, accountants, and operations pros who want to solve real-world allocation problems without having to learn an optimization language.

Built on top of Google OR-Tools, Wallin turns your Excel data and rule logic into a solvable optimization model.

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Features

• Plain English syntax: Write rules like SUM(Cost) <= 1000 or Location = "NY"
• Aggregate constraints: SUM, COUNT, AVG, SUMPRODUCT, ratios, and scaled sums
• Row-level logic: Filter rows with conditions like State = "CA" or Profit / Cost > 1.5
• Support for SETVALUE objectives with tolerance
• Per-row math: Ratios, addition, subtraction, multiplication
• Robust scaling for decimal precision
• No boilerplate: One function call to solve and return selected rows

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Installation

Clone the repo locally or install with:

pip install wallin

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How It Works

Wallin expects two inputs:

1. A Pandas DataFrame (from Excel, CSV, etc.)
2. A multi-line string of rules written in plain English

It returns the same DataFrame with a Selected column marking which rows were chosen by the optimizer.

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Example

import pandas as pd
import wallin as wl

# Load your Excel file
df = pd.read_excel("Transaction Detail.xlsx")

# Define rules
RULES = """
MAXIMIZE: SUM(Profit)
CONSTRAINT: COGS/Profit < 0.06
CONSTRAINT: SUMPRODUCT(COGS, Profit) <= 60000
CONSTRAINT: Location = "NC"
"""

# Solve
result = wl.solve(df, RULES)

# Get selected rows
selected = result[result["Selected"]]
selected.to_excel("selected_rows.xlsx", index=False)

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Supported Syntax

Objectives

MAXIMIZE: SUM(Profit)
MINIMIZE: COUNT(*)
SETVALUE: SUM(Amount) = 100
TOLERANCE: 1.5         # (optional, used with SETVALUE)

Aggregate Constraints

CONSTRAINT: SUM(Cost) <= 1000
CONSTRAINT: COUNT(*) = 5
CONSTRAINT: AVG(Score) > 3.5
CONSTRAINT: SUM(COGS) + SUM(Fees) <= 2000
CONSTRAINT: 2 * SUM(Value) / 3 >= 50
CONSTRAINT: SUM(COGS) / SUM(Profit) <= 0.2
CONSTRAINT: SUM(COGS * Profit) <= 30000
CONSTRAINT: SUMPRODUCT(COGS, Profit) <= 30000

Row-level Math

CONSTRAINT: Profit / Cost >= 1.5
CONSTRAINT: Score + Bonus >= 10
CONSTRAINT: 3 * Cost / 2 <= 500
CONSTRAINT: COGS * Profit < 2000

Row Filters (text/numbers)

CONSTRAINT: Region = "West"
CONSTRAINT: Profit >= 50 AND Profit <= 100
CONSTRAINT: State = "CA" OR State = "NY"

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Output

The output DataFrame contains a Selected column marking which rows were chosen. You can filter it like this:

selected = result[result["Selected"]]

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License

MIT License. Free to use, modify, and distribute.