diff --git a/clean_html.py b/clean_html.py
new file mode 100644
index 0000000..2b687d1
--- /dev/null
+++ b/clean_html.py
@@ -0,0 +1,30 @@
+import re
+import html
+import sqlite3
+
+con = sqlite3.connect('works_db')
+cur = con.cursor()
+
+# очистка полей от html
+info_by_id = cur.execute("SELECT ID, skills, otherInfo FROM works;")
+
+def clean_html(string):
+	# https://stackoverflow.com/questions/753052/strip-html-from-strings-in-python
+    tagless = re.sub('<[^<]+?>', '', string)
+    return html.unescape(tagless).replace('\xa0', '')
+
+cleaned_info = {}
+for row in info_by_id:
+    cl_skills = clean_html(row[1])
+    cl_other = clean_html(row[2])
+    cleaned_info[row[0]] = (cl_skills, cl_other)
+
+stmt = "UPDATE works SET skills = ?, otherInfo = ? WHERE ID = ?"
+for k, v in cleaned_info.items():
+    skills = v[0]
+    other = v[1]
+    cur.execute(stmt, [skills, other, k])
+
+con.commit()
+cur.close()
+con.close()
\ No newline at end of file
diff --git a/sql_assignment.ipynb b/sql_assignment.ipynb
new file mode 100644
index 0000000..f82371f
--- /dev/null
+++ b/sql_assignment.ipynb
@@ -0,0 +1,443 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "5509c5f3-8d9c-4b2a-8f50-8b09195c2fa3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import csv\n",
+    "import os\n",
+    "import numpy as np\n",
+    "import matplotlib\n",
+    "from matplotlib import pyplot as plt\n",
+    "from matplotlib.pyplot import figure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5279f0b9-90b9-4188-93a6-b014857e21a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con = sqlite3.connect('works_db')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e3d57e36-20fd-4050-85e9-be1a56f1e10d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cur = con.cursor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e417839b-465d-4153-b2f5-a2369796bd95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cur.execute(\"\"\"CREATE TABLE works(\n",
+    "    \"ID\" INTEGER PRIMARY KEY AUTOINCREMENT,\n",
+    "    \"salary\" INTEGER,\n",
+    "    \"educationType\" TEXT,\n",
+    "    \"jobTitle\" TEXT,\n",
+    "    \"qualification\" TEXT,\n",
+    "    \"gender\" TEXT,\n",
+    "    \"dateModify\" TEXT,\n",
+    "    \"skills\" TEXT,\n",
+    "    \"otherInfo\" TEXT\n",
+    ");\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9cb9535-447f-43c3-90db-89ad4e77cc0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# https://stackoverflow.com/questions/2887878/importing-a-csv-file-into-a-sqlite3-database-table-using-python\n",
+    "to_db = None\n",
+    "with open('works.csv', newline='', encoding='utf-8') as csv_source:\n",
+    "    reader = csv.DictReader(csv_source)\n",
+    "    to_db = [(r['salary'], r['educationType'], r['jobTitle'], r['qualification'], r['gender'], r['dateModify'], r['skills'], r['otherInfo']) \n",
+    "             for r in reader]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "339ba4dd-b383-4dc0-9a68-567f8201e757",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stmt = '''INSERT INTO works (salary, educationType, jobTitle, qualification, gender, dateModify, skills, otherInfo) \n",
+    "        VALUES (?, ?, ?, ?, ?, ?, ?, ?);'''\n",
+    "cur.executemany(stmt, to_db)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "f780ffeb-9c8e-45c9-ab14-97ebcc2a29de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "con.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "551f725d-2f73-40e1-8ddf-740d02d62f31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2191360"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# объем файла до добавления индекса\n",
+    "print(\"Обьем в байтах без индекса: \", os.path.getsize('works_db'))# 2191360 байт"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "db07a949-2955-47c3-bcb3-336bae286bda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2310144\n"
+     ]
+    }
+   ],
+   "source": [
+    "cur.execute(\"CREATE INDEX salary_idx ON works (salary);\")\n",
+    "# объем файла после добавления индекса\n",
+    "print(\"Обьем в байтах с индексом: \", os.path.getsize('works_db'))# 2310144 байт"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "07e1478b-15e6-4af3-8fcc-7f07183fe276",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество строк:  11063\n"
+     ]
+    }
+   ],
+   "source": [
+    "# количество записей\n",
+    "total_count = 0\n",
+    "for res in cur.execute(\"SELECT COUNT(*) FROM works;\"):\n",
+    "    total_count = res[0]\n",
+    "print(\"Количество строк: \", total_count) # 11063"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "82a11a44-3e43-4920-bb1e-0eb162c02b9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Количество женщин:  5996\n",
+      "Количество мужчин:  4588\n",
+      "Пол не указан:  479\n"
+     ]
+    }
+   ],
+   "source": [
+    "# количество женщин и мужчин\n",
+    "women_count = 0\n",
+    "for res in cur.execute(\"SELECT COUNT(gender) FROM works WHERE gender='Женский';\"):\n",
+    "    women_count = res[0]\n",
+    "\n",
+    "men_count = 0\n",
+    "for res in cur.execute(\"SELECT COUNT(gender) FROM works WHERE gender='Мужской';\"):\n",
+    "    men_count = res[0]\n",
+    "\n",
+    "print(\"Количество женщин: \", women_count) # 5996\n",
+    "print(\"Количество мужчин: \", men_count) # 4588\n",
+    "print(\"Пол не указан: \", total_count-women_count-men_count) # 479"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "66194476-ca0e-428e-bd9a-c98351d020c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<p>Уверенно владею компьютером, оргтехникой, работаю в программах:&nbsp; Excel, Word,&nbsp; хорошо знаю русский язык.&nbsp;</p>\n",
+      "<p>владение компьютером и оргтехникой, ведение отчетности, ведение базы данных, умение договариваться с клиентами,</p>\n",
+      "<p>Обладаю аналитическим складом ума, быстро обучаема, работоспособна, исполнительна, организованна, стрессоустойчива. Обладаю навыком работы с большим объемом информации.</p>\n"
+     ]
+    }
+   ],
+   "source": [
+    "has_skills  = list(cur.execute(\"SELECT skills FROM works WHERE skills IS NOT NULL AND skills!='';\"))\n",
+    "for res in has_skills[:3]:\n",
+    "    print(res[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9d798eb3-6a13-4204-b28a-300f7dcc9acb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[30000, 65000, 18000]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# зарплата специалистов, владеющих python\n",
+    "python_salary = []\n",
+    "for val in cur.execute(\"SELECT salary FROM works WHERE skills LIKE '%python%';\"):\n",
+    "    python_salary.append(val[0])\n",
+    "print(python_salary) # 30000, 65000, 18000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b05e0374-4a07-4cf8-b5af-a545a5e58797",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10% женщин получают до 15000\n",
+      "10% мужчин получают до 15000\n",
+      "20% женщин получают до 18000\n",
+      "20% мужчин получают до 20000\n",
+      "30% женщин получают до 20000\n",
+      "30% мужчин получают до 25000\n",
+      "40% женщин получают до 20000\n",
+      "40% мужчин получают до 30000\n",
+      "50% женщин получают до 25000\n",
+      "50% мужчин получают до 30000\n",
+      "60% женщин получают до 28000\n",
+      "60% мужчин получают до 35000\n",
+      "70% женщин получают до 30000\n",
+      "70% мужчин получают до 40000\n",
+      "80% женщин получают до 35000\n",
+      "80% мужчин получают до 50000\n",
+      "90% женщин получают до 45000\n",
+      "90% мужчин получают до 60000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# перцентили по зарплате женщин и мужчин\n",
+    "women_salary = np.array([s[0] for s in cur.execute(\"SELECT salary FROM works WHERE gender='Женский';\")])\n",
+    "men_salary = np.array([s[0] for s in cur.execute(\"SELECT salary FROM works WHERE gender='Мужской';\")])\n",
+    "percentiles = range(10, 91, 10)\n",
+    "women_percentiles = np.percentile(women_salary, percentiles, interpolation='lower')\n",
+    "men_percentiles = np.percentile(men_salary, percentiles, interpolation='lower')\n",
+    "for idx, v in enumerate(percentiles):\n",
+    "    print(f\"{percentiles[idx]}% женщин получают до {women_percentiles[idx]}\")\n",
+    "    print(f\"{percentiles[idx]}% мужчин получают до {men_percentiles[idx]}\")\n",
+    "# Женщины: [20000 25000 35000] для перцентилей [30, 50, 80]\n",
+    "# Мужчины: [25000 30000 50000] для перцентилей [30, 50, 80]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "effb336f-c298-4650-836b-00f55c966ce9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure(figsize=(10, 6))\n",
+    "plt.ylabel(\"Зарплата в рублях\")\n",
+    "plt.xlabel(\"Процент людей, получающих до этой суммы\")\n",
+    "plt.plot(percentiles, women_percentiles, 'r')\n",
+    "plt.plot(percentiles, men_percentiles, 'b')\n",
+    "plt.legend([\"Женщины\", \"Мужчины\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "88427c6a-1c9f-491c-88a0-095f8722d0e9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2016x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# распределения зарплат до 160 тыс. руб.\n",
+    "# количество людей, зарабатывающих больше, единицы \n",
+    "# поэтому они не включены в расчет\n",
+    "sal_step = 10000\n",
+    "figure(figsize=(28,7))\n",
+    "sal_upper_boundary = 160000\n",
+    "women_data = women_salary[women_salary<=sal_upper_boundary]\n",
+    "men_data = men_salary[men_salary<=sal_upper_boundary]\n",
+    "bins = int(sal_upper_boundary/sal_step)\n",
+    "xs = range(0, sal_upper_boundary, sal_step)\n",
+    "y_loc = [i*1e-6 for i in range(0, 36, 5)]\n",
+    "ys = [round(i*sal_step, 2) for i in y_loc]\n",
+    "plt.xticks(ticks=xs, labels=xs, rotation=30)\n",
+    "plt.yticks(ticks=y_loc, labels=ys)\n",
+    "plt.hist([women_data, men_data], bins, label=['Женщины','Мужчины'], density=True)\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.title(\"Распределение заработной платы по полу\")\n",
+    "plt.ylabel(\"Доля в соответствующей группе\")\n",
+    "plt.xlabel(\"Зарплата\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4db9d4d5-657c-4b40-9b07-be0c6caa5cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Среднее',)\n",
+      "('Среднее профессиональное',)\n",
+      "('',)\n",
+      "('Высшее',)\n",
+      "('Незаконченное высшее',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "for r in cur.execute(\"SELECT DISTINCT educationType FROM works;\"):\n",
+    "    print(r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "41e3ba46-ed5f-4c12-9ad4-4c0e66681cb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2016x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "stmt = \"SELECT salary FROM works WHERE salary <= \"+ str(sal_upper_boundary) +\" AND educationType = \"\n",
+    "school = np.array([i[0] for i in cur.execute(stmt + \"'Среднее';\")])\n",
+    "coll = np.array([i[0] for i in cur.execute(stmt + \"'Среднее профессиональное';\")])\n",
+    "bach = np.array([i[0] for i in cur.execute(stmt + \"'Высшее';\")])\n",
+    "dropout = np.array([i[0] for i in cur.execute(stmt + \"'Незаконченное высшее';\")])\n",
+    "\n",
+    "y_loc = [i*1e-6 for i in range(0, 41, 5)]\n",
+    "ys = [round(i*sal_step, 2) for i in y_loc]\n",
+    "figure(figsize=(28,7))\n",
+    "plt.xticks(ticks=xs, labels=xs, rotation=30)\n",
+    "plt.yticks(ticks=y_loc, labels=ys)\n",
+    "plt.hist([school, coll, bach, dropout], bins, label=['Среднее','Среднее профессиональное','Высшее','Незаконченное высшее'], density=True)\n",
+    "plt.legend(loc='upper right')\n",
+    "plt.title(\"Распределение заработной платы по образованию\")\n",
+    "plt.ylabel(\"Доля в соответствующей группе\")\n",
+    "plt.xlabel(\"Зарплата\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a568c816-8bd6-407e-b0a2-8779ddcc5090",
+   "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": "3.8.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}