From d328e16a7cac1e1bde810a383e97ebb013d4ec44 Mon Sep 17 00:00:00 2001
From: Davte <davide@davte.it>
Date: Thu, 8 Jan 2026 11:09:46 +0100
Subject: [PATCH] Assign projects to students using linear sum assignment

---
 .gitignore                |   5 ++
 Project assignments.ipynb | 121 ++++++++++++++++++++++++++++++++++++++
 2 files changed, 126 insertions(+)
 create mode 100644 Project assignments.ipynb

diff --git a/.gitignore b/.gitignore
index 0c8bc9a..b61b2f9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,8 @@
+# Data and results
+*.csv
+*.pdf
+*.html
+
 # ---> JupyterNotebooks
 # gitignore template for Jupyter Notebooks
 # website: http://jupyter.org/
diff --git a/Project assignments.ipynb b/Project assignments.ipynb
new file mode 100644
index 0000000..df89acb
--- /dev/null
+++ b/Project assignments.ipynb	
@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "49839009",
+   "metadata": {},
+   "source": [
+    "# Import dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb8a880c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!/usr/bin/env python3\n",
+    "import pandas as pd\n",
+    "from scipy.optimize import linear_sum_assignment\n",
+    "import prettytable as pt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f2e14e2",
+   "metadata": {},
+   "source": [
+    "# Read data\n",
+    "Read the CSV file where I stored Google Form responses. The matrix has been\n",
+    "transposed (rows = projects, columns = people) and the Borda count matrix score\n",
+    "was evaluated subtracting 1 to poll responses (a score of 0 corresponds to the\n",
+    "most desirable assignation)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf13fa73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"form_responses.csv\", index_col=0)\n",
+    "cost = df.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "391fd82c",
+   "metadata": {},
+   "source": [
+    "# Solve the assignment problem using scipy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7efa127e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "row_ind, col_ind = linear_sum_assignment(cost)\n",
+    "assignment = pd.DataFrame({\n",
+    "    \"Project\": df.index[row_ind],\n",
+    "    \"Assignee\": df.columns[col_ind],\n",
+    "    \"Cost\": cost[row_ind, col_ind],\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5e6f5a2",
+   "metadata": {},
+   "source": [
+    "# Show results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4563429",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"These are the best assignments:\")\n",
+    "assignment[[\"Assignee\", \"Cost\", \"Project\"]].reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac221b1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(f\"The total cost of these assignments was {assignment['Cost'].sum()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}