Exam1_Nijat_Hajiyev.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1st Task\n",
    "\n",
    "\n",
    "Ashaqida verilen cedvele gore insanin illik geliri uzre vergini hesablayin.\n",
    "\n",
    "Meselen 45000 AZN illik geliri olan adamin vergi borcu ashaqidaki kimi hesablanacaq.\n",
    "\n",
    "(10000)*0% + (10000)10%  + (25000)20% = 6000.\n",
    "\n",
    "P.S illik gelir input ile daxil olunmalidir"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Illik gelirinizi daxil edin: 45000\n",
      "Sizin verginiz: \n",
      "6000.0\n"
     ]
    }
   ],
   "source": [
    "salary = int(input(\"Illik gelirinizi daxil edin: \"))\n",
    "    \n",
    "\n",
    "if salary <= 10000:\n",
    "    print(\"Sizin verginiz: 0\")\n",
    "elif salary > 10000 and salary <= 20000:\n",
    "    print(\"Sizin verginiz:\")\n",
    "    print((salary - 10000) * 0.1) \n",
    "else:\n",
    "    print(\"Sizin verginiz: \")\n",
    "    print( 10000* 0.1 + (salary - 20000) * 0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2nd Task\n",
    "\n",
    "45 ile 144 arasinda (her ikisi daxil) hem 3e hem 11e qaliqsiz bolunen ededleri gosterin.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66\n",
      "99\n",
      "132\n"
     ]
    }
   ],
   "source": [
    "for i in range(45, 145):\n",
    "    if i % 3 == 0 and i%11 == 0:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3rd Task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quracaqiniz proqram 10-15 arasindaki reqemi texmin etmelidir. \n",
    "\n",
    "Random vasitesile verilen araliqda reqem yadda saxlanilir.\n",
    "\n",
    "Input ile user-in daxil edeceyi reqem ile random-dan gelen reqem eyni olarsa hemin reqem print olunur.\n",
    "\n",
    "Eks halda reqem doqru tapilana kimi proses davam edir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Texmin etdiyimiz reqem: 11\n",
    "\n",
    "Texmin etdiyimiz reqem: 10\n",
    "\n",
    "Texmin etdiyimiz reqem: 12\n",
    "\n",
    "Texmin etdiyimiz reqem: 13\n",
    "\n",
    "Texmin etdiyimiz reqem: 14\n",
    "\n",
    "Texmin etdiyimiz reqem: 15\n",
    "\n",
    "15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reqem texmin edin: 10\n",
      "Reqem texmin edin: 1\n",
      "Reqem texmin edin: 11\n",
      "Reqem texmin edin: 12\n",
      "Tebrikler, duz tapdiniz\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "from random import randint\n",
    "rand_num = randint(10,15)\n",
    "\n",
    "\n",
    "\n",
    "while True:\n",
    "    reqem = int(input(\"Reqem texmin edin: \"))\n",
    "    if rand_num == reqem:\n",
    "        print(\"Tebrikler, duz tapdiniz\")\n",
    "        print(rand_num)\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4th task"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Verilmish listin daxilindeki deyerlerin nece defe tekrarlandiqin dictionary ile gosterin.\n",
    "\n",
    "Listdeki deyerler 'key', tekrarlanma sayi ise 'value' olmalidir.\n",
    "\n",
    "List - [142, 'laptop', 'point', 13, 'point', 142, 'real', 'real', 13]\n",
    "\n",
    "netice:\n",
    "\n",
    "{142: 2, 'laptop': 1, 'point': 2, 13: 2, 'real': 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{142: 2, 'laptop': 1, 'point': 2, 13: 2, 'real': 2}"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [142, 'laptop', 'point', 13, 'point', 142, 'real', 'real', 13]\n",
    "\n",
    "dict((i, my_list.count(i)) for i in my_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5th task \n",
    "\n",
    "1-7 arasindaki ededlerle ozunden evvelki ededlerin cemini hesablayan proqram qurun.\n",
    "\n",
    "Netice:\n",
    "\n",
    "Cari eded 1 Onceki Eded 0 Cem 1\n",
    "\n",
    "Cari eded 2 Onceki Eded 1 Cem 3\n",
    "\n",
    "Cari eded 3 Onceki Eded 2 Cem 5\n",
    "\n",
    "Cari eded 4 Onceki Eded 3 Cem 7\n",
    "\n",
    "Cari eded 5 Onceki Eded 4 Cem 9\n",
    "\n",
    "Cari eded 6 Onceki Eded 5 Cem 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cari eded: 1, Onceki eded: 0, Cem: 1\n",
      "Cari eded: 2, Onceki eded: 1, Cem: 3\n",
      "Cari eded: 3, Onceki eded: 2, Cem: 5\n",
      "Cari eded: 4, Onceki eded: 3, Cem: 7\n",
      "Cari eded: 5, Onceki eded: 4, Cem: 9\n",
      "Cari eded: 6, Onceki eded: 5, Cem: 11\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,7):\n",
    "    a= i\n",
    "    b = a-1\n",
    "    c = a+b\n",
    "    print(\"Cari eded: {0}, Onceki eded: {1}, Cem: {2}\".format(a, b, c))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
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