diff --git a/pomelovsd/DataStruct/data_structures.ipynb b/pomelovsd/DataStruct/data_structures.ipynb
index fac6f8a..5ba5fc8 100644
--- a/pomelovsd/DataStruct/data_structures.ipynb
+++ b/pomelovsd/DataStruct/data_structures.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 106,
    "id": "c533959c",
    "metadata": {},
    "outputs": [],
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 107,
    "id": "88611f78",
    "metadata": {},
    "outputs": [],
@@ -36,7 +36,7 @@
     "sys.setrecursionlimit(10000) \n",
     "records_sorted = [(f\"User_{i:05d}\", f\"+7999{i:07d}\") for i in range(N)] \n",
     "records_shuffled = records_sorted.copy()\n",
-    "rand.shuffle(records_shuffled)"
+    "rand.shuffle(records_shuffled)\n"
    ]
   },
   {
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 108,
    "id": "11634fa4",
    "metadata": {},
    "outputs": [],
@@ -92,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 109,
    "id": "9eab4641",
    "metadata": {},
    "outputs": [],
@@ -108,7 +108,7 @@
     "        data_ll_so.append(head)\n",
     "    end = time.perf_counter()\n",
     "    heads_ll_so.append(head)\n",
-    "    time_ll_insert_so.append(end - start)"
+    "    time_ll_insert_so.append(end - start)\n"
    ]
   },
   {
@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 110,
    "id": "aac6cd23",
    "metadata": {},
    "outputs": [],
@@ -140,7 +140,7 @@
     "        str_find = f\"Node_{m}\"\n",
     "        find_ll_sh.append(ll.ll_find(data_ll_sh[0], str_find))\n",
     "    end = time.perf_counter()\n",
-    "    time_ll_find_sh.append(end - start)"
+    "    time_ll_find_sh.append(end - start)\n"
    ]
   },
   {
@@ -155,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 111,
    "id": "5e5ae537",
    "metadata": {},
    "outputs": [],
@@ -172,7 +172,7 @@
     "        str_find = f\"Node_{m}\"\n",
     "        find_ll_so.append(ll.ll_find(data_ll_sh[0], str_find))\n",
     "    end = time.perf_counter()\n",
-    "    time_ll_find_so.append(end - start)"
+    "    time_ll_find_so.append(end - start)\n"
    ]
   },
   {
@@ -186,10 +186,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 112,
    "id": "6cdf8a70",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.036851685003057355,\n",
+       " 0.03824450399770285,\n",
+       " 0.03843940600199858,\n",
+       " 0.028395101999194594,\n",
+       " 0.034785898998961784]"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "time_ll_delete_sh = []\n",
     "for n in range(5):\n",
@@ -202,7 +217,8 @@
     "        current_head = ll.ll_delete(current_head, str_delete)\n",
     "    end = time.perf_counter()\n",
     "\n",
-    "    time_ll_delete_sh.append(end - start)"
+    "    time_ll_delete_sh.append(end - start)\n",
+    "time_ll_delete_sh"
    ]
   },
   {
@@ -216,10 +232,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 113,
    "id": "575e375c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.04971757499879459,\n",
+       " 0.02925056100139045,\n",
+       " 0.027353375000529923,\n",
+       " 0.05481464200056507,\n",
+       " 0.05585349399916595]"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "time_ll_delete_so = []\n",
     "for n in range(5):\n",
@@ -232,7 +263,8 @@
     "        current_head = ll.ll_delete(current_head, str_delete)\n",
     "    end = time.perf_counter()\n",
     "\n",
-    "    time_ll_delete_sh.append(end - start)"
+    "    time_ll_delete_so.append(end - start)\n",
+    "time_ll_delete_so"
    ]
   },
   {
@@ -256,7 +288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 114,
    "id": "939aa900",
    "metadata": {},
    "outputs": [],
@@ -272,7 +304,7 @@
     "        data_bt_sh.append(head)\n",
     "    end = time.perf_counter()\n",
     "    heads_bt_sh.append(head)\n",
-    "    time_bt_insert_sh.append(end - start)"
+    "    time_bt_insert_sh.append(end - start)\n"
    ]
   },
   {
@@ -288,7 +320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 115,
    "id": "d17b8108",
    "metadata": {},
    "outputs": [],
@@ -303,7 +335,7 @@
     "    data_bt_so.append(head)\n",
     "    end = time.perf_counter()\n",
     "    heads_bt_so.append(head)\n",
-    "    time_bt_insert_so.append(end - start)"
+    "    time_bt_insert_so.append(end - start)\n"
    ]
   },
   {
@@ -318,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 116,
    "id": "4352b11d",
    "metadata": {},
    "outputs": [],
@@ -335,7 +367,7 @@
     "        str_find = f\"Node_{m}\"\n",
     "        find_bt_sh.append(bt.bst_find(data_bt_sh[0], str_find))\n",
     "    end = time.perf_counter()\n",
-    "    time_bt_find_sh.append(end - start)"
+    "    time_bt_find_sh.append(end - start)\n"
    ]
   },
   {
@@ -350,7 +382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 117,
    "id": "7941e689",
    "metadata": {},
    "outputs": [],
@@ -367,7 +399,7 @@
     "        str_find = f\"Node_{m}\"\n",
     "        find_bt_so.append(bt.bst_find(data_bt_so[0], str_find))\n",
     "    end = time.perf_counter()\n",
-    "    time_bt_find_sh.append(end - start)"
+    "    time_bt_find_so.append(end - start)\n"
    ]
   },
   {
@@ -381,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 118,
    "id": "4043a9dc",
    "metadata": {},
    "outputs": [],
@@ -411,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 119,
    "id": "7ab6136c",
    "metadata": {},
    "outputs": [],
@@ -451,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 120,
    "id": "cb1788d1",
    "metadata": {},
    "outputs": [],
@@ -465,7 +497,7 @@
     "        buckets = ht.ht_insert(buckets, records_shuffled[i][0], records_shuffled[i][1])\n",
     "        data_ht_sh.append(buckets)\n",
     "    end = time.perf_counter()\n",
-    "    time_ht_insert_sh.append(end - start)"
+    "    time_ht_insert_sh.append(end - start)\n"
    ]
   },
   {
@@ -481,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 121,
    "id": "4cb524e4",
    "metadata": {},
    "outputs": [],
@@ -495,7 +527,7 @@
     "        buckets = ht.ht_insert(buckets, records_sorted[i][0], records_sorted[i][1])\n",
     "        data_ht_so.append(buckets)\n",
     "    end = time.perf_counter()\n",
-    "    time_ht_insert_so.append(end - start)"
+    "    time_ht_insert_so.append(end - start)\n"
    ]
   },
   {
@@ -510,22 +542,277 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 122,
    "id": "45cec102",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "time_ht_find_sh = []\n",
+    "for n in range(5):\n",
+    "    find_ht_sh = []\n",
+    "    start = time.perf_counter()\n",
+    "    for m in range(100): # замер для 100 случайных узлов \n",
+    "        i = rand.randint(0, N-1)\n",
+    "        str_find = records_shuffled[i][0]\n",
+    "        find_ht_sh.append(ht.ht_find(data_ht_sh[0], str_find))\n",
+    "    for m in range(10): # недоступные данные\n",
+    "        str_find = f\"Node_{m}\"\n",
+    "        find_ht_sh.append(ht.ht_find(data_ht_sh[0], str_find))\n",
+    "    end = time.perf_counter()\n",
+    "    time_ht_find_sh.append(end - start)\n"
+   ]
   },
   {
    "cell_type": "markdown",
    "id": "8f11dbad",
    "metadata": {},
-   "source": []
+   "source": [
+    "### Поиск элементов в отсортированном массиве\n",
+    "- **time_ht_find_so** - Време поиска в сортированном массиве (для 5 замеров)\n",
+    "- **find_ht_so** - массив найденных данных в сортированном массиве (только последний замер)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "id": "19e7a19a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_ht_find_so = []\n",
+    "for n in range(5):\n",
+    "    find_ht_so = []\n",
+    "    start = time.perf_counter()\n",
+    "    for m in range(100): # замер для 100 случайных узлов \n",
+    "        i = rand.randint(0, N-1)\n",
+    "        str_find = records_shuffled[i][0]\n",
+    "        find_ht_so.append(ht.ht_find(data_ht_so[0], str_find))\n",
+    "    for m in range(10): # недоступные данные\n",
+    "        str_find = f\"Node_{m}\"\n",
+    "        find_ht_so.append(ht.ht_find(data_ht_so[0], str_find))\n",
+    "    end = time.perf_counter()\n",
+    "    time_ht_find_so.append(end - start)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e39fa07c",
+   "metadata": {},
+   "source": [
+    "### Удаление элементов в произвольном массиве\n",
+    "- **time_ht_delete_sh** - Време поиска в произвольном массиве (для 5 замеров)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "id": "90a9613d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_ht_delete_sh = []\n",
+    "for n in range(5):\n",
+    "    current_head = data_ht_sh[0]\n",
+    "\n",
+    "    start = time.perf_counter()\n",
+    "    for m in range(50):  \n",
+    "        i = rand.randint(0, N-1)\n",
+    "        str_delete = records_shuffled[i][0]\n",
+    "        current_head = ht.ht_delete(current_head, str_delete)\n",
+    "    end = time.perf_counter()\n",
+    "    time_ht_delete_sh.append(end - start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb9fcf01",
+   "metadata": {},
+   "source": [
+    "### Удаление элементов в сортированном массиве\n",
+    "- **time_ht_delete_so** - Време поиска в произвольном массиве (для 5 замеров)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "id": "dfe01fe5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "time_ht_delete_so = []\n",
+    "for n in range(5):\n",
+    "    current_head = data_ht_so[0]\n",
+    "\n",
+    "    start = time.perf_counter()\n",
+    "    for m in range(50):  \n",
+    "        i = rand.randint(0, N-1)\n",
+    "        str_delete = records_shuffled[i][0]\n",
+    "        current_head = ht.ht_delete(current_head, str_delete)\n",
+    "    end = time.perf_counter()\n",
+    "    time_ht_delete_so.append(end - start)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0ce9bbd",
+   "metadata": {},
+   "source": [
+    "## Создание CSV файла\n",
+    "Запишем все рание полученные данные в CSV файл"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "id": "ce249772",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = [\n",
+    "    [\"Структура\", \"Режим\", \"Операция\", \"Время (сек)\"],\n",
+    "    [\"LinkedList\", \"случайный\", \"вставка\", sum(time_ll_insert_sh)/len(time_ll_insert_sh)],\n",
+    "    [\"LinkedList\", \"сортированный\", \"вставка\", sum(time_ll_insert_so)/len(time_ll_insert_so)],\n",
+    "    [\"LinkedList\", \"случайный\", \"поиск\", sum(time_ll_find_sh)/len(time_ll_find_sh)],\n",
+    "    [\"LinkedList\", \"сортированный\", \"поиск\", sum(time_ll_find_so)/len(time_ll_find_so)],\n",
+    "    [\"LinkedList\", \"случайный\", \"удаление\", sum(time_ll_delete_sh)/len(time_ll_delete_sh)],\n",
+    "    [\"LinkedList\", \"сортированный\", \"удаление\", sum(time_ll_delete_so)/len(time_ll_delete_so)],\n",
+    "    [\"HashTable\", \"случайный\", \"вставка\", sum(time_ht_insert_sh)/len(time_ht_insert_sh)],\n",
+    "    [\"HashTable\", \"сортированный\", \"вставка\", sum(time_ht_insert_so)/len(time_ht_insert_so)],\n",
+    "    [\"HashTable\", \"случайный\", \"поиск\", sum(time_ht_find_sh)/len(time_ht_find_sh)],\n",
+    "    [\"HashTable\", \"сортированный\", \"поиск\", sum(time_ht_find_so)/len(time_ht_find_so)],\n",
+    "    [\"HashTable\", \"случайный\", \"удаление\", sum(time_ht_delete_sh)/len(time_ht_delete_sh)],\n",
+    "    [\"HashTable\", \"сортированный\", \"удаление\", sum(time_ht_delete_so)/len(time_ht_delete_so)],\n",
+    "    [\"BinaryTree\", \"случайный\", \"вставка\", sum(time_bt_insert_sh)/len(time_bt_insert_sh)],\n",
+    "    [\"BinaryTree\", \"сортированный\", \"вставка\", sum(time_bt_insert_so)/len(time_bt_insert_so)],\n",
+    "    [\"BinaryTree\", \"случайный\", \"поиск\", sum(time_bt_find_sh)/len(time_bt_find_sh)],\n",
+    "    [\"BinaryTree\", \"сортированный\", \"поиск\", sum(time_bt_find_so)/len(time_bt_find_so)],\n",
+    "    [\"BinaryTree\", \"случайный\", \"удаление\", sum(time_bt_delete_sh)/len(time_bt_delete_sh)],\n",
+    "    [\"BinaryTree\", \"сортированный\", \"удаление\", sum(time_bt_delete_so)/len(time_bt_delete_so)],\n",
+    "]\n",
+    "\n",
+    "with open(\"results.csv\", \"w\", newline=\"\") as f:\n",
+    "    writer = csv.writer(f)\n",
+    "    writer.writerows(results)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69023ea2",
+   "metadata": {},
+   "source": [
+    "## Построение графиков и сравнение данных между собой\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "d06c4bb0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2000x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "t_ll = [sum(time_ll_insert_sh)/len(time_ll_insert_sh), sum(time_ll_insert_so)/len(time_ll_insert_so), sum(time_ll_find_sh)/len(time_ll_find_sh), sum(time_ll_find_so)/len(time_ll_find_so), sum(time_ll_delete_sh)/len(time_ll_delete_sh), sum(time_ll_delete_so)/len(time_ll_delete_so)]\n",
+    "t_ht = [sum(time_ht_insert_sh)/len(time_ht_insert_sh), sum(time_ht_insert_so)/len(time_ht_insert_so), sum(time_ht_find_sh)/len(time_ht_find_sh), sum(time_ht_find_so)/len(time_ht_find_so), sum(time_ht_delete_sh)/len(time_ht_delete_sh), sum(time_ht_delete_so)/len(time_ht_delete_so)]\n",
+    "t_bt = [sum(time_bt_insert_sh)/len(time_bt_insert_sh), sum(time_bt_insert_so)/len(time_bt_insert_so), sum(time_bt_find_sh)/len(time_bt_find_sh), sum(time_bt_find_so)/len(time_bt_find_so), sum(time_bt_delete_sh)/len(time_bt_delete_sh), sum(time_bt_delete_so)/len(time_bt_delete_so)]\n",
+    "categoris = [\"рандомное добавление\", \"отсортированное добавление\", \"рандомный поиск\", \"отсортированный поиск\", \"рандомное удаление\", \"отсортированное удаление\"]\n",
+    "\n",
+    "plt.figure(\"графики\", figsize = (20,15))\n",
+    "\n",
+    "plt.subplot(3,3,1)\n",
+    "plt.bar(categoris[0], t_ll[0], color = \"orange\")\n",
+    "plt.bar(categoris[1], t_ll[1], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время добавление для LinkedList\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.subplot(3,3,2)\n",
+    "plt.bar(categoris[2], t_ll[2], color = \"orange\")\n",
+    "plt.bar(categoris[3], t_ll[3], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время поиска элементов в LinkedList\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "\n",
+    "plt.subplot(3,3,3)\n",
+    "plt.bar(categoris[4], t_ll[4], color = \"orange\")\n",
+    "plt.bar(categoris[5], t_ll[5], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время удаления элементов в LinkedList\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.subplot(3,3,4)\n",
+    "plt.bar(categoris[0], t_ht[0], color = \"orange\")\n",
+    "plt.bar(categoris[1], t_ht[1], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время добавление для HashTable\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.subplot(3,3,5)\n",
+    "plt.bar(categoris[2], t_ht[2], color = \"orange\")\n",
+    "plt.bar(categoris[3], t_ht[3], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время поиска элементов в HashTable\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "\n",
+    "plt.subplot(3,3,6)\n",
+    "plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
+    "plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время удаления элементов в HashTable\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.subplot(3,3,7)\n",
+    "plt.bar(categoris[0], t_bt[0], color = \"orange\")\n",
+    "plt.bar(categoris[1], t_bt[1], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время добавление для BinaryTree\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.subplot(3,3,8)\n",
+    "plt.bar(categoris[2], t_bt[2], color = \"orange\")\n",
+    "plt.bar(categoris[3], t_bt[3], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время поиска элементов в BinaryTree\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "\n",
+    "plt.subplot(3,3,9)\n",
+    "plt.bar(categoris[4], t_bt[4], color = \"orange\")\n",
+    "plt.bar(categoris[5], t_bt[5], color = \"green\")\n",
+    "\n",
+    "plt.title(f\"Время удаления элементов в BinaryTree\")\n",
+    "\n",
+    "plt.ylabel(\"время работы(секунды)\")\n",
+    "\n",
+    "plt.tight_layout() \n",
+    "plt.show()"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "19e7a19a",
+   "id": "b3dd4c73",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/pomelovsd/DataStruct/results.csv b/pomelovsd/DataStruct/results.csv
new file mode 100644
index 0000000..157399a
--- /dev/null
+++ b/pomelovsd/DataStruct/results.csv
@@ -0,0 +1,19 @@
+Структура,Режим,Операция,Время (сек)
+LinkedList,случайный,вставка,4.791107272799854
+LinkedList,сортированный,вставка,5.242345250000653
+LinkedList,случайный,поиск,0.12513352360037971
+LinkedList,сортированный,поиск,0.0488736079998489
+LinkedList,случайный,удаление,0.035343319200183035
+LinkedList,сортированный,удаление,0.0433979294000892
+HashTable,случайный,вставка,0.07157636960037053
+HashTable,сортированный,вставка,0.022270291600580094
+HashTable,случайный,поиск,0.0005006769999454264
+HashTable,сортированный,поиск,0.0003584531994420104
+HashTable,случайный,удаление,0.00021688099950551988
+HashTable,сортированный,удаление,0.00018475759934517556
+BinaryTree,случайный,вставка,0.04002425920043606
+BinaryTree,сортированный,вставка,0.011209254199638963
+BinaryTree,случайный,поиск,0.0007849900001019705
+BinaryTree,сортированный,поиск,0.0007412777995341457
+BinaryTree,случайный,удаление,0.0002893609998864122
+BinaryTree,сортированный,удаление,0.0005448235999210738
diff --git a/pomelovsd/HashTable.py b/pomelovsd/HashTable.py
deleted file mode 100644
index e69de29..0000000