Python_practice.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ccd7d070",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a string:sunil\n",
      "SUNIL\n"
     ]
    }
   ],
   "source": [
    "string_input=input(\"Enter a string:\") \n",
    "\n",
    "lambda_func=lambda x : x.upper()     #create lambda function\n",
    "\n",
    "result = lambda_func(string_input)   # call lambda function with user input\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f4b30413",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the stringGood Evening\n",
      "Vowels found in the string  ['o', 'o', 'E', 'e', 'i']\n"
     ]
    }
   ],
   "source": [
    "str_input=input(\"Enter the string\")\n",
    "\n",
    "lambda_fun= lambda x :True if x in 'aeiouAEIOU' else False\n",
    "\n",
    "vowels = filter(lambda_fun,str_input)\n",
    "\n",
    "print(\"Vowels found in the string \",list(vowels))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11c64d10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "user enter stringhello sir\n",
      "HELLO SIR\n",
      "hello sir\n",
      "Hello Sir\n",
      "Hello sir\n"
     ]
    }
   ],
   "source": [
    "user_string=input(\"user enter string\")\n",
    "\n",
    "to_upper=lambda x : x.upper()\n",
    "\n",
    "to_lower=lambda x : x.lower()\n",
    "\n",
    "to_title=lambda x : x.title()\n",
    "\n",
    "to_capital=lambda x : x.capitalize()\n",
    "\n",
    "upper_string=to_upper(user_string)\n",
    "lower_string=to_lower(user_string)\n",
    "title_string=to_title(user_string)\n",
    "capital_string=to_capital(upper_string)\n",
    "\n",
    "\n",
    "print(upper_string)\n",
    "print(lower_string)\n",
    "print(title_string)\n",
    "print(capital_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "924a8d5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter any string rana\n",
      "a a n r\n"
     ]
    }
   ],
   "source": [
    "user=input(\"Enter any string \")\n",
    "\n",
    "sort_string = lambda x : \" \".join(sorted(x))\n",
    "\n",
    "sorted_string = sort_string(user)\n",
    "\n",
    "\n",
    "print(sorted_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78cdc316",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the stringsunil\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "enter_string = input(\"Enter the string\")\n",
    "\n",
    "lam_fn = lambda x : len(x)\n",
    "\n",
    "len_str = lam_fn(enter_string)\n",
    "\n",
    "print(len_str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f69eb3ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "30\n",
      "40\n",
      "50\n",
      "[None, None, None, None, None]\n"
     ]
    }
   ],
   "source": [
    "my_list = [10,20,30,40,50]\n",
    "\n",
    "print_list = lambda x : print(x)\n",
    "\n",
    "print(list(map(print_list,my_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3625e2fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "sunil\n",
      "10.2\n",
      "True\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_list=[10,20,'sunil',10.20,True]\n",
    "\n",
    "print_list = lambda x : print(x)\n",
    "\n",
    "list(map(print_list,new_list)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af84d104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 'sunil', 10.2, True]\n"
     ]
    }
   ],
   "source": [
    "my_list[0] = 10\n",
    "my_list[1] = 20\n",
    "my_list[2] = \"sunil\"\n",
    "my_list[3] = 10.2\n",
    "my_list[4] = True\n",
    "\n",
    "print(my_list)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8cc52006",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the number7\n",
      "The prime number of 7 is True\n"
     ]
    }
   ],
   "source": [
    "is_prime=lambda n : n >1 and all (n%i!=0 for i in range(2,int(n**0.5) + 1))\n",
    "prime = eval(input(\"Enter the number\"))\n",
    "\n",
    "result =is_prime(prime)\n",
    "\n",
    "print(\"The prime number of {0} is {1}\".format(prime,result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1cd457e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number: 5\n",
      "The number 5 is prime\n"
     ]
    }
   ],
   "source": [
    "is_prime = lambda n: n > 1 and all(n % i != 0 for i in range(2, int(n**0.5) + 1))\n",
    "\n",
    "prime = int(input(\"Enter a number: \"))\n",
    "\n",
    "if is_prime(prime):\n",
    "    print(\"The number\", prime, \"is prime\")\n",
    "else:\n",
    "    print(\"The number\", prime, \"is not prime\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8a183fad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a non negative integer5\n",
      "factorial of 5 is 120\n"
     ]
    }
   ],
   "source": [
    "factorial = lambda n : 1 if n == 0 else n*factorial(n-1)\n",
    "\n",
    "n=int(input(\"Enter a non negative integer\"))\n",
    "\n",
    "result=factorial(n)\n",
    "\n",
    "print(f\"factorial of {n} is {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "96dcfa50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "World\n"
     ]
    }
   ],
   "source": [
    "string = \" Hello World\"\n",
    "\n",
    "slicing_func = lambda s : s[7:12]\n",
    "\n",
    "print(slicing_func(string))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "822dba6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x29d8f73da30>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "x = [1, 2, 3, 4]\n",
    "y = [1, 4, 9, 16]\n",
    "plt.plot(x, y)"
   ]
  }
 ],
 "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",
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