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index 9ba8b4a..9be4449 100644
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diff --git a/stepushovgs/data-structures/source/docs/img/insert.pdf b/stepushovgs/data-structures/source/docs/img/insert.pdf
index 2206279..e1fba2a 100644
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diff --git a/stepushovgs/data-structures/source/docs/img/search.pdf b/stepushovgs/data-structures/source/docs/img/search.pdf
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diff --git a/stepushovgs/data-structures/source/docs/main.ipynb b/stepushovgs/data-structures/source/docs/main.ipynb
deleted file mode 100644
index 2469f83..0000000
--- a/stepushovgs/data-structures/source/docs/main.ipynb
+++ /dev/null
@@ -1,749 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "id": "e631810e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "id": "12fa3ed1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# CMU Serif\n",
-    "plt.rcParams['font.family'] = 'CMU Serif'\n",
-    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
-    "plt.rcParams['font.size'] = 14\n",
-    "plt.rcParams['axes.titlesize'] = 16\n",
-    "plt.rcParams['axes.labelsize'] = 15\n",
-    "plt.rcParams['xtick.labelsize'] = 13\n",
-    "plt.rcParams['ytick.labelsize'] = 13\n",
-    "plt.rcParams['legend.fontsize'] = 12"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "id": "c691c40e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Structure</th>\n",
-       "      <th>Mode</th>\n",
-       "      <th>Operation</th>\n",
-       "      <th>Time</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Вставка</td>\n",
-       "      <td>0.047621</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.003505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Удаление</td>\n",
-       "      <td>0.005502</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Вставка</td>\n",
-       "      <td>0.048707</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Связный список</td>\n",
-       "      <td>Случайный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.003505</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>193</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Поиск</td>\n",
-       "      <td>0.007040</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>194</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Удаление</td>\n",
-       "      <td>0.013611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Вставка (среднее)</td>\n",
-       "      <td>0.303134</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Поиск (среднее)</td>\n",
-       "      <td>0.007562</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>Бинарное дерево поиска</td>\n",
-       "      <td>Отсортированный</td>\n",
-       "      <td>Удаление (среднее)</td>\n",
-       "      <td>0.013591</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>198 rows × 4 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Structure             Mode           Operation      Time\n",
-       "0            Связный список        Случайный             Вставка  0.047621\n",
-       "1            Связный список        Случайный               Поиск  0.003505\n",
-       "2            Связный список        Случайный            Удаление  0.005502\n",
-       "3            Связный список        Случайный             Вставка  0.048707\n",
-       "4            Связный список        Случайный               Поиск  0.003505\n",
-       "..                      ...              ...                 ...       ...\n",
-       "193  Бинарное дерево поиска  Отсортированный               Поиск  0.007040\n",
-       "194  Бинарное дерево поиска  Отсортированный            Удаление  0.013611\n",
-       "195  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.303134\n",
-       "196  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.007562\n",
-       "197  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.013591\n",
-       "\n",
-       "[198 rows x 4 columns]"
-      ]
-     },
-     "execution_count": 226,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "csv_path = \"../results/benchmarks.csv\"\n",
-    "\n",
-    "data = pd.read_csv(csv_path)\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "id": "a3737f45",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.003486), np.float64(0.048099), np.float64(0.006181))"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Получение данных для Связного списка\n",
-    "\n",
-    "ll_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "ll_random_search_average, ll_random_insert_average, ll_random_delete_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "id": "5434d260",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.003512), np.float64(0.048028), np.float64(0.005869))"
-      ]
-     },
-     "execution_count": 228,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Получение данных для Связного списка\n",
-    "\n",
-    "ll_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ll_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ll_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "id": "3deed9a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.0), np.float64(0.002007), np.float64(0.0))"
-      ]
-     },
-     "execution_count": 229,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для хеш таблицы\n",
-    "ht_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_random_delete_average, ht_random_insert_average, ht_random_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "id": "490e5c46",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.00015), np.float64(0.001794), np.float64(5e-05))"
-      ]
-     },
-     "execution_count": 230,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для хеш таблицы\n",
-    "ht_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "ht_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "id": "9d7274ab",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.013959), np.float64(0.301662), np.float64(0.007347))"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для дерева\n",
-    "bst_random_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_search  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_random_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_random_delete_average, bst_random_insert_average, bst_random_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "id": "92a545c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(np.float64(0.013591), np.float64(0.303134), np.float64(0.007562))"
-      ]
-     },
-     "execution_count": 232,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#Получение данных для дерева\n",
-    "bst_sorted_insert  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_insert_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_search  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_search_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_delete  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
-    "        'Time'\n",
-    "    ].tolist()\n",
-    "bst_sorted_delete_average  = data.loc[\n",
-    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
-    "        'Time'\n",
-    "    ].iloc[0]\n",
-    "\n",
-    "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "id": "b2e93d6e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "countUsers           = 10_000\n",
-    "countRepeat          = 10\n",
-    "countRandomSearch    = 200\n",
-    "countNotExitstSearch = 100\n",
-    "countDeletes         = 500\n",
-    "\n",
-    "\n",
-    "ll_col = 'blue'\n",
-    "ht_col = 'orange'\n",
-    "bst_col = 'green'\n",
-    "\n",
-    "iterations = range(countRepeat)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "id": "208784a5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Вставка случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "\n",
-    "# Связный список\n",
-    "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "# Хеш таблица\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "# Дерево\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend(loc='best')\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Вставка отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "# Связный список\n",
-    "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "# Хеш таблица\n",
-    "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "# Дерево\n",
-    "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n",
-    "            linestyle='--', alpha=0.5)\n",
-    "\n",
-    "ax2.legend(loc='best')\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общая настройка\n",
-    "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n",
-    "             fontsize=14)\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('./img/insert.pdf',\n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight',  \n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "id": "7de42c9d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Поиск в случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "# ax1.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Поиск в отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общий заголовок\n",
-    "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('./img/search.pdf', \n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight',  # обрезает лишние поля\n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "id": "0fd42f30",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1400x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Создание двух графиков рядом\n",
-    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
-    "\n",
-    "# ============= Левый график: случайные данные =============\n",
-    "ax1.set_title(\"Удаление в случайных данных\")\n",
-    "ax1.set_ylabel('Время, с')\n",
-    "ax1.set_xlabel('Повторения')\n",
-    "ax1.set_xticks(iterations)\n",
-    "# ax1.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n",
-    "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n",
-    "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n",
-    "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax1.legend()\n",
-    "ax1.grid(True, alpha=0.3)\n",
-    "\n",
-    "# ============= Правый график: отсортированные данные =============\n",
-    "ax2.set_title(\"Удаление в отсортированных данных\")\n",
-    "ax2.set_ylabel('Время, с')\n",
-    "ax2.set_xlabel('Повторения')\n",
-    "ax2.set_xticks(iterations)\n",
-    "# ax2.set_xticklabels(range(1, 6))\n",
-    "\n",
-    "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n",
-    "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n",
-    "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n",
-    "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
-    "\n",
-    "ax2.legend()\n",
-    "ax2.grid(True, alpha=0.3)\n",
-    "\n",
-    "# Общий заголовок\n",
-    "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('./img/delete.pdf', \n",
-    "            format='pdf',\n",
-    "            dpi=300,\n",
-    "            bbox_inches='tight', \n",
-    "            pad_inches=0.1)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9eca6493",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/stepushovgs/data-structures/source/docs/report.md b/stepushovgs/data-structures/source/docs/report.md
new file mode 100644
index 0000000..e69de29
diff --git a/stepushovgs/data-structures/source/docs/src/main.ipynb b/stepushovgs/data-structures/source/docs/src/main.ipynb
new file mode 100644
index 0000000..dbea58c
--- /dev/null
+++ b/stepushovgs/data-structures/source/docs/src/main.ipynb
@@ -0,0 +1,754 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "e631810e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "12fa3ed1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CMU Serif\n",
+    "plt.rcParams['font.family'] = 'CMU Serif'\n",
+    "plt.rcParams['mathtext.fontset'] = 'cm'\n",
+    "plt.rcParams['font.size'] = 14\n",
+    "plt.rcParams['axes.titlesize'] = 16\n",
+    "plt.rcParams['axes.labelsize'] = 15\n",
+    "plt.rcParams['xtick.labelsize'] = 13\n",
+    "plt.rcParams['ytick.labelsize'] = 13\n",
+    "plt.rcParams['legend.fontsize'] = 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "c691c40e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Structure</th>\n",
+       "      <th>Mode</th>\n",
+       "      <th>Operation</th>\n",
+       "      <th>Time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Вставка</td>\n",
+       "      <td>0.047666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.002502</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Удаление</td>\n",
+       "      <td>0.004514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Вставка</td>\n",
+       "      <td>0.048082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Связный список</td>\n",
+       "      <td>Случайный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.003015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>193</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Поиск</td>\n",
+       "      <td>0.007098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>194</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Удаление</td>\n",
+       "      <td>0.026717</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>195</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Вставка (среднее)</td>\n",
+       "      <td>0.317356</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>196</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Поиск (среднее)</td>\n",
+       "      <td>0.007994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>197</th>\n",
+       "      <td>Бинарное дерево поиска</td>\n",
+       "      <td>Отсортированный</td>\n",
+       "      <td>Удаление (среднее)</td>\n",
+       "      <td>0.025775</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>198 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  Structure             Mode           Operation      Time\n",
+       "0            Связный список        Случайный             Вставка  0.047666\n",
+       "1            Связный список        Случайный               Поиск  0.002502\n",
+       "2            Связный список        Случайный            Удаление  0.004514\n",
+       "3            Связный список        Случайный             Вставка  0.048082\n",
+       "4            Связный список        Случайный               Поиск  0.003015\n",
+       "..                      ...              ...                 ...       ...\n",
+       "193  Бинарное дерево поиска  Отсортированный               Поиск  0.007098\n",
+       "194  Бинарное дерево поиска  Отсортированный            Удаление  0.026717\n",
+       "195  Бинарное дерево поиска  Отсортированный   Вставка (среднее)  0.317356\n",
+       "196  Бинарное дерево поиска  Отсортированный     Поиск (среднее)  0.007994\n",
+       "197  Бинарное дерево поиска  Отсортированный  Удаление (среднее)  0.025775\n",
+       "\n",
+       "[198 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "csv_path = \"../../results/benchmarks.csv\"\n",
+    "\n",
+    "data = pd.read_csv(csv_path)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a3737f45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.002936), np.float64(0.047986), np.float64(0.004647))"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получение данных для Связного списка\n",
+    "\n",
+    "ll_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "ll_random_search_average, ll_random_insert_average, ll_random_delete_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "5434d260",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.003821), np.float64(0.048337), np.float64(0.005586))"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Получение данных для Связного списка\n",
+    "\n",
+    "ll_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ll_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ll_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Связный список') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "ll_sorted_search_average, ll_sorted_insert_average, ll_sorted_delete_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3deed9a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.0), np.float64(0.002009), np.float64(0.0))"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для хеш таблицы\n",
+    "ht_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_random_delete_average, ht_random_insert_average, ht_random_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "490e5c46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.00015), np.float64(0.001666), np.float64(0.0))"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для хеш таблицы\n",
+    "ht_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "ht_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Хеш таблица') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "ht_sorted_delete_average, ht_sorted_insert_average, ht_sorted_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "9d7274ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.000101), np.float64(0.002153), np.float64(0.0))"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для дерева\n",
+    "bst_random_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_search  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_random_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Случайный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_random_delete_average, bst_random_insert_average, bst_random_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "92a545c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(0.025775), np.float64(0.317356), np.float64(0.007994))"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Получение данных для дерева\n",
+    "bst_sorted_insert  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_insert_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Вставка (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_search  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_search_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Поиск (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_delete  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление'),\n",
+    "        'Time'\n",
+    "    ].tolist()\n",
+    "bst_sorted_delete_average  = data.loc[\n",
+    "        (data['Structure'] == 'Бинарное дерево поиска') &  (data['Mode'] == 'Отсортированный') & (data['Operation'] == 'Удаление (среднее)'),\n",
+    "        'Time'\n",
+    "    ].iloc[0]\n",
+    "\n",
+    "bst_sorted_delete_average, bst_sorted_insert_average, bst_sorted_search_average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b2e93d6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# countUsers           = 10_000\n",
+    "# countRepeat          = 10\n",
+    "# countRandomSearch    = 200\n",
+    "# countNotExitstSearch = 100\n",
+    "# countDeletes         = 500\n",
+    "\n",
+    "countUsers           = 20_000\n",
+    "countRepeat          = 20\n",
+    "countRandomSearch    = 1000\n",
+    "countNotExitstSearch = 500\n",
+    "countDeletes         = 1000\n",
+    "\n",
+    "ll_col = 'blue'\n",
+    "ht_col = 'orange'\n",
+    "bst_col = 'green'\n",
+    "\n",
+    "iterations = range(countRepeat)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "208784a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Вставка случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "\n",
+    "# Связный список\n",
+    "ax1.scatter(iterations, ll_random_insert, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_insert_average, color=ll_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# Хеш таблица\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_insert, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_insert_average, color=ht_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "# Дерево\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_insert, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_insert_average, color=bst_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend(loc='best')\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Вставка отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "# Связный список\n",
+    "ax2.scatter(iterations, ll_sorted_insert, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_insert_average, color=ll_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Хеш таблица\n",
+    "ax2.scatter(iterations, ht_sorted_insert, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_insert_average, color=ht_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "# Дерево\n",
+    "ax2.scatter(iterations, bst_sorted_insert, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_insert_average, color=bst_col, linewidth=1, \n",
+    "            linestyle='--', alpha=0.5)\n",
+    "\n",
+    "ax2.legend(loc='best')\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общая настройка\n",
+    "plt.suptitle(f'Сравнение производительности вставки в структуры данных (N = {countUsers})', \n",
+    "             fontsize=14)\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/insert.pdf',\n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight',  \n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "7de42c9d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Поиск в случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "# ax1.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax1.scatter(iterations, ll_random_search, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_search, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_search, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Поиск в отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax2.scatter(iterations, ll_sorted_search, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_search_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, ht_sorted_search, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_search_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, bst_sorted_search, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_search_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общий заголовок\n",
+    "plt.suptitle(f'Сравнение времени поиска в структурах данных (N = {countRandomSearch} + {countNotExitstSearch})', fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/search.pdf', \n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight',  # обрезает лишние поля\n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "0fd42f30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Создание двух графиков рядом\n",
+    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
+    "\n",
+    "# ============= Левый график: случайные данные =============\n",
+    "ax1.set_title(\"Удаление в случайных данных\")\n",
+    "ax1.set_ylabel('Время, с')\n",
+    "ax1.set_xlabel('Повторения')\n",
+    "ax1.set_xticks(iterations)\n",
+    "# ax1.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax1.scatter(iterations, ll_random_delete, label='связный список', color=ll_col)\n",
+    "ax1.axhline(y=ll_random_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, ht_random_delete, label='хеш таблица', color=ht_col)\n",
+    "ax1.axhline(y=ht_random_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.scatter(iterations, bst_random_delete, label='дерево', color=bst_col)\n",
+    "ax1.axhline(y=bst_random_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax1.legend()\n",
+    "ax1.grid(True, alpha=0.3)\n",
+    "\n",
+    "# ============= Правый график: отсортированные данные =============\n",
+    "ax2.set_title(\"Удаление в отсортированных данных\")\n",
+    "ax2.set_ylabel('Время, с')\n",
+    "ax2.set_xlabel('Повторения')\n",
+    "ax2.set_xticks(iterations)\n",
+    "# ax2.set_xticklabels(range(1, 6))\n",
+    "\n",
+    "ax2.scatter(iterations, ll_sorted_delete, label='связный список', color=ll_col)\n",
+    "ax2.axhline(y=ll_sorted_delete_average, color=ll_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, ht_sorted_delete, label='хеш таблица', color=ht_col)\n",
+    "ax2.axhline(y=ht_sorted_delete_average, color=ht_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.scatter(iterations, bst_sorted_delete, label='дерево', color=bst_col)\n",
+    "ax2.axhline(y=bst_sorted_delete_average, color=bst_col, linewidth=1, linestyle='--', alpha=0.7)\n",
+    "\n",
+    "ax2.legend()\n",
+    "ax2.grid(True, alpha=0.3)\n",
+    "\n",
+    "# Общий заголовок\n",
+    "plt.suptitle(f'Сравнение времени удаления в структурах данных (N = {countDeletes})', fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('../img/delete.pdf', \n",
+    "            format='pdf',\n",
+    "            dpi=300,\n",
+    "            bbox_inches='tight', \n",
+    "            pad_inches=0.1)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9eca6493",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/stepushovgs/data-structures/source/pkg/data_struct/qsort.go b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go
index 00e0c01..0ae848a 100644
--- a/stepushovgs/data-structures/source/pkg/data_struct/qsort.go
+++ b/stepushovgs/data-structures/source/pkg/data_struct/qsort.go
@@ -1,10 +1,17 @@
 package data_struct
 
 func QSort(arr []MyData, l, r int) []MyData {
+	result := make([]MyData, len(arr))
+	copy(result, arr)
+	qSort(result, l, r)
+	return result
+}
+
+func qSort(arr []MyData, l, r int) []MyData {
 	if l < r {
 		s := Partition_Hoa(arr, l, r)
-		arr = QSort(arr, l, s)
-		arr = QSort(arr, s+1, r)
+		arr = qSort(arr, l, s)
+		arr = qSort(arr, s+1, r)
 	}
 	return arr
 }
diff --git a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go
index f13f1f5..2ebffd5 100644
--- a/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go
+++ b/stepushovgs/data-structures/source/pkg/gen_data/data_generator.go
@@ -29,6 +29,12 @@ func RecordsShuffled(count int) []ds.MyData {
 		data[i].Phone = genRandomPhone()
 	}
 
+	// Перемешиваем (Fisher-Yates shuffle)
+	for i := len(data) - 1; i > 0; i-- {
+		j := rand.Intn(i + 1)
+		data[i], data[j] = data[j], data[i]
+	}
+
 	return data
 }
 
diff --git a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go
index 4291039..4266d3c 100644
--- a/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go
+++ b/stepushovgs/data-structures/source/pkg/structures/bin_search_tree/bin_search_tree.go
@@ -44,6 +44,9 @@ func (bst *BinSearchTree) Minimum() *BSTree {
 }
 
 func (root *BSTree) Minimum() *BSTree {
+	if root == nil {
+		return nil
+	}
 	if root.left == nil {
 		return root
 	}
@@ -54,6 +57,9 @@ func (bst *BinSearchTree) Maximum() *BSTree {
 	return bst.root.Maximum()
 }
 func (root *BSTree) Maximum() *BSTree {
+	if root == nil {
+		return nil
+	}
 	if root.right == nil {
 		return root
 	}
@@ -194,10 +200,39 @@ Node delete(root : Node, z : T):               // корень поддерев
   return root
 */
 
+func (bst *BinSearchTree) Height() int {
+	if bst.root == nil {
+		return 0
+	}
+	return bst.root.height()
+}
+
+// height возвращает высоту поддерева (для BSTree)
+func (node *BSTree) height() int {
+	if node == nil {
+		return 0
+	}
+
+	leftHeight := node.left.height()
+	rightHeight := node.right.height()
+
+	// Высота = 1 (текущий узел) + максимум из высот поддеревьев
+	if leftHeight > rightHeight {
+		return leftHeight + 1
+	}
+	return rightHeight + 1
+}
+
 func (bst *BinSearchTree) Delete(targetName string) bool {
 	if bst.root == nil {
 		return false
 	}
+
+	_, found := bst.Search(targetName)
+	if !found {
+		return false
+	}
+
 	bst.root = bst.root.delete(targetName)
 	return true
 }
diff --git a/stepushovgs/data-structures/source/results/benchmarks.csv b/stepushovgs/data-structures/source/results/benchmarks.csv
index 1e4bbad..dcb6c95 100644
--- a/stepushovgs/data-structures/source/results/benchmarks.csv
+++ b/stepushovgs/data-structures/source/results/benchmarks.csv
@@ -1,199 +1,379 @@
 Structure,Mode,Operation,Time
-Связный список,Случайный,Вставка,0.047621
-Связный список,Случайный,Поиск,0.003505
-Связный список,Случайный,Удаление,0.005502
-Связный список,Случайный,Вставка,0.048707
-Связный список,Случайный,Поиск,0.003505
-Связный список,Случайный,Удаление,0.006007
-Связный список,Случайный,Вставка,0.048086
-Связный список,Случайный,Поиск,0.003546
-Связный список,Случайный,Удаление,0.006606
-Связный список,Случайный,Вставка,0.049133
-Связный список,Случайный,Поиск,0.003018
-Связный список,Случайный,Удаление,0.006084
-Связный список,Случайный,Вставка,0.048328
-Связный список,Случайный,Поиск,0.003524
-Связный список,Случайный,Удаление,0.007066
-Связный список,Случайный,Вставка,0.047057
-Связный список,Случайный,Поиск,0.003518
-Связный список,Случайный,Удаление,0.006054
-Связный список,Случайный,Вставка,0.047979
-Связный список,Случайный,Поиск,0.003604
-Связный список,Случайный,Удаление,0.006039
-Связный список,Случайный,Вставка,0.047694
-Связный список,Случайный,Поиск,0.004520
-Связный список,Случайный,Удаление,0.005522
-Связный список,Случайный,Вставка,0.047864
-Связный список,Случайный,Поиск,0.003514
-Связный список,Случайный,Удаление,0.006814
-Связный список,Случайный,Вставка,0.048523
-Связный список,Случайный,Поиск,0.002611
-Связный список,Случайный,Удаление,0.006122
-Связный список,Случайный,Вставка (среднее),0.048099
-Связный список,Случайный,Поиск (среднее),0.003486
-Связный список,Случайный,Удаление (среднее),0.006181
-Связный список,Отсортированный,Вставка,0.046193
-Связный список,Отсортированный,Поиск,0.004520
-Связный список,Отсортированный,Удаление,0.005769
-Связный список,Отсортированный,Вставка,0.048112
-Связный список,Отсортированный,Поиск,0.003001
-Связный список,Отсортированный,Удаление,0.007026
-Связный список,Отсортированный,Вставка,0.047699
-Связный список,Отсортированный,Поиск,0.003503
-Связный список,Отсортированный,Удаление,0.006515
-Связный список,Отсортированный,Вставка,0.047867
-Связный список,Отсортированный,Поиск,0.003509
-Связный список,Отсортированный,Удаление,0.006001
-Связный список,Отсортированный,Вставка,0.047622
-Связный список,Отсортированный,Поиск,0.003001
-Связный список,Отсортированный,Удаление,0.006008
-Связный список,Отсортированный,Вставка,0.047737
-Связный список,Отсортированный,Поиск,0.003546
-Связный список,Отсортированный,Удаление,0.005514
-Связный список,Отсортированный,Вставка,0.048361
-Связный список,Отсортированный,Поиск,0.003012
-Связный список,Отсортированный,Удаление,0.005328
-Связный список,Отсортированный,Вставка,0.049622
-Связный список,Отсортированный,Поиск,0.003510
-Связный список,Отсортированный,Удаление,0.005522
-Связный список,Отсортированный,Вставка,0.048137
-Связный список,Отсортированный,Поиск,0.003518
-Связный список,Отсортированный,Удаление,0.005531
-Связный список,Отсортированный,Вставка,0.048927
-Связный список,Отсортированный,Поиск,0.004004
-Связный список,Отсортированный,Удаление,0.005470
-Связный список,Отсортированный,Вставка (среднее),0.048028
-Связный список,Отсортированный,Поиск (среднее),0.003512
-Связный список,Отсортированный,Удаление (среднее),0.005869
-Хеш таблица,Случайный,Вставка,0.001504
+Связный список,Случайный,Вставка,0.193393
+Связный список,Случайный,Поиск,0.023572
+Связный список,Случайный,Удаление,0.013566
+Связный список,Случайный,Вставка,0.193837
+Связный список,Случайный,Поиск,0.023969
+Связный список,Случайный,Удаление,0.013580
+Связный список,Случайный,Вставка,0.190963
+Связный список,Случайный,Поиск,0.023944
+Связный список,Случайный,Удаление,0.013505
+Связный список,Случайный,Вставка,0.194440
+Связный список,Случайный,Поиск,0.022179
+Связный список,Случайный,Удаление,0.015040
+Связный список,Случайный,Вставка,0.191873
+Связный список,Случайный,Поиск,0.023604
+Связный список,Случайный,Удаление,0.014556
+Связный список,Случайный,Вставка,0.191788
+Связный список,Случайный,Поиск,0.023570
+Связный список,Случайный,Удаление,0.013037
+Связный список,Случайный,Вставка,0.192362
+Связный список,Случайный,Поиск,0.023050
+Связный список,Случайный,Удаление,0.013997
+Связный список,Случайный,Вставка,0.191223
+Связный список,Случайный,Поиск,0.023778
+Связный список,Случайный,Удаление,0.013582
+Связный список,Случайный,Вставка,0.191326
+Связный список,Случайный,Поиск,0.023035
+Связный список,Случайный,Удаление,0.013183
+Связный список,Случайный,Вставка,0.191919
+Связный список,Случайный,Поиск,0.023584
+Связный список,Случайный,Удаление,0.013988
+Связный список,Случайный,Вставка,0.190009
+Связный список,Случайный,Поиск,0.022013
+Связный список,Случайный,Удаление,0.014679
+Связный список,Случайный,Вставка,0.191010
+Связный список,Случайный,Поиск,0.024724
+Связный список,Случайный,Удаление,0.015116
+Связный список,Случайный,Вставка,0.192876
+Связный список,Случайный,Поиск,0.023690
+Связный список,Случайный,Удаление,0.021114
+Связный список,Случайный,Вставка,0.202786
+Связный список,Случайный,Поиск,0.023466
+Связный список,Случайный,Удаление,0.014063
+Связный список,Случайный,Вставка,0.191339
+Связный список,Случайный,Поиск,0.022909
+Связный список,Случайный,Удаление,0.014661
+Связный список,Случайный,Вставка,0.189898
+Связный список,Случайный,Поиск,0.023056
+Связный список,Случайный,Удаление,0.013592
+Связный список,Случайный,Вставка,0.195372
+Связный список,Случайный,Поиск,0.023198
+Связный список,Случайный,Удаление,0.013819
+Связный список,Случайный,Вставка,0.191713
+Связный список,Случайный,Поиск,0.023542
+Связный список,Случайный,Удаление,0.012883
+Связный список,Случайный,Вставка,0.190514
+Связный список,Случайный,Поиск,0.023745
+Связный список,Случайный,Удаление,0.015012
+Связный список,Случайный,Вставка,0.189447
+Связный список,Случайный,Поиск,0.023544
+Связный список,Случайный,Удаление,0.014063
+Связный список,Случайный,Вставка (среднее),0.192404
+Связный список,Случайный,Поиск (среднее),0.023409
+Связный список,Случайный,Удаление (среднее),0.014352
+Связный список,Отсортированный,Вставка,0.193078
+Связный список,Отсортированный,Поиск,0.033527
+Связный список,Отсортированный,Удаление,0.022597
+Связный список,Отсортированный,Вставка,0.190167
+Связный список,Отсортированный,Поиск,0.032657
+Связный список,Отсортированный,Удаление,0.022773
+Связный список,Отсортированный,Вставка,0.191660
+Связный список,Отсортированный,Поиск,0.034012
+Связный список,Отсортированный,Удаление,0.022709
+Связный список,Отсортированный,Вставка,0.191970
+Связный список,Отсортированный,Поиск,0.032553
+Связный список,Отсортированный,Удаление,0.022828
+Связный список,Отсортированный,Вставка,0.194583
+Связный список,Отсортированный,Поиск,0.034762
+Связный список,Отсортированный,Удаление,0.022843
+Связный список,Отсортированный,Вставка,0.192641
+Связный список,Отсортированный,Поиск,0.033173
+Связный список,Отсортированный,Удаление,0.022690
+Связный список,Отсортированный,Вставка,0.194789
+Связный список,Отсортированный,Поиск,0.033526
+Связный список,Отсортированный,Удаление,0.022985
+Связный список,Отсортированный,Вставка,0.191488
+Связный список,Отсортированный,Поиск,0.032518
+Связный список,Отсортированный,Удаление,0.023373
+Связный список,Отсортированный,Вставка,0.203929
+Связный список,Отсортированный,Поиск,0.034133
+Связный список,Отсортированный,Удаление,0.023423
+Связный список,Отсортированный,Вставка,0.193756
+Связный список,Отсортированный,Поиск,0.033023
+Связный список,Отсортированный,Удаление,0.022845
+Связный список,Отсортированный,Вставка,0.195554
+Связный список,Отсортированный,Поиск,0.032817
+Связный список,Отсортированный,Удаление,0.023482
+Связный список,Отсортированный,Вставка,0.193150
+Связный список,Отсортированный,Поиск,0.033271
+Связный список,Отсортированный,Удаление,0.022587
+Связный список,Отсортированный,Вставка,0.192696
+Связный список,Отсортированный,Поиск,0.033454
+Связный список,Отсортированный,Удаление,0.022527
+Связный список,Отсортированный,Вставка,0.192065
+Связный список,Отсортированный,Поиск,0.033382
+Связный список,Отсортированный,Удаление,0.022786
+Связный список,Отсортированный,Вставка,0.191702
+Связный список,Отсортированный,Поиск,0.031664
+Связный список,Отсортированный,Удаление,0.023819
+Связный список,Отсортированный,Вставка,0.194042
+Связный список,Отсортированный,Поиск,0.032380
+Связный список,Отсортированный,Удаление,0.023181
+Связный список,Отсортированный,Вставка,0.190397
+Связный список,Отсортированный,Поиск,0.033168
+Связный список,Отсортированный,Удаление,0.022893
+Связный список,Отсортированный,Вставка,0.191636
+Связный список,Отсортированный,Поиск,0.032367
+Связный список,Отсортированный,Удаление,0.022448
+Связный список,Отсортированный,Вставка,0.193131
+Связный список,Отсортированный,Поиск,0.032905
+Связный список,Отсортированный,Удаление,0.022932
+Связный список,Отсортированный,Вставка,0.191676
+Связный список,Отсортированный,Поиск,0.032851
+Связный список,Отсортированный,Удаление,0.022315
+Связный список,Отсортированный,Вставка (среднее),0.193206
+Связный список,Отсортированный,Поиск (среднее),0.033107
+Связный список,Отсортированный,Удаление (среднее),0.022902
+Хеш таблица,Случайный,Вставка,0.002119
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002047
+Хеш таблица,Случайный,Вставка,0.003507
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002010
+Хеш таблица,Случайный,Вставка,0.002563
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002005
+Хеш таблица,Случайный,Вставка,0.003023
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002513
+Хеш таблица,Случайный,Вставка,0.003513
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.001505
+Хеш таблица,Случайный,Вставка,0.002516
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002529
+Хеш таблица,Случайный,Вставка,0.003369
+Хеш таблица,Случайный,Поиск,0.001002
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.002091
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.001504
+Хеш таблица,Случайный,Вставка,0.003654
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002023
+Хеш таблица,Случайный,Вставка,0.003102
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка,0.002429
+Хеш таблица,Случайный,Вставка,0.003008
 Хеш таблица,Случайный,Поиск,0.000000
 Хеш таблица,Случайный,Удаление,0.000000
-Хеш таблица,Случайный,Вставка (среднее),0.002007
-Хеш таблица,Случайный,Поиск (среднее),0.000000
+Хеш таблица,Случайный,Вставка,0.003997
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.001580
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.005478
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.002015
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.003410
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.002158
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.003586
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.003123
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка,0.003011
+Хеш таблица,Случайный,Поиск,0.000000
+Хеш таблица,Случайный,Удаление,0.000000
+Хеш таблица,Случайный,Вставка (среднее),0.003041
+Хеш таблица,Случайный,Поиск (среднее),0.000050
 Хеш таблица,Случайный,Удаление (среднее),0.000000
-Хеш таблица,Отсортированный,Вставка,0.002005
+Хеш таблица,Отсортированный,Вставка,0.003208
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.001282
-Хеш таблица,Отсортированный,Поиск,0.000000
-Хеш таблица,Отсортированный,Удаление,0.001504
-Хеш таблица,Отсортированный,Вставка,0.001015
+Хеш таблица,Отсортированный,Вставка,0.003095
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.002016
+Хеш таблица,Отсортированный,Вставка,0.002048
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.001505
+Хеш таблица,Отсортированный,Вставка,0.003991
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.002016
-Хеш таблица,Отсортированный,Поиск,0.000502
-Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.002009
+Хеш таблица,Отсортированный,Вставка,0.002006
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.001068
+Хеш таблица,Отсортированный,Вставка,0.003505
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.002516
+Хеш таблица,Отсортированный,Вставка,0.003013
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка,0.002505
+Хеш таблица,Отсортированный,Вставка,0.002423
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.001102
+Хеш таблица,Отсортированный,Вставка,0.003121
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000503
+Хеш таблица,Отсортированный,Вставка,0.002511
 Хеш таблица,Отсортированный,Поиск,0.000000
 Хеш таблица,Отсортированный,Удаление,0.000000
-Хеш таблица,Отсортированный,Вставка (среднее),0.001794
-Хеш таблица,Отсортированный,Поиск (среднее),0.000050
-Хеш таблица,Отсортированный,Удаление (среднее),0.000150
-Бинарное дерево поиска,Случайный,Вставка,0.304277
-Бинарное дерево поиска,Случайный,Поиск,0.007550
-Бинарное дерево поиска,Случайный,Удаление,0.013564
-Бинарное дерево поиска,Случайный,Вставка,0.297918
-Бинарное дерево поиска,Случайный,Поиск,0.007167
-Бинарное дерево поиска,Случайный,Удаление,0.014539
-Бинарное дерево поиска,Случайный,Вставка,0.300777
-Бинарное дерево поиска,Случайный,Поиск,0.008009
-Бинарное дерево поиска,Случайный,Удаление,0.015144
-Бинарное дерево поиска,Случайный,Вставка,0.307342
-Бинарное дерево поиска,Случайный,Поиск,0.008307
-Бинарное дерево поиска,Случайный,Удаление,0.013577
-Бинарное дерево поиска,Случайный,Вставка,0.301149
-Бинарное дерево поиска,Случайный,Поиск,0.007792
-Бинарное дерево поиска,Случайный,Удаление,0.012897
-Бинарное дерево поиска,Случайный,Вставка,0.304480
-Бинарное дерево поиска,Случайный,Поиск,0.006521
-Бинарное дерево поиска,Случайный,Удаление,0.014053
-Бинарное дерево поиска,Случайный,Вставка,0.299029
-Бинарное дерево поиска,Случайный,Поиск,0.007552
-Бинарное дерево поиска,Случайный,Удаление,0.013073
-Бинарное дерево поиска,Случайный,Вставка,0.304265
-Бинарное дерево поиска,Случайный,Поиск,0.006518
-Бинарное дерево поиска,Случайный,Удаление,0.014555
-Бинарное дерево поиска,Случайный,Вставка,0.297193
-Бинарное дерево поиска,Случайный,Поиск,0.007529
-Бинарное дерево поиска,Случайный,Удаление,0.014092
-Бинарное дерево поиска,Случайный,Вставка,0.300190
-Бинарное дерево поиска,Случайный,Поиск,0.006525
-Бинарное дерево поиска,Случайный,Удаление,0.014096
-Бинарное дерево поиска,Случайный,Вставка (среднее),0.301662
-Бинарное дерево поиска,Случайный,Поиск (среднее),0.007347
-Бинарное дерево поиска,Случайный,Удаление (среднее),0.013959
-Бинарное дерево поиска,Отсортированный,Вставка,0.317393
-Бинарное дерево поиска,Отсортированный,Поиск,0.007554
-Бинарное дерево поиска,Отсортированный,Удаление,0.013053
-Бинарное дерево поиска,Отсортированный,Вставка,0.301527
-Бинарное дерево поиска,Отсортированный,Поиск,0.006503
-Бинарное дерево поиска,Отсортированный,Удаление,0.014265
-Бинарное дерево поиска,Отсортированный,Вставка,0.300625
-Бинарное дерево поиска,Отсортированный,Поиск,0.008043
-Бинарное дерево поиска,Отсортированный,Удаление,0.013068
-Бинарное дерево поиска,Отсортированный,Вставка,0.303297
-Бинарное дерево поиска,Отсортированный,Поиск,0.007039
-Бинарное дерево поиска,Отсортированный,Удаление,0.013601
-Бинарное дерево поиска,Отсортированный,Вставка,0.300072
-Бинарное дерево поиска,Отсортированный,Поиск,0.008539
-Бинарное дерево поиска,Отсортированный,Удаление,0.013535
-Бинарное дерево поиска,Отсортированный,Вставка,0.298420
-Бинарное дерево поиска,Отсортированный,Поиск,0.007256
-Бинарное дерево поиска,Отсортированный,Удаление,0.013550
-Бинарное дерево поиска,Отсортированный,Вставка,0.298652
-Бинарное дерево поиска,Отсортированный,Поиск,0.008040
-Бинарное дерево поиска,Отсортированный,Удаление,0.013532
-Бинарное дерево поиска,Отсортированный,Вставка,0.299859
-Бинарное дерево поиска,Отсортированный,Поиск,0.007577
-Бинарное дерево поиска,Отсортированный,Удаление,0.013575
-Бинарное дерево поиска,Отсортированный,Вставка,0.307201
-Бинарное дерево поиска,Отсортированный,Поиск,0.008025
-Бинарное дерево поиска,Отсортированный,Удаление,0.014125
-Бинарное дерево поиска,Отсортированный,Вставка,0.304290
-Бинарное дерево поиска,Отсортированный,Поиск,0.007040
-Бинарное дерево поиска,Отсортированный,Удаление,0.013611
-Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.303134
-Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.007562
-Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.013591
+Хеш таблица,Отсортированный,Вставка,0.003011
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.002003
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.001341
+Хеш таблица,Отсортированный,Вставка,0.001513
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.004485
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.002740
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.002310
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.002506
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.002515
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.003073
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка,0.004169
+Хеш таблица,Отсортированный,Поиск,0.000000
+Хеш таблица,Отсортированный,Удаление,0.000000
+Хеш таблица,Отсортированный,Вставка (среднее),0.002862
+Хеш таблица,Отсортированный,Поиск (среднее),0.000000
+Хеш таблица,Отсортированный,Удаление (среднее),0.000147
+Бинарное дерево поиска,Случайный,Вставка,0.004626
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001014
+Бинарное дерево поиска,Случайный,Вставка,0.004910
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.005058
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001009
+Бинарное дерево поиска,Случайный,Вставка,0.004521
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.005904
+Бинарное дерево поиска,Случайный,Поиск,0.001025
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.005221
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001066
+Бинарное дерево поиска,Случайный,Вставка,0.005843
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.007343
+Бинарное дерево поиска,Случайный,Поиск,0.000503
+Бинарное дерево поиска,Случайный,Удаление,0.000516
+Бинарное дерево поиска,Случайный,Вставка,0.006171
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000512
+Бинарное дерево поиска,Случайный,Вставка,0.005454
+Бинарное дерево поиска,Случайный,Поиск,0.001009
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.006885
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001451
+Бинарное дерево поиска,Случайный,Вставка,0.006402
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001009
+Бинарное дерево поиска,Случайный,Вставка,0.004521
+Бинарное дерево поиска,Случайный,Поиск,0.001055
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.005170
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001503
+Бинарное дерево поиска,Случайный,Вставка,0.005615
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.001001
+Бинарное дерево поиска,Случайный,Вставка,0.005535
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.005566
+Бинарное дерево поиска,Случайный,Поиск,0.001004
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.004022
+Бинарное дерево поиска,Случайный,Поиск,0.001010
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.004053
+Бинарное дерево поиска,Случайный,Поиск,0.001079
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка,0.004512
+Бинарное дерево поиска,Случайный,Поиск,0.000000
+Бинарное дерево поиска,Случайный,Удаление,0.000000
+Бинарное дерево поиска,Случайный,Вставка (среднее),0.005367
+Бинарное дерево поиска,Случайный,Поиск (среднее),0.000334
+Бинарное дерево поиска,Случайный,Удаление (среднее),0.000454
+Бинарное дерево поиска,Отсортированный,Вставка,0.901140
+Бинарное дерево поиска,Отсортированный,Поиск,0.055198
+Бинарное дерево поиска,Отсортированный,Удаление,0.068970
+Бинарное дерево поиска,Отсортированный,Вставка,0.901221
+Бинарное дерево поиска,Отсортированный,Поиск,0.055432
+Бинарное дерево поиска,Отсортированный,Удаление,0.067588
+Бинарное дерево поиска,Отсортированный,Вставка,0.875497
+Бинарное дерево поиска,Отсортированный,Поиск,0.055297
+Бинарное дерево поиска,Отсортированный,Удаление,0.068742
+Бинарное дерево поиска,Отсортированный,Вставка,0.894420
+Бинарное дерево поиска,Отсортированный,Поиск,0.054808
+Бинарное дерево поиска,Отсортированный,Удаление,0.068112
+Бинарное дерево поиска,Отсортированный,Вставка,0.879010
+Бинарное дерево поиска,Отсортированный,Поиск,0.054503
+Бинарное дерево поиска,Отсортированный,Удаление,0.068600
+Бинарное дерево поиска,Отсортированный,Вставка,0.873702
+Бинарное дерево поиска,Отсортированный,Поиск,0.055239
+Бинарное дерево поиска,Отсортированный,Удаление,0.067484
+Бинарное дерево поиска,Отсортированный,Вставка,0.889047
+Бинарное дерево поиска,Отсортированный,Поиск,0.066586
+Бинарное дерево поиска,Отсортированный,Удаление,0.071171
+Бинарное дерево поиска,Отсортированный,Вставка,0.881977
+Бинарное дерево поиска,Отсортированный,Поиск,0.059766
+Бинарное дерево поиска,Отсортированный,Удаление,0.069783
+Бинарное дерево поиска,Отсортированный,Вставка,0.890244
+Бинарное дерево поиска,Отсортированный,Поиск,0.054863
+Бинарное дерево поиска,Отсортированный,Удаление,0.069397
+Бинарное дерево поиска,Отсортированный,Вставка,0.871035
+Бинарное дерево поиска,Отсортированный,Поиск,0.055970
+Бинарное дерево поиска,Отсортированный,Удаление,0.065886
+Бинарное дерево поиска,Отсортированный,Вставка,0.873882
+Бинарное дерево поиска,Отсортированный,Поиск,0.056181
+Бинарное дерево поиска,Отсортированный,Удаление,0.069140
+Бинарное дерево поиска,Отсортированный,Вставка,0.932258
+Бинарное дерево поиска,Отсортированный,Поиск,0.055037
+Бинарное дерево поиска,Отсортированный,Удаление,0.066576
+Бинарное дерево поиска,Отсортированный,Вставка,0.875622
+Бинарное дерево поиска,Отсортированный,Поиск,0.055679
+Бинарное дерево поиска,Отсортированный,Удаление,0.068214
+Бинарное дерево поиска,Отсортированный,Вставка,0.871718
+Бинарное дерево поиска,Отсортированный,Поиск,0.055748
+Бинарное дерево поиска,Отсортированный,Удаление,0.067876
+Бинарное дерево поиска,Отсортированный,Вставка,0.872458
+Бинарное дерево поиска,Отсортированный,Поиск,0.054689
+Бинарное дерево поиска,Отсортированный,Удаление,0.068765
+Бинарное дерево поиска,Отсортированный,Вставка,0.871178
+Бинарное дерево поиска,Отсортированный,Поиск,0.054816
+Бинарное дерево поиска,Отсортированный,Удаление,0.067832
+Бинарное дерево поиска,Отсортированный,Вставка,0.876907
+Бинарное дерево поиска,Отсортированный,Поиск,0.054184
+Бинарное дерево поиска,Отсортированный,Удаление,0.068164
+Бинарное дерево поиска,Отсортированный,Вставка,0.868001
+Бинарное дерево поиска,Отсортированный,Поиск,0.054167
+Бинарное дерево поиска,Отсортированный,Удаление,0.068493
+Бинарное дерево поиска,Отсортированный,Вставка,0.879394
+Бинарное дерево поиска,Отсортированный,Поиск,0.058173
+Бинарное дерево поиска,Отсортированный,Удаление,0.068654
+Бинарное дерево поиска,Отсортированный,Вставка,0.868870
+Бинарное дерево поиска,Отсортированный,Поиск,0.055234
+Бинарное дерево поиска,Отсортированный,Удаление,0.068269
+Бинарное дерево поиска,Отсортированный,Вставка (среднее),0.882379
+Бинарное дерево поиска,Отсортированный,Поиск (среднее),0.056078
+Бинарное дерево поиска,Отсортированный,Удаление (среднее),0.068386
diff --git a/stepushovgs/data-structures/source/tests/benchmark/main.go b/stepushovgs/data-structures/source/tests/benchmark/main.go
index 36bbd71..b2e804e 100644
--- a/stepushovgs/data-structures/source/tests/benchmark/main.go
+++ b/stepushovgs/data-structures/source/tests/benchmark/main.go
@@ -16,11 +16,11 @@ import (
 )
 
 const (
-	countUsers           = 10_000
-	countRepeat          = 10
-	countRandomSearch    = 200
-	countNotExitstSearch = 100
-	countDeletes         = 500
+	countUsers           = 20_000
+	countRepeat          = 20
+	countRandomSearch    = 1000
+	countNotExitstSearch = 500
+	countDeletes         = 1000
 )
 
 type TestData struct {
@@ -56,8 +56,9 @@ func NewBinSearchTree() DataStructure {
 
 func uniqueElements(data []ds.MyData) []ds.MyData {
 	res := make([]ds.MyData, 0, len(data))
-	isUnique := true
+
 	for _, el := range data {
+		isUnique := true
 		for _, resEl := range res {
 			if el == resEl {
 				isUnique = false
@@ -74,6 +75,7 @@ func uniqueElements(data []ds.MyData) []ds.MyData {
 
 func GenerateTestData() TestData {
 	items := dg.RecordsShuffled(countUsers)
+	// fmt.Println("isSorted:", isSorted(items))
 	itemsSort := ds.QSort(items, 0, len(items)-1)
 
 	uniqueItems := uniqueElements(items)
@@ -174,6 +176,14 @@ func testForData(nameStruct, mode string, factory StructureFactory, data_insert,
 		insertTime := testOnesInsert(structure, data_insert)
 		averageTimeInsert += insertTime
 
+		// Отладочная информация для бинарного дерева (проверка на вырождение)
+		if bst, ok := structure.(*bst.BinSearchTree); ok {
+			fmt.Printf(
+				"Высота дерева: %d, элементов: %d\n",
+				bst.Height(), bst.Len(),
+			)
+		}
+
 		BenchRes = append(BenchRes, csvwriter.BenchmarkResult{
 			Structure: nameStruct,
 			Mode:      mode,
@@ -235,9 +245,21 @@ func testForData(nameStruct, mode string, factory StructureFactory, data_insert,
 	csvwriter.AppendRaw(BenchRes)
 }
 
+func isSorted(data []ds.MyData) bool {
+	for i := 0; i < len(data)-1; i++ {
+		if data[i].Name > data[i+1].Name {
+			return false
+		}
+	}
+	return true
+}
+
 func Test(nameStruct string, factory StructureFactory) {
 	data := GenerateTestData()
 
+	// fmt.Println("items", isSorted(data.Items))
+	// fmt.Println("items sort", isSorted(data.ItemsSorted))
+
 	testForData(nameStruct, "Случайный", factory, data.Items, data.Search, data.ToDelete)
 
 	testForData(nameStruct, "Отсортированный", factory, data.ItemsSorted, data.Search, data.ToDelete)
@@ -253,4 +275,7 @@ func main() {
 	Test("Связный список", NewLinkedList)
 	Test("Хеш таблица", NewHashTable)
 	Test("Бинарное дерево поиска", NewBinSearchTree)
+
+	// fmt.Println("User_0001" < "User_00100")
+	// fmt.Println(isSorted(dg.RecordsShuffled(10000)))
 }