wallin 1.0.3

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 math: SUM, COUNT, AVG, ratios, SUMPRODUCT, scaling, cross-column constraints
• Row-level logic: Conditions like Region = "West" or Profit / Cost > 1.5
• Per-row math: Addition, subtraction, multiplication, division, scaling
• SETVALUE support: Target-based objectives with optional tolerance
• GROUPSUM() support: Enables ratio-based constraints across selected rows
• Handles negative numbers and quoted column names
• Minimal setup: Just define rules and call solve(df, rules)

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Installation

Install from PyPI:

pip install wallin

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How It Works

You provide:

1. A Pandas DataFrame (typically from Excel)
2. A block of plain English rules

Wallin returns the same DataFrame with a Selected column showing which rows were chosen.

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Example

import pandas as pd
import wallin as wl

df = pd.read_excel("Transaction Detail.xlsx")

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

result = wl.solve(df, RULES)
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, works with SETVALUE

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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

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Row-Level Math

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

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Filters and Conditions

CONSTRAINT: Region = "West"
CONSTRAINT: Profit >= 50
CONSTRAINT: Type = "Hardware"

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GROUPSUM() Constraints

CONSTRAINT: Amount / GROUPSUM(Amount) <= 0.3
CONSTRAINT: Ending / GROUPSUM(Ending) = Beginning / GROUPSUM(Beginning)
CONSTRAINT: Sales / GROUPSUM(Sales WHERE Category = "Books") <= 0.4

GROUPSUM(col) computes the sum of a column over selected rows (optionally filtered). This enables proportional, fairness, and relational constraints.

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Output

Wallin appends a boolean Selected column to the DataFrame.

result[result["Selected"]]

This shows the rows the optimizer picked based on your objective and constraints.

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License

MIT License — free for commercial and non-commercial use.