From 32ae109f661ff4d7453aaa36c6a8d6f7e08f2962 Mon Sep 17 00:00:00 2001
From: Oleg Zakharov <ozakharov@gpu-2-2-19.int.aldan3.itm-rsmu.ru>
Date: Fri, 18 Oct 2024 00:08:45 +0300
Subject: [PATCH] v6

---
 Untitled.ipynb | 82 --------------------------------------------------
 1 file changed, 82 deletions(-)
 delete mode 100644 Untitled.ipynb

diff --git a/Untitled.ipynb b/Untitled.ipynb
deleted file mode 100644
index c610fb0..0000000
--- a/Untitled.ipynb
+++ /dev/null
@@ -1,82 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "58e6167e-1e30-4bbf-93a1-af6ab5cb4673",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "\n",
-    "def preprocess_test(test_solutions_path: str, test_tasks_path: str, test_tests_path: str, save_path: str) -> None:\n",
-    "    def read_files(*paths):\n",
-    "        return (pd.read_excel(path) for path in paths)\n",
-    "\n",
-    "    solutions_df, tasks_df, tests_df = read_files(test_solutions_path, test_tasks_path, test_tests_path)\n",
-    "\n",
-    "    test_dataset = pd.merge(pd.merge(solutions_df, tasks_df, left_on='task_id', right_on='id', how='inner'), \n",
-    "                            tests_df, on='task_id', how='inner')\n",
-    "\n",
-    "    test_dataset['input_output'] = test_dataset.apply(\n",
-    "        lambda row: f\"{row['input']}->{row['output']}\" if pd.notna(row['input']) or pd.notna(row['output']) else \"\", \n",
-    "        axis=1\n",
-    "    )\n",
-    "\n",
-    "    test_dataset = test_dataset[['id', 'student_solution', 'description', 'author_solution', 'input_output']]\n",
-    "    test_dataset.to_excel(save_path, index=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "e561a852-007c-4b78-a7e1-697f80272169",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base = 'data/raw/test/{}'\n",
-    "\n",
-    "preprocess_test(base.format('solutions.xlsx'), base.format('tasks.xlsx'), base.format('tests.xlsx'), './test.xlsx')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "2c15dbb7-1034-41a7-96be-69e2418bf98e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "d923d6c4-f962-4646-a041-999862bb3950",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "preprocess_test(base.format('solutions.xlsx'), base.format('tasks.xlsx'), base.format('tests.xlsx'), './test2.xlsx')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:.conda-poetry]",
-   "language": "python",
-   "name": "conda-env-.conda-poetry-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}