nascom.py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cf8559e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 2, 3, 5, 8, 13, 21, 34]\n"
     ]
    }
   ],
   "source": [
    "#wrire a program to print fibonacci series using a function \n",
    "def fibonacci(n):\n",
    "    a,b=0,1\n",
    "    fib_series=[a,b]\n",
    "    for i in range(2,n,1):\n",
    "        \n",
    "        a,b=b,a+b\n",
    "        fib_series.append(b)\n",
    "    return fib_series\n",
    "res=fibonacci(10)        \n",
    "print(res)   \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "154ffc7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " sum of elements in the list are : 90\n"
     ]
    }
   ],
   "source": [
    "def sum_of(list1):\n",
    "    return sum(list1)\n",
    "list1=[2,4,67,8,9]\n",
    "print(f\" sum of elements in the list are : {sum_of(list1)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d0e47499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n",
      "*\n"
     ]
    }
   ],
   "source": [
    "# ask this doubt why this output is coming\n",
    "\n",
    "for i in range(1,5):\n",
    "    for j in range(1,i+1):\n",
    "        print(\"*\")\n",
    "          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e2036cfe",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 4) (3011653620.py, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[22], line 4\u001b[1;36m\u001b[0m\n\u001b[1;33m    print(f\" the dictionary has this key {check_(dict,\"name\")})\u001b[0m\n\u001b[1;37m                                                           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 4)\n"
     ]
    }
   ],
   "source": [
    "def check_(dict,key):\n",
    "    return key in dict\n",
    "dict={\"name\":\"Maih\",\"age\":12,\"class\":9}\n",
    "print(f\" the dictionary has this key {check_(dict,\"name\")})    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9fa39ef",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Id</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shalini</td>\n",
       "      <td>45</td>\n",
       "      <td>500000</td>\n",
       "      <td>1</td>\n",
       "      <td>kacheri road</td>\n",
       "      <td>kota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rani</td>\n",
       "      <td>78</td>\n",
       "      <td>890000</td>\n",
       "      <td>2</td>\n",
       "      <td>BK Kaul</td>\n",
       "      <td>Rajkot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>shrusti</td>\n",
       "      <td>56</td>\n",
       "      <td>450000</td>\n",
       "      <td>nandi nagar</td>\n",
       "      <td>jaipur</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ragini</td>\n",
       "      <td>41</td>\n",
       "      <td>780000</td>\n",
       "      <td>ganesh nagar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Salary            Id       Address    City\n",
       "0  shalini   45  500000             1  kacheri road    kota\n",
       "1     rani   78  890000             2       BK Kaul  Rajkot\n",
       "2  shrusti   56  450000   nandi nagar        jaipur     NaN\n",
       "3   ragini   41  780000  ganesh nagar           NaN     NaN"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"nascomm.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0490fb4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 6 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Name     4 non-null      object\n",
      " 1   Age      4 non-null      int64 \n",
      " 2   Salary   4 non-null      int64 \n",
      " 3   Id       4 non-null      object\n",
      " 4   Address  3 non-null      object\n",
      " 5   City     2 non-null      object\n",
      "dtypes: int64(2), object(4)\n",
      "memory usage: 324.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b0a6401e",
   "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>Age</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>655000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>16.593171</td>\n",
       "      <td>213619.5996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>450000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>487500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.500000</td>\n",
       "      <td>640000.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.500000</td>\n",
       "      <td>807500.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.000000</td>\n",
       "      <td>890000.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Age       Salary\n",
       "count   4.000000       4.0000\n",
       "mean   55.000000  655000.0000\n",
       "std    16.593171  213619.5996\n",
       "min    41.000000  450000.0000\n",
       "25%    44.000000  487500.0000\n",
       "50%    50.500000  640000.0000\n",
       "75%    61.500000  807500.0000\n",
       "max    78.000000  890000.0000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6038d708",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Name'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[53], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m df\u001b[38;5;241m.\u001b[39mdrop(columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mName\u001b[39m\u001b[38;5;124m'\u001b[39m],axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m      2\u001b[0m df\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:5258\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   5110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[0;32m   5111\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   5112\u001b[0m     labels: IndexLabel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5119\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   5120\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   5121\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   5122\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[0;32m   5123\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   5256\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[0;32m   5257\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 5258\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mdrop(\n\u001b[0;32m   5259\u001b[0m         labels\u001b[38;5;241m=\u001b[39mlabels,\n\u001b[0;32m   5260\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m   5261\u001b[0m         index\u001b[38;5;241m=\u001b[39mindex,\n\u001b[0;32m   5262\u001b[0m         columns\u001b[38;5;241m=\u001b[39mcolumns,\n\u001b[0;32m   5263\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[0;32m   5264\u001b[0m         inplace\u001b[38;5;241m=\u001b[39minplace,\n\u001b[0;32m   5265\u001b[0m         errors\u001b[38;5;241m=\u001b[39merrors,\n\u001b[0;32m   5266\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4549\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[1;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[0;32m   4547\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   4548\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 4549\u001b[0m         obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_drop_axis(labels, axis, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4551\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m   4552\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:4591\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[1;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[0;32m   4589\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4590\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 4591\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[0;32m   4592\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[0;32m   4594\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[0;32m   4595\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6699\u001b[0m, in \u001b[0;36mIndex.drop\u001b[1;34m(self, labels, errors)\u001b[0m\n\u001b[0;32m   6697\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[0;32m   6698\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m-> 6699\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlist\u001b[39m(labels[mask])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   6700\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[0;32m   6701\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Name'] not found in axis\""
     ]
    }
   ],
   "source": [
    "df.drop(columns=['Name'],axis=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8139d079",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Id</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shalini</td>\n",
       "      <td>45</td>\n",
       "      <td>500000</td>\n",
       "      <td>1</td>\n",
       "      <td>kacheri road</td>\n",
       "      <td>kota</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>rani</td>\n",
       "      <td>78</td>\n",
       "      <td>890000</td>\n",
       "      <td>2</td>\n",
       "      <td>BK Kaul</td>\n",
       "      <td>Rajkot</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Salary Id       Address    City\n",
       "0  shalini   45  500000  1  kacheri road    kota\n",
       "1     rani   78  890000  2       BK Kaul  Rajkot"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b2e477d",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ragini</td>\n",
       "      <td>41</td>\n",
       "      <td>780000</td>\n",
       "      <td>ganesh nagar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Name  Age  Salary            Id Address City\n",
       "3  ragini   41  780000  ganesh nagar     NaN  NaN"
      ]
     },
     "execution_count": 6,
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   "execution_count": 7,
   "id": "cb47ff07",
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      "text/plain": [
       "(4, 6)"
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   "execution_count": 9,
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       "      <td>487500.0000</td>\n",
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       "      <td>50.500000</td>\n",
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       "             Age       Salary\n",
       "count   4.000000       4.0000\n",
       "mean   55.000000  655000.0000\n",
       "std    16.593171  213619.5996\n",
       "min    41.000000  450000.0000\n",
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       "50%    50.500000  640000.0000\n",
       "75%    61.500000  807500.0000\n",
       "max    78.000000  890000.0000"
      ]
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   "execution_count": 10,
   "id": "bae5d741",
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   "outputs": [
    {
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       "Index(['Name', 'Age', 'Salary', 'Id', 'Address', 'City'], dtype='object')"
      ]
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     "execution_count": 10,
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       "    Name    Age  Salary     Id  Address   City\n",
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       "1  False  False   False  False    False  False\n",
       "2  False  False   False  False    False   True\n",
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   "cell_type": "code",
   "execution_count": 12,
   "id": "2e124334",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Name       0\n",
       "Age        0\n",
       "Salary     0\n",
       "Id         0\n",
       "Address    1\n",
       "City       2\n",
       "dtype: int64"
      ]
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     "execution_count": 12,
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       "1     rani   78  890000             2       BK Kaul  Rajkot\n",
       "2  shrusti   56  450000   nandi nagar        jaipur     NaN\n",
       "3   ragini   41  780000  ganesh nagar           NaN     NaN"
      ]
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     "metadata": {},
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    }
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    "df"
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   "cell_type": "code",
   "execution_count": 16,
   "id": "78fb6bcb",
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   "outputs": [
    {
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       "0    45\n",
       "1    78\n",
       "2    56\n",
       "3    41\n",
       "Name: Age, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df.Age\n",
    "\n"
   ]
  },
  {
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   "id": "305a81d4",
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       "      Name  Age  Salary            Id       Address    City\n",
       "0  shalini   45  500000             1  kacheri road    kota\n",
       "1     rani   78  890000             2       BK Kaul  Rajkot\n",
       "2  shrusti   56  450000   nandi nagar        jaipur     NaN\n",
       "3   ragini   41  780000  ganesh nagar           NaN     NaN"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"Salary\"]>12000]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4ac4ff53",
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Id</th>\n",
       "      <th>Address</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>shalini</td>\n",
       "      <td>45</td>\n",
       "      <td>500000</td>\n",
       "      <td>1</td>\n",
       "      <td>kacheri road</td>\n",
       "      <td>kota</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age  Salary Id       Address  City\n",
       "0  shalini   45  500000  1  kacheri road  kota"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df[\"Salary\"] > 2000) & (df[\"City\"] == \"kota\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7cfa9139",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "80235059",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Salary'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "010a0166",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "74adee30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_label=\"Salary\",y_label=\"City\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "22942a3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'chart'}, ylabel='y-axis'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"Salary\",y=\"Age\",kind=\"pie\",xlabel=\"x-axis\",ylabel=\"y-axis\",title=\"chart\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9765a84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([500000, 890000, 450000, 780000], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Salary\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a1233a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#include <bits/stdc++.h>\n",
      "\n",
      "using namespace std;\n",
      "\n",
      "string ltrim(const string &);\n",
      "string rtrim(const string &);\n",
      "\n",
      "/*\n",
      " * Complete the 'isBalanced' function below.\n",
      " *\n",
      " * The function is expected to return a STRING.\n",
      " * The function accepts STRING s as parameter.\n",
      " */\n",
      "\n",
      "string isBalanced(string s) {\n",
      "      stack<char>st;\n",
      "      for(int i=0;i<s.length();i++){\n",
      "          if(s[i]=='{'||s[i]=='['||s[i]=='('){\n",
      "              st.push(s[i]);\n",
      "          }\n",
      "          else if(s[i] == ')' || s[i] == ']' || s[i] == '}') {\n",
      "            if(st.empty()) {\n",
      "                return \"NO\";\n",
      "            }\n",
      "\n",
      "            char top = st.top();\n",
      "            st.pop();\n",
      "\n",
      "            if((s[i] == ')' && top != '(') ||\n",
      "               (s[i] == ']' && top != '[') ||\n",
      "               (s[i] == '}' && top != '{')) {\n",
      "                return \"NO\";\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "    return st.empty() ? \"YES\": \"NO\"; \n",
      "}\n",
      "\n",
      "\n",
      "int main()\n",
      "{\n",
      "    ofstream fout(getenv(\"OUTPUT_PATH\"));\n",
      "\n",
      "    string t_temp;\n",
      "    getline(cin, t_temp);\n",
      "\n",
      "    int t = stoi(ltrim(rtrim(t_temp)));\n",
      "\n",
      "    for (int t_itr = 0; t_itr < t; t_itr++) {\n",
      "        string s;\n",
      "        getline(cin, s);\n",
      "\n",
      "        string result = isBalanced(s);\n",
      "\n",
      "        fout << result << \"\\n\";\n",
      "    }\n",
      "\n",
      "    fout.close();\n",
      "\n",
      "    return 0;\n",
      "}\n",
      "\n",
      "string ltrim(const string &str) {\n",
      "    string s(str);\n",
      "\n",
      "    s.erase(\n",
      "        s.begin(),\n",
      "        find_if(s.begin(), s.end(), not1(ptr_fun<int, int>(isspace)))\n",
      "    );\n",
      "\n",
      "    return s;\n",
      "}\n",
      "\n",
      "string rtrim(const string &str) {\n",
      "    string s(str);\n",
      "\n",
      "    s.erase(\n",
      "        find_if(s.rbegin(), s.rend(), not1(ptr_fun<int, int>(isspace))).base(),\n",
      "        s.end()\n",
      "    );\n",
      "\n",
      "    return s;\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    file=open(r\"C:\\Users\\91787\\Desktop\\myFile.txt\",'r')\n",
    "    content=file.read()\n",
    "    print(content)\n",
    "    file.close()\n",
    "    \n",
    "except FileNotFoundError:\n",
    "    print(\"file is not found \")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cc7feb9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index not found\n"
     ]
    }
   ],
   "source": [
    "list=[1,2,3,4,5]\n",
    "try:\n",
    "    print(list[6])\n",
    "except IndexError:\n",
    "    print(\"index not found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb06aa2f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key is not found \n"
     ]
    }
   ],
   "source": [
    "dict1={\"name\":\"riya\",\"rollno\":45,\"id\":1}\n",
    "try:\n",
    "    print(dict1[\"roll\"])\n",
    "except KeyError:\n",
    "    print(\"key is not found \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cd161371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter any no 4\n",
      "enter value of divisor 0\n",
      "division is not possible \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    n=int(input(\"enter any no \"))\n",
    "    p=int(input(\"enter value of divisor \"))\n",
    "    result=n/p\n",
    "    print(result)\n",
    "except ZeroDivisionError:\n",
    "    print(\"division is not possible \")\n",
    "except ValueError:\n",
    "    print(\"please enter a valid input \")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6efa99ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jkdksjdsd\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    file=open(r\"C:\\Users\\91787\\Desktop\\myFile.txt\",'r')\n",
    "    content=file.read()\n",
    "    print(content)\n",
    "    file.close()\n",
    "    \n",
    "except FileNotFoundError:\n",
    "    print(\"file is not found \")\n",
    "except IOError:\n",
    "    print(\"input output error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4b13dd5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello hello helloABCDEFGHIJK\n"
     ]
    }
   ],
   "source": [
    "file2=open(r\"C:\\Users\\91787\\Desktop\\test.txt\",'r')\n",
    "content=file2.read()\n",
    "print(content)\n",
    "file2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14a45284",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r\"C:\\Users\\91787\\Desktop\\test.txt\",'w') as file2:\n",
    "    file2.write(\"hello hello hello\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0529efe5",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r\"C:\\Users\\91787\\Desktop\\test.txt\",'a') as file2:\n",
    "    file2.write(\"ABCDEFGHIJK\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8e6bc143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>city</th>\n",
       "      <th>address</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>450000</td>\n",
       "      <td>ajmer</td>\n",
       "      <td>ganpati nagar</td>\n",
       "      <td>IT officer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4700000</td>\n",
       "      <td>jaipur</td>\n",
       "      <td>vigyan nagar</td>\n",
       "      <td>HOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47000</td>\n",
       "      <td>kota</td>\n",
       "      <td>nandi nagar</td>\n",
       "      <td>AI engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23000</td>\n",
       "      <td>bangaluru</td>\n",
       "      <td>narayani nagar</td>\n",
       "      <td>backend developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>63333</td>\n",
       "      <td>beawar</td>\n",
       "      <td>sarojini nagar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   salary       city         address               post\n",
       "0   1   450000      ajmer   ganpati nagar         IT officer\n",
       "1   2  4700000     jaipur    vigyan nagar                HOD\n",
       "2   3    47000       kota     nandi nagar        AI engineer\n",
       "3   4    23000  bangaluru  narayani nagar  backend developer\n",
       "4   5    63333     beawar  sarojini nagar                NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"nascomm.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c1acfce6",
   "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>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>city</th>\n",
       "      <th>address</th>\n",
       "      <th>post</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>450000</td>\n",
       "      <td>ajmer</td>\n",
       "      <td>ganpati nagar</td>\n",
       "      <td>IT officer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4700000</td>\n",
       "      <td>jaipur</td>\n",
       "      <td>vigyan nagar</td>\n",
       "      <td>HOD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47000</td>\n",
       "      <td>kota</td>\n",
       "      <td>nandi nagar</td>\n",
       "      <td>AI engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>23000</td>\n",
       "      <td>bangaluru</td>\n",
       "      <td>narayani nagar</td>\n",
       "      <td>backend developer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>63333</td>\n",
       "      <td>beawar</td>\n",
       "      <td>sarojini nagar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id   salary       city         address               post\n",
       "0   1   450000      ajmer   ganpati nagar         IT officer\n",
       "1   2  4700000     jaipur    vigyan nagar                HOD\n",
       "2   3    47000       kota     nandi nagar        AI engineer\n",
       "3   4    23000  bangaluru  narayani nagar  backend developer\n",
       "4   5    63333     beawar  sarojini nagar                  0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "aed198fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='id'>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"id\",y=\"salary\",kind=\"bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "8a695d2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='id'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(x=\"id\",y=\"salary\",kind=\"line\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "95ea8b8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter value of a4\n",
      "enter value of divisor 0\n",
      "cannot divide by zero\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a=int(input(\"enter value of a\"))\n",
    "    b=int(input(\"enter value of divisor \"))\n",
    "    result=a/b\n",
    "    print(result)\n",
    "except ZeroDivisionError:\n",
    "    print(\"cannot divide by zero\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "66fcba52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter value of a4\n",
      "enter value  of  divisor k\n",
      " value entered is invalid  \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a=int(input(\"enter value of a\"))\n",
    "    b=int(input(\"enter value  of  divisor \"))\n",
    "    print(a/b)\n",
    "except ValueError:\n",
    "    print(\" value entered is invalid  \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c3b0dc9e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hghjffhjjkhkh\n"
     ]
    }
   ],
   "source": [
    "\n",
    "try:\n",
    "    file5=open(r\"C:\\Users\\91787\\Desktop\\test2.txt\",'r')\n",
    "    \n",
    "    con=file5.read()\n",
    "    print(con)\n",
    "    file5.close()\n",
    "    \n",
    "except IOError:\n",
    "    print(\" input output error \")\n",
    "except FileNotFoundError:\n",
    "    print(\"file not found \")\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6dafba11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cdb0f80a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 2, 1, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.array([4,2,1,5])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a7636eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2=np.array([[1,2,3],[4,5,6]])\n",
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e21d858f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3=np.zeros((2,3))\n",
    "arr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "056b9f0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr5=np.ones((2,2))\n",
    "arr5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5985f71f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 10, 10],\n",
       "       [12, 10,  8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr6=np.array([[1,2,3],[4,5,6]])\n",
    "arr7=np.array([[9,8,7],[8,5,2]])\n",
    "result=arr6+arr7\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e495382d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 16],\n",
       "       [32, 10]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiplying two arrays \n",
    "arr6=np.array([[1,2],[4,5]])\n",
    "arr7=np.array([[9,8],[8,2]])\n",
    "result=arr6*arr7\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c2799798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr8=np.arange(9).reshape(3,3)\n",
    "arr8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ace69189",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.69314718, 1.60943791, 1.79175947, 1.94591015])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr36=np.array([1,2,5,6,7])\n",
    "log_val=np.log(arr36)\n",
    "log_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "43e22cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   2.71828183,    7.3890561 ,  148.4131591 ,  403.42879349,\n",
       "       1096.63315843])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr36=np.array([1,2,5,6,7])\n",
    "exp_val=np.exp(arr36)\n",
    "exp_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ae6d1605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ar=np.array([[12,2],[5,6]])\n",
    "sli=ar[0,0:1]\n",
    "sli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3410f7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3151673805580453"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr36=np.array([1,2,5,6,7])\n",
    "res=np.std(arr36)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b5ffc4dc",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'stats' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mode\n\u001b[0;32m      2\u001b[0m arrr\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m5\u001b[39m,\u001b[38;5;241m4\u001b[39m])\n\u001b[1;32m----> 3\u001b[0m mo\u001b[38;5;241m=\u001b[39mstats\u001b[38;5;241m.\u001b[39mmode(arrr)\n\u001b[0;32m      4\u001b[0m mo\n",
      "\u001b[1;31mNameError\u001b[0m: name 'stats' is not defined"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mode\n",
    "arrr=np.array([2,5,5,4])\n",
    "mo=stats.mode(arrr)\n",
    "mo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5dc8a157",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105,  18],\n",
       "       [210,  36]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr=np.array([[2,3],[4,6]])\n",
    "arr2=np.array([[45,3],[5,4]])\n",
    "res=np.dot(arr,arr2)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "380b2d3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[105,  18],\n",
       "       [210,  36]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr=np.array([[2,3],[4,6]])\n",
    "arr2=np.array([[45,3],[5,4]])\n",
    "res=arr@arr2\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ab43bf15",
   "metadata": {},
   "outputs": [
    {
     "ename": "LinAlgError",
     "evalue": "Singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[11], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m arr\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[1;32m----> 2\u001b[0m inverse1\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39minv(arr)\n\u001b[0;32m      3\u001b[0m inverse1\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36minv\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\numpy\\linalg\\linalg.py:538\u001b[0m, in \u001b[0;36minv\u001b[1;34m(a)\u001b[0m\n\u001b[0;32m    536\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124md->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    537\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_singular)\n\u001b[1;32m--> 538\u001b[0m ainv \u001b[38;5;241m=\u001b[39m _umath_linalg\u001b[38;5;241m.\u001b[39minv(a, signature\u001b[38;5;241m=\u001b[39msignature, extobj\u001b[38;5;241m=\u001b[39mextobj)\n\u001b[0;32m    539\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap(ainv\u001b[38;5;241m.\u001b[39mastype(result_t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\numpy\\linalg\\linalg.py:89\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[1;34m(err, flag)\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[1;32m---> 89\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSingular matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mLinAlgError\u001b[0m: Singular matrix"
     ]
    }
   ],
   "source": [
    "arr=np.array([[2,3],[4,6]])\n",
    "inverse1=np.linalg.inv(arr)\n",
    "inverse1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81e782b3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "LinAlgError",
     "evalue": "Singular matrix",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mLinAlgError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[17], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m arr9\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([[\u001b[38;5;241m2\u001b[39m,\u001b[38;5;241m3\u001b[39m],[\u001b[38;5;241m4\u001b[39m,\u001b[38;5;241m6\u001b[39m]])\n\u001b[0;32m      2\u001b[0m arr7\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39marray([[\u001b[38;5;241m7\u001b[39m,\u001b[38;5;241m8\u001b[39m],[\u001b[38;5;241m6\u001b[39m,\u001b[38;5;241m9\u001b[39m]])\n\u001b[1;32m----> 3\u001b[0m pe\u001b[38;5;241m=\u001b[39mnp\u001b[38;5;241m.\u001b[39mlinalg\u001b[38;5;241m.\u001b[39msolve(arr9,arr7)\n\u001b[0;32m      4\u001b[0m pe\n",
      "File \u001b[1;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36msolve\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\numpy\\linalg\\linalg.py:386\u001b[0m, in \u001b[0;36msolve\u001b[1;34m(a, b)\u001b[0m\n\u001b[0;32m    384\u001b[0m signature \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDD->D\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m isComplexType(t) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdd->d\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m    385\u001b[0m extobj \u001b[38;5;241m=\u001b[39m get_linalg_error_extobj(_raise_linalgerror_singular)\n\u001b[1;32m--> 386\u001b[0m r \u001b[38;5;241m=\u001b[39m gufunc(a, b, signature\u001b[38;5;241m=\u001b[39msignature, extobj\u001b[38;5;241m=\u001b[39mextobj)\n\u001b[0;32m    388\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m wrap(r\u001b[38;5;241m.\u001b[39mastype(result_t, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\numpy\\linalg\\linalg.py:89\u001b[0m, in \u001b[0;36m_raise_linalgerror_singular\u001b[1;34m(err, flag)\u001b[0m\n\u001b[0;32m     88\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_raise_linalgerror_singular\u001b[39m(err, flag):\n\u001b[1;32m---> 89\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m LinAlgError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSingular matrix\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mLinAlgError\u001b[0m: Singular matrix"
     ]
    }
   ],
   "source": [
    "arr9=np.array([[2,3],[4,6]])\n",
    "arr7=np.array([[7,8],[6,9]])\n",
    "pe=np.linalg.solve(arr9,arr7)\n",
    "pe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "42a9a6fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-343.99999999999994"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# determinant of matrix \n",
    "arr5=np.array([[2,89],[4,6]])\n",
    "deter=np.linalg.det(arr5)\n",
    "deter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cb1e92c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "33f41cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate a random value between 0 to 1\n",
    "random_number=rn.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fa4c542e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4260956492600355"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bce8da5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56\n"
     ]
    }
   ],
   "source": [
    "rand=rn.randint(1,60)\n",
    "print(rand)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "40556f1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#start stop step\n",
    "rand1=rn.randrange(0,100,2)\n",
    "rand1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "89d6f860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'banana'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lis=[\"apple\",\"banana\",\"orange\",\"grapes\"]\n",
    "rand=rn.choice(lis)\n",
    "rand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5d83fbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['apple', 'banana', 'orange', 'grapes']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shuffle function in random library\n",
    "lis2=[\"apple\",\"banana\",\"orange\",\"grapes\"]\n",
    "rand=rn.shuffle(lis2)\n",
    "lis2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d13db870",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[9, 1]\n",
      "[7, 9]\n"
     ]
    }
   ],
   "source": [
    "r,c=2,2\n",
    "random_arr=[[rn.randint(1,10) for _ in range(r)] for _ in range(c)]\n",
    "for r in random_arr:\n",
    "    print(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2f28ad73",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random as rn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cbb14c79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter your choice head or tail-> head\n",
      "toss output = head\n",
      "you win the toss \n"
     ]
    }
   ],
   "source": [
    "def coin_toss():\n",
    "    result=rn.choice(['head','tail'])\n",
    "    return result\n",
    " \n",
    "    \n",
    "def main():\n",
    "    user_choice=input(\"enter your choice head or tail-> \")\n",
    "    toss_result=coin_toss()\n",
    "    print(\"toss output =\",toss_result)\n",
    "    \n",
    "    if(user_choice== toss_result):\n",
    "        print(\"you win the toss \")\n",
    "    else:\n",
    "        print(\"you lost the toss \")\n",
    "        \n",
    "if __name__==\"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a6535a86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "please enter any button to roll the diceh\n",
      "you got a number  4\n",
      "do you want to roll again enter yes or no ->yes\n",
      "please enter any button to roll the dice\n",
      "you got a number  6\n",
      "do you want to roll again enter yes or no ->no\n",
      "thanks for playing \n",
      "welcome to the dice game \n"
     ]
    }
   ],
   "source": [
    "# dice roller\n",
    "def dice():\n",
    "    return rn.randint(1,6)\n",
    "\n",
    "def main():\n",
    "    print(\"welcome to the dice game \")\n",
    "    \n",
    "while(True):\n",
    "        \n",
    "        input(\"please enter any button to roll the dice\")\n",
    "        result=dice()\n",
    "        print(\"you got a number \", result)\n",
    "        play=input(\"do you want to roll again enter yes or no ->\")\n",
    "        if(play!=\"yes\"):\n",
    "            print(\"thanks for playing \")\n",
    "            break\n",
    "if __name__==\"__main__\":\n",
    "    main()\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7329bc48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "welcome to stone paper scissor game \n"
     ]
    },
    {
     "ename": "UnboundLocalError",
     "evalue": "cannot access local variable 'user_choice' where it is not associated with a value",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[23], line 44\u001b[0m\n\u001b[0;32m     36\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m     43\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m\u001b[38;5;241m==\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m---> 44\u001b[0m     main()\n",
      "Cell \u001b[1;32mIn[23], line 28\u001b[0m, in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m     26\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwelcome to stone paper scissor game \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     27\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m(\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m---> 28\u001b[0m     user_choice\u001b[38;5;241m=\u001b[39muser_choice()\n\u001b[0;32m     29\u001b[0m     computer_choice\u001b[38;5;241m=\u001b[39mcomputer_choice()\n\u001b[0;32m     30\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou choose \u001b[39m\u001b[38;5;124m\"\u001b[39m, user_choice)\n",
      "\u001b[1;31mUnboundLocalError\u001b[0m: cannot access local variable 'user_choice' where it is not associated with a value"
     ]
    }
   ],
   "source": [
    "#wap to create stone paper scissor\n",
    "\n",
    "def user_choice():\n",
    "    user_choice=input(\"enter your choice (stone ,paper, scissor)->\").lower()\n",
    "    while user_choice not in [\"stone\",\"paper\",\"scissor\"]:\n",
    "        print(\"invalid input please choose again \")\n",
    "        user_choice=input(\"enter your choice (stone ,paper, scissor)->\").lower()\n",
    "    return user_choice\n",
    "    \n",
    "    \n",
    "def computer_choice():\n",
    "    return random.choice([\"stone\",\"paper\",\"scissor\"])\n",
    "\n",
    "\n",
    "def winner(user_input,computer_input):\n",
    "    if(user_input==computer_input):\n",
    "        return \"match is tie \"\n",
    "    elif((user_input==\"stone\" and computer_input==\"scissor\") or (user_input==\"paper\" and computer_input==\"stone\") or (user_input==\"scissor\" and computer_input==\"paper\")):\n",
    "        \n",
    "        print(\"user win the game \")\n",
    "    else:\n",
    "        return \"sorry you lost \"\n",
    "\n",
    "    \n",
    "def main():\n",
    "    print(\"welcome to stone paper scissor game \")\n",
    "    while(True):\n",
    "        user_choice=user_choice()\n",
    "        computer_choice=computer_choice()\n",
    "        print(\"you choose \", user_choice)\n",
    "        print(\"computer choice \",computer_choice)\n",
    "        winner(user_choice,computer_choice)\n",
    "        play_again=input(\"do you want to play again enter yes or no->\").lower()\n",
    "        if(play_again==\"no\"):\n",
    "            print(\"thanks for playing \")\n",
    "            break\n",
    "            \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "if __name__==\"__main__\":\n",
    "    main()\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8aa21823",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sssss\n"
     ]
    }
   ],
   "source": [
    "class details:\n",
    "    name=\"sssss\"\n",
    "    rollno=4\n",
    "\n",
    "obj=details()\n",
    "print(obj.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1aa9b555",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name is Mahima from address sarojini nagar and roll no is 25\n",
      "name is changed_name from address ssss and roll no is 45\n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    name=\"Mahima\"\n",
    "    rollno=25\n",
    "    address=\"sarojini nagar\"\n",
    "    \n",
    "    def info(self):\n",
    "        print(f\"name is {self.name} from address {self.address} and roll no is {self.rollno}\")\n",
    "        \n",
    "              \n",
    "              \n",
    "obj1=person()\n",
    "obj1.info()\n",
    "obj2=person()\n",
    "obj2.name=\"changed_name\"\n",
    "obj2.rollno=45\n",
    "obj2.address=\"ssss\"\n",
    "obj2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "83853355",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am a constructor \n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    def __init__(self):\n",
    "        print(\"i am a constructor \")\n",
    "obj=person()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2eadddd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " name is Heena and roll no is 78\n",
      " name is new_name and roll no is 78\n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    def __init__(self):\n",
    "        self.name=\"Heena\"\n",
    "        self.rollno=78\n",
    "    def info(self):\n",
    "        print(f\" name is {self.name} and roll no is {self.rollno}\")\n",
    "obj=person()\n",
    "obj.info()\n",
    "obj1=person()\n",
    "obj1.name=\"new_name\"\n",
    "obj1.rollno=78\n",
    "obj1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8ae92908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " name is sara  from  address rishikesh\n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    def __init__(self,n,r,a):\n",
    "        self.name=n\n",
    "        self.rollno=r\n",
    "        self.address=a\n",
    "        \n",
    "    def info(self):\n",
    "        print(f\" name is {self.name}  from  address {self.address}\")\n",
    "        \n",
    "obj=person(\"sara\",\"78\",\"rishikesh\")\n",
    "obj.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9a81319a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "car brand is Toyoto of year 2024 and model name is Lexus\n"
     ]
    }
   ],
   "source": [
    "class car:\n",
    "    def __init__(self,b,y,m):\n",
    "        self.brand=b\n",
    "        self.year=y\n",
    "        self.model=m\n",
    "    def details(self):\n",
    "        print(f\"car brand is {self.brand} of year {self.year} and model name is {self.model}\")\n",
    "        \n",
    "obj=car(\"Toyoto\",2024,\"Lexus\")\n",
    "obj.details()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b75c7669",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " hello I am teddy\n"
     ]
    }
   ],
   "source": [
    "class dog:\n",
    "    def __init__(self,n,a):\n",
    "        self.name=n\n",
    "        self.age=a\n",
    "    def bark(self):\n",
    "        print(f\" hello I am {self.name}\")\n",
    "        \n",
    "        \n",
    "obj=dog(\"teddy\",74)\n",
    "obj.bark()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b1d26f69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "good morning \n",
      "hello Mahima \n",
      "thank you \n"
     ]
    }
   ],
   "source": [
    "# decorator\n",
    "\n",
    "def decorator(func):\n",
    "    def wrapper(*args,**kwargs):\n",
    "        print(\"good morning \")\n",
    "        func(*args,**kwargs)\n",
    "        print(\"thank you \")\n",
    "    return wrapper\n",
    "@decorator\n",
    "def say_hello():\n",
    "    print(\"hello Mahima \")\n",
    "    \n",
    "say_hello()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dcf0a92b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the number of arguments passes multiply,(3, 2),{}\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "def value(func):\n",
    "    def wrapper(*args,**kwargs):\n",
    "        print(f\"the number of arguments passes {func.__name__},{args},{kwargs}\")\n",
    "        return func(*args,**kwargs)\n",
    "    return wrapper\n",
    "\n",
    "@value\n",
    "\n",
    "\n",
    "def multiply(x,y):\n",
    "    return x*y\n",
    "\n",
    "\n",
    "result=multiply(3,2)\n",
    "print(result)\n",
    "\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "32323eb1",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'accesss' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 18\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;129m@authenticate\u001b[39m\n\u001b[0;32m     12\u001b[0m \n\u001b[0;32m     13\u001b[0m \n\u001b[0;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21maccess\u001b[39m(username,password):\n\u001b[0;32m     15\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myou are allowed to access the data \u001b[39m\u001b[38;5;132;01m{\u001b[39;00musername\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m---> 18\u001b[0m accesss()\n",
      "\u001b[1;31mNameError\u001b[0m: name 'accesss' is not defined"
     ]
    }
   ],
   "source": [
    "def authenticate(func):\n",
    "    def wrapper(*args,**kwargs):\n",
    "        username=input(\"enter your name-> \")\n",
    "        password=int(input(\"enter your password-> \"))\n",
    "        if(username==\"mahima\" and password==1236):\n",
    "            return func(userename,password,*args,**kwargs)\n",
    "        else:\n",
    "            print(\"you are not allowed to login \")\n",
    "    return wrapper\n",
    "    \n",
    "@authenticate\n",
    "\n",
    "\n",
    "def access(username,password):\n",
    "    print(f\"you are allowed to access the data {username}\")\n",
    "    \n",
    "    \n",
    "accesss()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca8ef7b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def authenticate(func):\n",
    "    def wrapper(*args,**kwargs):\n",
    "       # print('propositional arguments are ',args)\n",
    "       # print('keywords arguments are ',kwargs)\n",
    "        username=input(\"enter username-> \")\n",
    "        password=input(\" enter password-> \")\n",
    "        if(username=='mahima' and password==1235):\n",
    "            return func(username,password,*args,**kwargs)\n",
    "        else:\n",
    "            print('you are not allowed to autinticate')\n",
    "    return wrapper\n",
    "@authenticate\n",
    "\n",
    "def sensitive_data(username,password,*args,**kwargs):\n",
    "    print(f\"you are eligible to access data with username {username}\")\n",
    "    print(\" additional arguments \",args)\n",
    "    print(\" keyword arguments \",kwargs)\n",
    "sensitive_data(12,3,4,name=\"akriti\",age=20)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "342a97b7",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() should return None, not 'str'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 17\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrive\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m     13\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m i am driving a bike of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbrand\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m---> 17\u001b[0m c\u001b[38;5;241m=\u001b[39mcar(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mverna\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(c\u001b[38;5;241m.\u001b[39mdrive())\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28mprint\u001b[39m(c\u001b[38;5;241m.\u001b[39mbrand)\n",
      "\u001b[1;31mTypeError\u001b[0m: __init__() should return None, not 'str'"
     ]
    }
   ],
   "source": [
    "class vehicle:\n",
    "    def __init__(self,brand):\n",
    "        self.brand=brand\n",
    "        return \"i am driving a car \"\n",
    "    \n",
    "    \n",
    "class car(vehicle):\n",
    "    def  drive(self):\n",
    "        return f\"I am driving a car of {self.brand}\"\n",
    "\n",
    "class bike(vehicle):\n",
    "    def drive(self):\n",
    "        return f\" i am driving a bike of {self.brand}\"\n",
    "    \n",
    "    \n",
    "    \n",
    "c=car(\"verna\")\n",
    "print(c.drive())\n",
    "print(c.brand)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97903725",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am a dog \n",
      " i am a cat\n"
     ]
    }
   ],
   "source": [
    "class animal:\n",
    "    def __init__(self,name):\n",
    "        self.name=name\n",
    "        \n",
    "    def sound(self):\n",
    "        pass\n",
    "    \n",
    "class dog(animal):\n",
    "    def sound(self):\n",
    "        return \"i am a dog \"\n",
    "    \n",
    "class cat(animal):\n",
    "    def sound(self):\n",
    "        return \" i am a cat\"\n",
    "    \n",
    "d=dog(\"tomm\")\n",
    "print(d.sound())\n",
    "\n",
    "c=cat(\"kitty\")\n",
    "print(c.sound())\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7c82a3d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ameena\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "class Parent:\n",
    "    def __init__(self,name):\n",
    "        self.name=name\n",
    "        \n",
    "class Child(Parent):\n",
    "    def __init__(self,name,age):\n",
    "        super().__init__(name)\n",
    "        self.age=age\n",
    "        \n",
    "        \n",
    "child=Child(\"ameena\",20)\n",
    "print(child.name)\n",
    "print(child.age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af2b90d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " name is mahima salary 5000\n",
      " name is mahi salary 5800000 department hr\n"
     ]
    }
   ],
   "source": [
    "class employee:\n",
    "    def __init__(self,name,salary):\n",
    "        self.name=name\n",
    "        self.salary=salary\n",
    "        \n",
    "        \n",
    "    def display(self):\n",
    "        return f\" name is {self.name} salary {self.salary}\"\n",
    "class Manager(employee):\n",
    "    def __init__(self,name,salary,dept):\n",
    "        super().__init__(name,salary)\n",
    "        self.dept=dept\n",
    "        \n",
    "        \n",
    "    def display(self):\n",
    "        return f\" name is {self.name} salary {self.salary} department {self.dept}\"\n",
    "e=employee(\"mahima\",5000)\n",
    "print(e.display())\n",
    "\n",
    "m=Manager(\"mahi\",5800000,\"hr\")\n",
    "\n",
    "\n",
    "print(m.display())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da81a0a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "my name is mahima\n"
     ]
    }
   ],
   "source": [
    "# private\n",
    "\n",
    "class My_class:\n",
    "    def __init__(self):\n",
    "        self.public_var=100\n",
    "        \n",
    "    def public(self):\n",
    "        return \"my name is mahima\"\n",
    "    \n",
    "obj=My_class()\n",
    "print(obj.public_var)\n",
    "print(obj.public())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "06775324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'My_class' object has no attribute '_My_class__private_method'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 10\u001b[0m\n\u001b[0;32m      8\u001b[0m obj\u001b[38;5;241m=\u001b[39mMy_class()\n\u001b[0;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39m_My_class__private)\n\u001b[1;32m---> 10\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39m_My_class__private_method())\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'My_class' object has no attribute '_My_class__private_method'"
     ]
    }
   ],
   "source": [
    "class My_class:\n",
    "    def __init__(self):\n",
    "        self.__private=100\n",
    "        \n",
    "    def private_method(self):\n",
    "        return \"my name is mahima\"\n",
    "    \n",
    "obj=My_class()\n",
    "print(obj._My_class__private)\n",
    "print(obj._My_class__private_method())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8fa7bd6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'subclass' object has no attribute '_protected_method'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 14\u001b[0m\n\u001b[0;32m     12\u001b[0m obj\u001b[38;5;241m=\u001b[39msubclass()\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39m_protected)\n\u001b[1;32m---> 14\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39m_protected_method())\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdisplay())\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'subclass' object has no attribute '_protected_method'"
     ]
    }
   ],
   "source": [
    "class My_class:\n",
    "    def __init__(self):\n",
    "        self._protected=100\n",
    "        \n",
    "    def protected_method(self):\n",
    "        return \"my name is mahima\"\n",
    "    \n",
    "class subclass(My_class):\n",
    "    def display(self):\n",
    "        return  self._protected\n",
    "    \n",
    "obj=subclass()\n",
    "print(obj._protected)\n",
    "print(obj._protected_method())\n",
    "print(obj.display())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0c55728",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hellooooo \n"
     ]
    }
   ],
   "source": [
    "file5=open(r\"C:\\Users\\91787\\Desktop\\file5.txt\",\"r\")\n",
    "content=file5.read()\n",
    "print(content)\n",
    "file5.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef329a4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "947cf797",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e613193",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81c6e37d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(r\"C:\\Users\\91787\\Desktop\\file5.txt\",\"a\") as file5:\n",
    "    file5.write(\" i am a third year btech student \")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4f993cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e567bf3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def user_choice():\n",
    "    user_choice=input(\"enter your choice (stone ,paper, scissor)->\").lower()\n",
    "    while user_choice not in [\"stone\",\"paper\",\"scissor\"]:\n",
    "        print(\"invalid input please choose again \")\n",
    "        user_choice=input(\"enter your choice (stone ,paper, scissor)->\").lower()\n",
    "    return user_choice\n",
    "    \n",
    "    \n",
    "def computer_choice():\n",
    "    return random.choice([\"stone\",\"paper\",\"scissor\"])\n",
    "\n",
    "\n",
    "def winner(user_input,computer_input):\n",
    "    if(user_input==computer_input):\n",
    "        return \"match is tie \"\n",
    "    elif((user_input==\"stone\" and computer_input==\"scissor\") or (user_input==\"paper\" and computer_input==\"stone\") or (user_input==\"scissor\" and computer_input==\"paper\")):\n",
    "        \n",
    "        print(\"user win the game \")\n",
    "    else:\n",
    "        return \"sorry you lost \"\n",
    "\n",
    "    \n",
    "def main():\n",
    "    print(\"welcome to stone paper scissor game \")\n",
    "    while(True):\n",
    "        user_choice=user_choice()\n",
    "        computer_choice=computer_choice()\n",
    "        print(\"you choose \", user_choice)\n",
    "        print(\"computer choice \",computer_choice)\n",
    "        winner(user_choice,computer_choice)\n",
    "        play_again=input(\"do you want to play again enter yes or no->\").lower()\n",
    "        if(play_again==\"no\"):\n",
    "            print(\"thanks for playing \")\n",
    "            break\n",
    "            \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "if __name__==\"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dabf034",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter your account number 1234\n",
      "enter your initial balance 100\n",
      "please set your ATM pin 123\n",
      "your account details are : 1234\n"
     ]
    }
   ],
   "source": [
    "class Bank_account:\n",
    "    def __init__(self,account_nbr,balance):\n",
    "        self.account_nbr=account_nbr\n",
    "        self._balance=balance\n",
    "        self.__pin=pin\n",
    "        \n",
    "    def deposite(self,amount):\n",
    "        self._balance+=amount\n",
    "        print(f\"deposited amount {amount} and new amount={self._balance}\")\n",
    "        \n",
    "    def withdraw(self,amount,pin):\n",
    "        if pin==self.__pin:\n",
    "            if amount<=self._balance:\n",
    "                self._balance-=amount\n",
    "                print(f\"the withdrawal amount= {amount} and new amount= {self._balance}\")\n",
    "            else:\n",
    "                print(\"your account balance is too low please add some money \")\n",
    "        else:\n",
    "            print(\"wrong atm pin please check again\")\n",
    "            \n",
    "            \n",
    "account_nbr=int(input(\"enter your account number \"))\n",
    "balance=float(input(\"enter your initial balance \"))\n",
    "pin=int(input (\"please set your ATM pin \"))\n",
    "\n",
    "account=Bank_account(account_nbr,balance)\n",
    "print(f\"your account details are : {account_nbr}\")\n",
    "\n",
    "deposite_amount=float(input(\"enter your amount to deposite \"))\n",
    "account.deposite(deposite_amount)\n",
    "\n",
    "withdraw_amount=float(input(\"enter amount to withdraw \"))\n",
    "account.withdraw(withdraw_amount,pin)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5054d499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27385176",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Bank_account:\n",
    "    def __init__(self,account_nbr,balance):\n",
    "        self.account_nbr=account_nbr#public\n",
    "        self._balance=balance#protected\n",
    "        self.__pin=pin#private\n",
    "        \n",
    "    def deposite(self,amount):\n",
    "        self._balance+=amount\n",
    "        print(f\"deposited amount {amount} and new amount={self._balance}\")\n",
    "        \n",
    "    def withdraw(self,amount,pin):\n",
    "        if pin==self.__pin:\n",
    "            if amount<=self._balance:\n",
    "                self._balance-=amount\n",
    "                print(f\"the withdrawal amount= {amount} and new amount= {self._balance}\")\n",
    "            else:\n",
    "                print(\"your account balance is too low please add some money \")\n",
    "        else:\n",
    "            print(\"wrong atm pin please check again\")\n",
    "            \n",
    "account_nbr=int(input(\"enter your account number \"))\n",
    "balance=float(input(\"enter your initial balance \"))\n",
    "pin=int(input (\"please set your ATM pin \"))\n",
    "\n",
    "account=Bank_account(account_nbr,balance)\n",
    "print(f\"your account details are : {account_nbr}\")\n",
    "\n",
    "deposite_amount=float(input(\"enter your amount to deposite \"))\n",
    "account.deposite(deposite_amount)\n",
    "\n",
    "withdraw_amount=float(input(\"enter amount to withdraw \"))\n",
    "account.withdraw(withdraw_amount,pin)            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6f524a9d",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1876771187.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[1], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    1. Write a program to simulate an ATM machine using conditional statements. Your program should offer options for depositing money, withdrawing money, and checking the balance.\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "1. Write a program to simulate an ATM machine using conditional statements. Your program should offer options for depositing money, withdrawing money, and checking the balance. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce0912f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello how you are?\n",
      " i am a dog\n"
     ]
    }
   ],
   "source": [
    "class Animal:\n",
    "    def hello(self):\n",
    "        print(\"hello how you are?\")\n",
    "    \n",
    "    \n",
    "class dog(Animal):\n",
    "    def hi(self):\n",
    "        print(\" i am a dog\")\n",
    "        \n",
    "d=dog()\n",
    "d.hello()\n",
    "d.hi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4cefb428",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am a father\n",
      "i am a child\n",
      "i am a mother\n"
     ]
    }
   ],
   "source": [
    "# multilevel inheritance\n",
    "class Father:\n",
    "    def skills(self):\n",
    "        print(\"i am a father\")\n",
    "        \n",
    "class Mother:\n",
    "    def knowledge(self):\n",
    "        print(\"i am a mother\")\n",
    "        \n",
    "        \n",
    "        \n",
    "class child(Father,Mother):\n",
    "    def hobbies(self):\n",
    "        print(\"i am a child\")\n",
    "        \n",
    "        \n",
    "        \n",
    "c=child()\n",
    "c.skills()\n",
    "c.hobbies()\n",
    "c.knowledge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ede6a51c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " i am a father\n",
      "i am a child\n"
     ]
    }
   ],
   "source": [
    "# multilevel inheritance\n",
    "\n",
    "class Grandfather:\n",
    "    def __init__(self):\n",
    "        print(\"i am a grandfather\")\n",
    "        \n",
    "class Father(Grandfather):\n",
    "    def __init__(self):\n",
    "        print(\" i am a father\")\n",
    "    \n",
    "class child(Father):\n",
    "    def display(self):\n",
    "        print(\"i am a child\")\n",
    "        \n",
    "        \n",
    "c=child()\n",
    "c.display()\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4e64cbb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am a animal\n",
      "i am a cat\n"
     ]
    }
   ],
   "source": [
    "class Animal:\n",
    "    def eat(self):\n",
    "        print(\"i am a animal\")\n",
    "        \n",
    "class Dog(Animal):\n",
    "    def hello(self):\n",
    "        print(\"i am a dog\")\n",
    "        \n",
    "        \n",
    "class Cat(Animal):\n",
    "    def hi(self):\n",
    "        print(\"i am a cat\")\n",
    "    \n",
    "    \n",
    "c=Cat()\n",
    "d=Dog()\n",
    "\n",
    "c.eat()\n",
    "c.hi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7737310d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9df11d82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i am dog\n"
     ]
    }
   ],
   "source": [
    "class Animal:\n",
    "    def Make_sound(self):\n",
    "        pass\n",
    "class Dog(Animal):\n",
    "    \n",
    "    def Make_sound(self):\n",
    "        print(\"i am dog\")\n",
    "        \n",
    "class Cat(Animal):\n",
    "    def Make_sound(self):\n",
    "        print(\"i am cat\")\n",
    "        \n",
    "\n",
    "\n",
    "d=Dog()\n",
    "c=Cat()\n",
    "d.Make_sound()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "80f67167",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter radius of circle->5\n",
      "area is  24\n",
      "area is  78.5\n"
     ]
    }
   ],
   "source": [
    "class shape:\n",
    "    def area(self):\n",
    "        pass\n",
    "    \n",
    "class rectangle(shape):\n",
    "    def __init__(self,l,b):\n",
    "        self.l=l\n",
    "        self.b=b\n",
    "        \n",
    "    def area(self):\n",
    "        return self.l*self.b\n",
    "    \n",
    "    \n",
    "class circle(shape):\n",
    "    def __init__(self,r):\n",
    "        self.r=int(input(\"enter radius of circle->\"))\n",
    "        \n",
    "    def area(self):\n",
    "        return 3.14*self.r*self.r\n",
    "    \n",
    "s=[rectangle(4,6), circle(5)]\n",
    "for s in s:\n",
    "    print(\"area is \", s.area())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c3f1abfe",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'int' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[27], line 22\u001b[0m\n\u001b[0;32m     17\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39msalary()\u001b[38;5;241m+\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbonus\n\u001b[0;32m     21\u001b[0m emp\u001b[38;5;241m=\u001b[39mManager(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124makira\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;241m450000\u001b[39m,\u001b[38;5;241m1000\u001b[39m)\n\u001b[1;32m---> 22\u001b[0m \u001b[38;5;28mprint\u001b[39m(emp\u001b[38;5;241m.\u001b[39msalary())\n",
      "\u001b[1;31mTypeError\u001b[0m: 'int' object is not callable"
     ]
    }
   ],
   "source": [
    "class Employee:\n",
    "    def __init__(self,name,salary):\n",
    "        self.name=name\n",
    "        self.salary=salary\n",
    "        \n",
    "    def salary(self):\n",
    "        return self.salary\n",
    "    \n",
    "    \n",
    "class Manager(Employee):\n",
    "    def __init__(self,name,salary,bonus):\n",
    "        super().__init__(name,salary)\n",
    "        self.bonus=bonus\n",
    "        \n",
    "        \n",
    "    def salary(self):\n",
    "        return super().salary()+self.bonus\n",
    "    \n",
    "    \n",
    "    \n",
    "emp=Manager(\"akira\",450000,1000)\n",
    "print(emp.salary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3838fa84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'car' object has no attribute 'get_model'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 36\u001b[0m\n\u001b[0;32m     33\u001b[0m obj\u001b[38;5;241m=\u001b[39mcar(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmaruti\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msuv\u001b[39m\u001b[38;5;124m\"\u001b[39m,\u001b[38;5;241m2012\u001b[39m)\n\u001b[0;32m     35\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39mget_make())\n\u001b[1;32m---> 36\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39mget_model())\n\u001b[0;32m     37\u001b[0m \u001b[38;5;28mprint\u001b[39m(obj\u001b[38;5;241m.\u001b[39mget_year())\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'car' object has no attribute 'get_model'"
     ]
    }
   ],
   "source": [
    "class car:\n",
    "    def __init__(self,make,model,year):\n",
    "        self.__make=make\n",
    "        self.__model=model\n",
    "        self.__year=year\n",
    "        \n",
    "    def get_make(self):\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "     def set_make(self):\n",
    "            self.__make=make\n",
    "            \n",
    "    def set_model(self):\n",
    "        self.__model=model\n",
    "        \n",
    "    def set_year(self):\n",
    "        self.__year=year\n",
    "        \n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "obj=car(\"maruti\",\"suv\",2012)\n",
    "\n",
    "print(obj.get_make())\n",
    "print(obj.get_model())\n",
    "print(obj.get_year())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ccfd8770",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mahima\n",
      "21\n",
      "india\n",
      "\n",
      "Sara\n",
      "45\n",
      "Japan\n"
     ]
    }
   ],
   "source": [
    "class person:\n",
    "    def __init__(self,name,age,country):\n",
    "        self.__name=name\n",
    "        self.__age=age\n",
    "        self.__country=country\n",
    "        \n",
    "        \n",
    "# getter method\n",
    "    def get_name(self):\n",
    "        return self.__name\n",
    "    \n",
    "    def get_age(self):\n",
    "        return self.__age\n",
    "    \n",
    "    \n",
    "    def get_country(self):\n",
    "        return self.__country\n",
    "    \n",
    "    \n",
    "# setter method\n",
    "\n",
    "    def set_name(self,name):\n",
    "        self.__name=name\n",
    "        \n",
    "    def set_age(self,age):\n",
    "        self.__age=age\n",
    "        \n",
    "        \n",
    "    def set_country(self,country):\n",
    "        self.__country=country\n",
    "        \n",
    "p=person(\"Mahima\",21,\"india\")        \n",
    "print(p.get_name())\n",
    "print(p.get_age())\n",
    "print(p.get_country())\n",
    "print()\n",
    "p.set_name(\"Sara\")\n",
    "p.set_age(45)\n",
    "p.set_country(\"Japan\")\n",
    "\n",
    "print(p.get_name())\n",
    "print(p.get_age())\n",
    "print(p.get_country())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e5b9348",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'account_getter' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[1], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mBank_account\u001b[39;00m:\n\u001b[0;32m      2\u001b[0m     \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m,balance):\n\u001b[0;32m      3\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__balance\u001b[38;5;241m=\u001b[39mbalance\n",
      "Cell \u001b[1;32mIn[1], line 7\u001b[0m, in \u001b[0;36mBank_account\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m,balance):\n\u001b[0;32m      3\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__balance\u001b[38;5;241m=\u001b[39mbalance\n\u001b[1;32m----> 7\u001b[0m \u001b[38;5;129m@account_getter\u001b[39m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mbalance\u001b[39m(\u001b[38;5;28mself\u001b[39m,new_balance):\n\u001b[0;32m      9\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m(new_balance\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m):\n\u001b[0;32m     10\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__balance\u001b[38;5;241m=\u001b[39mnew_balance\n",
      "\u001b[1;31mNameError\u001b[0m: name 'account_getter' is not defined"
     ]
    }
   ],
   "source": [
    "class Bank_account:\n",
    "    def __init__(self,balance):\n",
    "        self.__balance=balance\n",
    "        \n",
    "        \n",
    "        \n",
    "    @account_getter\n",
    "    def balance(self,new_balance):\n",
    "        if(new_balance>=0):\n",
    "            self.__balance=new_balance\n",
    "        else:\n",
    "            print(\"unsufficient balance \")\n",
    "            \n",
    "            \n",
    "acc=Bank_account(1000)\n",
    "print(acc.balance)\n",
    "\n",
    "acc.balance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48a53675",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "6.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# encapsulation\n",
    "\n",
    "class Circle:\n",
    "    def __init__(self,radius):\n",
    "        self.radius=radius\n",
    "        \n",
    "    @property\n",
    "    def diameter(self):\n",
    "        return 2*self.radius\n",
    "    \n",
    "    @diameter.setter\n",
    "    def diameter(self,diameter):\n",
    "        self.radius=diameter/2\n",
    "        \n",
    "        \n",
    "circle=Circle(5)\n",
    "print(circle.diameter)\n",
    "\n",
    "circle.diameter=12\n",
    "print(circle.radius)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a6422127",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Rectangle.get_area() missing 1 required positional argument: 'self'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[13], line 16\u001b[0m\n\u001b[0;32m     12\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m2\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_length\u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_width)\n\u001b[0;32m     15\u001b[0m rectangle\u001b[38;5;241m=\u001b[39mRectangle(length\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m,width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m---> 16\u001b[0m area\u001b[38;5;241m=\u001b[39mRectangle\u001b[38;5;241m.\u001b[39mget_area()\n\u001b[0;32m     17\u001b[0m perimeter\u001b[38;5;241m=\u001b[39mRectangle\u001b[38;5;241m.\u001b[39mget_perimeter()\n\u001b[0;32m     19\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m area of rectangle = \u001b[39m\u001b[38;5;132;01m{\u001b[39;00marea\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: Rectangle.get_area() missing 1 required positional argument: 'self'"
     ]
    }
   ],
   "source": [
    "# abstraction\n",
    "\n",
    "class Rectangle:\n",
    "    def __init__(self,length,width):\n",
    "        self._length=length\n",
    "        self._width=width\n",
    "        \n",
    "    def get_area(self):\n",
    "        return (self._length*self._width)\n",
    "    \n",
    "    def get_perimeter(self):\n",
    "        return 2*(self._length+ self._width)\n",
    "    \n",
    "    \n",
    "rectangle=Rectangle(length=20,width=10)\n",
    "area=Rectangle.get_area()\n",
    "perimeter=Rectangle.get_perimeter()\n",
    "\n",
    "print(f\" area of rectangle = {area}\")\n",
    "print(f\" perimeter of rectangle +{perimeter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e96d6d58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter the expression 4+4\n",
      "the result of your calculation  8\n"
     ]
    }
   ],
   "source": [
    "class Calculator:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    \n",
    "    def evaluate_expression(self,expression):\n",
    "        result=eval(expression)\n",
    "        return result\n",
    "        \n",
    "        \n",
    "def main():\n",
    "    calc=Calculator()\n",
    "    expression=input(\"enter the expression \")\n",
    "    result=calc.evaluate_expression(expression)\n",
    "    print(\"the result of your calculation \",result)\n",
    "    \n",
    "    \n",
    "if __name__==\"__main__\":\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4afc806c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Calculator():\n",
    "    expr=input(\"enter your expression \")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b34df572",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter any number8\n",
      "8 is no is even\n"
     ]
    }
   ],
   "source": [
    "class Number_find:\n",
    "    def __init__(self):\n",
    "        pass\n",
    "    def even_or_odd(self,number):\n",
    "        if(number%2==0):\n",
    "            return \"no is even\"\n",
    "        \n",
    "        else:\n",
    "            return \"no is odd\"\n",
    "        \n",
    "number=Number_find()\n",
    "my_number=int(input(\"enter any number\"))\n",
    "result=number.even_or_odd(my_number)\n",
    "print(f\"{my_number} is {result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba8070d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter any text-> my name is khan\n",
      "number of words  4\n"
     ]
    }
   ],
   "source": [
    "class Word_counter:\n",
    "    def __init__(self,text):\n",
    "        self.text=text\n",
    "        \n",
    "    def count_words(self):\n",
    "        words=self.text.split()\n",
    "        return len(words)\n",
    "    \n",
    "    \n",
    "def main():\n",
    "    text=input(\"enter any text-> \")\n",
    "    counter=Word_counter(text)\n",
    "    word_count=counter.count_words()\n",
    "    print(\"number of words \",word_count)\n",
    "    \n",
    "if __name__==\"__main__\":\n",
    "    main()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "addfeea5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " size of stack 4\n",
      " pop operation 96\n",
      " stack is empty False\n"
     ]
    }
   ],
   "source": [
    "class Stack:\n",
    "    def __init__(self):\n",
    "        self._items=[]\n",
    "        \n",
    "    def push(self,item):\n",
    "        self._items.append(item)\n",
    "        \n",
    "        \n",
    "    def pop(self):\n",
    "        if not self.empty():\n",
    "            return self._items.pop()\n",
    "        else:\n",
    "            return \"stack is empty\"\n",
    "        \n",
    "    def empty(self):\n",
    "        return len(self._items)==0\n",
    "    \n",
    "    \n",
    "    def size(self):\n",
    "        return len(self._items)\n",
    "    \n",
    "    \n",
    "    \n",
    "stack=Stack()\n",
    "stack.push(4)\n",
    "\n",
    "stack.push(100)\n",
    "stack.push(56)\n",
    "stack.push(96)\n",
    "\n",
    "\n",
    "\n",
    "print(f\" size of stack {stack.size()}\")\n",
    "\n",
    "print(f\" pop operation {stack.pop()}\")\n",
    "\n",
    "print(f\" stack is empty {stack.empty()}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4f5257ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "x=[1,2,4,5,6]\n",
    "y=[4,5,3,8,4]\n",
    "plt.plot(x,y)\n",
    "plt.title(\"simple line chart\")\n",
    "plt.xlabel(\"x - axis\")\n",
    "plt.ylabel(\"y- axis \")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c43cd5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=[23,4,5,6,7]\n",
    "y=[1,35,4,9,8]\n",
    "z=[56,8,96,3,2]\n",
    "plt.plot(x,y,label=\"line one\")\n",
    "plt.plot(y,z,label=\"line two\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "497011d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.linspace(0,10,100)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y,color=\"red\",linestyle=\"--\",linewidth=\"2\",label=\"sin(x)\")\n",
    "plt.title(\"customised line chart\")\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b19b178",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=[\"Saraa\",\"mrinal\",\"sarita\",\"sitara\"]\n",
    "age=[14,56,23,45]\n",
    "plt.bar(name,age)\n",
    "plt.title(\"simple names\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0f3eb0d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "name=[\"Saraa\",\"mrinal\",\"sarita\",\"sitara\"]\n",
    "age=[14,56,23,45]\n",
    "plt.barh(name,age,color=\"red\")\n",
    "plt.title(\"simple names\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-label\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5164571a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'y-axis')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plot\n",
    "import matplotlib.pyplot as plt\n",
    "x=[3,4,5,6,7]\n",
    "y=[45,8,5,3,6]\n",
    "plt.scatter(x,y,color=\"red\",alpha=1)\n",
    "plt.title(\"simple scatter plot\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "428e9228",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram \n",
    "name=[\"Saraa\",\"mrinal\",\"sarita\",\"sitara\"]\n",
    "age=[14,56,23,45]\n",
    "plt.hist(age,color=\"red\")\n",
    "plt.title(\"simple names\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-label\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fd601618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "data=np.random.randn(1000)\n",
    "\n",
    "plt.hist(data,bins=30,color=\"skyblue\")\n",
    "plt.xlabel(\"x-axis\")\n",
    "plt.ylabel(\"y-axis\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fed4bab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "age=[24,26,5,4,56]\n",
    "name=[\"sara\",\"sarika\",\"sitara\",\"sarita\",\"amita\"]\n",
    "plt.pie(age,labels=name,autopct=\" %1.1f%%\")\n",
    "plt.title(\"simple pie chart\")\n",
    "plt.axis(\"equal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7cbcac3d",
   "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>ts</th>\n",
       "      <th>platform</th>\n",
       "      <th>ms_played</th>\n",
       "      <th>conn_country</th>\n",
       "      <th>master_metadata_track_name</th>\n",
       "      <th>master_metadata_album_artist_name</th>\n",
       "      <th>master_metadata_album_album_name</th>\n",
       "      <th>spotify_track_uri</th>\n",
       "      <th>reason_start</th>\n",
       "      <th>reason_end</th>\n",
       "      <th>shuffle</th>\n",
       "      <th>offline</th>\n",
       "      <th>offline_timestamp</th>\n",
       "      <th>incognito_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-04T10:12:23Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>7130</td>\n",
       "      <td>LK</td>\n",
       "      <td>Broke In A Minute</td>\n",
       "      <td>Tory Lanez</td>\n",
       "      <td>The New Toronto 3</td>\n",
       "      <td>spotify:track:4xqIYGwwZTEem9U8A42SPF</td>\n",
       "      <td>playbtn</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750333278</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-04T10:16:20Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>237714</td>\n",
       "      <td>LK</td>\n",
       "      <td>Bonnona</td>\n",
       "      <td>DJ JNK</td>\n",
       "      <td>Bonnona</td>\n",
       "      <td>spotify:track:6ahGShWSkIHVoBd278gYnC</td>\n",
       "      <td>clickrow</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750341690</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-04T10:17:08Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>47045</td>\n",
       "      <td>LK</td>\n",
       "      <td>Broken People (Originally Performed by Logic &amp;...</td>\n",
       "      <td>Covered Up</td>\n",
       "      <td>Chart Backing Tracks 2017, Vol. 14</td>\n",
       "      <td>spotify:track:0MONybFd78nzNA7Au93yb2</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750578721</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-04T10:19:41Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>32500</td>\n",
       "      <td>LK</td>\n",
       "      <td>ブルーバード</td>\n",
       "      <td>Ikimonogakari</td>\n",
       "      <td>Chou Ikimonobakari Tennen Kinen Members Best S...</td>\n",
       "      <td>spotify:track:2XpV9sHBexcNrz0Gyf3l18</td>\n",
       "      <td>clickrow</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750627276</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-04T10:22:57Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>197298</td>\n",
       "      <td>LK</td>\n",
       "      <td>The Bones - with Hozier</td>\n",
       "      <td>Maren Morris</td>\n",
       "      <td>The Bones</td>\n",
       "      <td>spotify:track:1yTTMcUhL7rtz08Dsgb7Qb</td>\n",
       "      <td>clickrow</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750779709</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39810</th>\n",
       "      <td>2024-01-15T16:46:58Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>184889</td>\n",
       "      <td>LK</td>\n",
       "      <td>Heading Home</td>\n",
       "      <td>Alan Walker</td>\n",
       "      <td>Heading Home</td>\n",
       "      <td>spotify:track:22O2Zdfj3jnJZDSzlDMAJp</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337034</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39811</th>\n",
       "      <td>2024-01-15T16:51:28Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>268717</td>\n",
       "      <td>LK</td>\n",
       "      <td>Dandelions - slowed + reverb</td>\n",
       "      <td>Ruth B.</td>\n",
       "      <td>Dandelions</td>\n",
       "      <td>spotify:track:6fhG2KJgwO7CrNflRaRmvp</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337219</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39812</th>\n",
       "      <td>2024-01-15T16:54:36Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>187541</td>\n",
       "      <td>LK</td>\n",
       "      <td>WAP (feat. Megan Thee Stallion)</td>\n",
       "      <td>Cardi B</td>\n",
       "      <td>WAP (feat. Megan Thee Stallion)</td>\n",
       "      <td>spotify:track:4Oun2ylbjFKMPTiaSbbCih</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337488</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39813</th>\n",
       "      <td>2024-01-15T16:59:11Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>274560</td>\n",
       "      <td>LK</td>\n",
       "      <td>春夏秋冬</td>\n",
       "      <td>sumika</td>\n",
       "      <td>Chime</td>\n",
       "      <td>spotify:track:3TQKZoYr4tH2YeJ02bcw1m</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337676</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39814</th>\n",
       "      <td>2024-01-16T03:28:03Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>32888</td>\n",
       "      <td>LK</td>\n",
       "      <td>TO THE MOON</td>\n",
       "      <td>JNR CHOI</td>\n",
       "      <td>TO THE MOON</td>\n",
       "      <td>spotify:track:5vUnjhBzRJJIAOJPde6zDx</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>logout</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337951</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39815 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ts                            platform  ms_played  \\\n",
       "0      2021-09-04T10:12:23Z  Windows 10 (10.0.22000; x64; AppX)       7130   \n",
       "1      2021-09-04T10:16:20Z  Windows 10 (10.0.22000; x64; AppX)     237714   \n",
       "2      2021-09-04T10:17:08Z  Windows 10 (10.0.22000; x64; AppX)      47045   \n",
       "3      2021-09-04T10:19:41Z  Windows 10 (10.0.22000; x64; AppX)      32500   \n",
       "4      2021-09-04T10:22:57Z  Windows 10 (10.0.22000; x64; AppX)     197298   \n",
       "...                     ...                                 ...        ...   \n",
       "39810  2024-01-15T16:46:58Z                                 ios     184889   \n",
       "39811  2024-01-15T16:51:28Z                                 ios     268717   \n",
       "39812  2024-01-15T16:54:36Z                                 ios     187541   \n",
       "39813  2024-01-15T16:59:11Z                                 ios     274560   \n",
       "39814  2024-01-16T03:28:03Z                                 ios      32888   \n",
       "\n",
       "      conn_country                         master_metadata_track_name  \\\n",
       "0               LK                                  Broke In A Minute   \n",
       "1               LK                                            Bonnona   \n",
       "2               LK  Broken People (Originally Performed by Logic &...   \n",
       "3               LK                                             ブルーバード   \n",
       "4               LK                            The Bones - with Hozier   \n",
       "...            ...                                                ...   \n",
       "39810           LK                                       Heading Home   \n",
       "39811           LK                       Dandelions - slowed + reverb   \n",
       "39812           LK                    WAP (feat. Megan Thee Stallion)   \n",
       "39813           LK                                               春夏秋冬   \n",
       "39814           LK                                        TO THE MOON   \n",
       "\n",
       "      master_metadata_album_artist_name  \\\n",
       "0                            Tory Lanez   \n",
       "1                                DJ JNK   \n",
       "2                            Covered Up   \n",
       "3                         Ikimonogakari   \n",
       "4                          Maren Morris   \n",
       "...                                 ...   \n",
       "39810                       Alan Walker   \n",
       "39811                           Ruth B.   \n",
       "39812                           Cardi B   \n",
       "39813                            sumika   \n",
       "39814                          JNR CHOI   \n",
       "\n",
       "                        master_metadata_album_album_name  \\\n",
       "0                                      The New Toronto 3   \n",
       "1                                                Bonnona   \n",
       "2                     Chart Backing Tracks 2017, Vol. 14   \n",
       "3      Chou Ikimonobakari Tennen Kinen Members Best S...   \n",
       "4                                              The Bones   \n",
       "...                                                  ...   \n",
       "39810                                       Heading Home   \n",
       "39811                                         Dandelions   \n",
       "39812                    WAP (feat. Megan Thee Stallion)   \n",
       "39813                                              Chime   \n",
       "39814                                        TO THE MOON   \n",
       "\n",
       "                          spotify_track_uri reason_start reason_end  shuffle  \\\n",
       "0      spotify:track:4xqIYGwwZTEem9U8A42SPF      playbtn    endplay    False   \n",
       "1      spotify:track:6ahGShWSkIHVoBd278gYnC     clickrow  trackdone    False   \n",
       "2      spotify:track:0MONybFd78nzNA7Au93yb2    trackdone    endplay    False   \n",
       "3      spotify:track:2XpV9sHBexcNrz0Gyf3l18     clickrow    endplay    False   \n",
       "4      spotify:track:1yTTMcUhL7rtz08Dsgb7Qb     clickrow  trackdone    False   \n",
       "...                                     ...          ...        ...      ...   \n",
       "39810  spotify:track:22O2Zdfj3jnJZDSzlDMAJp    trackdone  trackdone     True   \n",
       "39811  spotify:track:6fhG2KJgwO7CrNflRaRmvp    trackdone  trackdone     True   \n",
       "39812  spotify:track:4Oun2ylbjFKMPTiaSbbCih    trackdone  trackdone     True   \n",
       "39813  spotify:track:3TQKZoYr4tH2YeJ02bcw1m    trackdone  trackdone     True   \n",
       "39814  spotify:track:5vUnjhBzRJJIAOJPde6zDx    trackdone     logout     True   \n",
       "\n",
       "       offline  offline_timestamp  incognito_mode  \n",
       "0        False      1630750333278           False  \n",
       "1        False      1630750341690           False  \n",
       "2        False      1630750578721           False  \n",
       "3        False      1630750627276           False  \n",
       "4        False      1630750779709           False  \n",
       "...        ...                ...             ...  \n",
       "39810    False         1705337034           False  \n",
       "39811    False         1705337219           False  \n",
       "39812    False         1705337488           False  \n",
       "39813    False         1705337676           False  \n",
       "39814    False         1705337951           False  \n",
       "\n",
       "[39815 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "df=pd.read_json(\"clean_data.json\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec7f9471",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>platform</th>\n",
       "      <th>ms_played</th>\n",
       "      <th>conn_country</th>\n",
       "      <th>master_metadata_track_name</th>\n",
       "      <th>master_metadata_album_artist_name</th>\n",
       "      <th>master_metadata_album_album_name</th>\n",
       "      <th>spotify_track_uri</th>\n",
       "      <th>reason_start</th>\n",
       "      <th>reason_end</th>\n",
       "      <th>shuffle</th>\n",
       "      <th>offline</th>\n",
       "      <th>offline_timestamp</th>\n",
       "      <th>incognito_mode</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-09-04T10:12:23Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>7130</td>\n",
       "      <td>LK</td>\n",
       "      <td>Broke In A Minute</td>\n",
       "      <td>Tory Lanez</td>\n",
       "      <td>The New Toronto 3</td>\n",
       "      <td>spotify:track:4xqIYGwwZTEem9U8A42SPF</td>\n",
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       "      <td>1630750333278</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-09-04T10:16:20Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>237714</td>\n",
       "      <td>LK</td>\n",
       "      <td>Bonnona</td>\n",
       "      <td>DJ JNK</td>\n",
       "      <td>Bonnona</td>\n",
       "      <td>spotify:track:6ahGShWSkIHVoBd278gYnC</td>\n",
       "      <td>clickrow</td>\n",
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       "      <td>False</td>\n",
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       "      <td>1630750341690</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-09-04T10:17:08Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>47045</td>\n",
       "      <td>LK</td>\n",
       "      <td>Broken People (Originally Performed by Logic &amp;...</td>\n",
       "      <td>Covered Up</td>\n",
       "      <td>Chart Backing Tracks 2017, Vol. 14</td>\n",
       "      <td>spotify:track:0MONybFd78nzNA7Au93yb2</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750578721</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-09-04T10:19:41Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>32500</td>\n",
       "      <td>LK</td>\n",
       "      <td>ブルーバード</td>\n",
       "      <td>Ikimonogakari</td>\n",
       "      <td>Chou Ikimonobakari Tennen Kinen Members Best S...</td>\n",
       "      <td>spotify:track:2XpV9sHBexcNrz0Gyf3l18</td>\n",
       "      <td>clickrow</td>\n",
       "      <td>endplay</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750627276</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-04T10:22:57Z</td>\n",
       "      <td>Windows 10 (10.0.22000; x64; AppX)</td>\n",
       "      <td>197298</td>\n",
       "      <td>LK</td>\n",
       "      <td>The Bones - with Hozier</td>\n",
       "      <td>Maren Morris</td>\n",
       "      <td>The Bones</td>\n",
       "      <td>spotify:track:1yTTMcUhL7rtz08Dsgb7Qb</td>\n",
       "      <td>clickrow</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1630750779709</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     ts                            platform  ms_played  \\\n",
       "0  2021-09-04T10:12:23Z  Windows 10 (10.0.22000; x64; AppX)       7130   \n",
       "1  2021-09-04T10:16:20Z  Windows 10 (10.0.22000; x64; AppX)     237714   \n",
       "2  2021-09-04T10:17:08Z  Windows 10 (10.0.22000; x64; AppX)      47045   \n",
       "3  2021-09-04T10:19:41Z  Windows 10 (10.0.22000; x64; AppX)      32500   \n",
       "4  2021-09-04T10:22:57Z  Windows 10 (10.0.22000; x64; AppX)     197298   \n",
       "\n",
       "  conn_country                         master_metadata_track_name  \\\n",
       "0           LK                                  Broke In A Minute   \n",
       "1           LK                                            Bonnona   \n",
       "2           LK  Broken People (Originally Performed by Logic &...   \n",
       "3           LK                                             ブルーバード   \n",
       "4           LK                            The Bones - with Hozier   \n",
       "\n",
       "  master_metadata_album_artist_name  \\\n",
       "0                        Tory Lanez   \n",
       "1                            DJ JNK   \n",
       "2                        Covered Up   \n",
       "3                     Ikimonogakari   \n",
       "4                      Maren Morris   \n",
       "\n",
       "                    master_metadata_album_album_name  \\\n",
       "0                                  The New Toronto 3   \n",
       "1                                            Bonnona   \n",
       "2                 Chart Backing Tracks 2017, Vol. 14   \n",
       "3  Chou Ikimonobakari Tennen Kinen Members Best S...   \n",
       "4                                          The Bones   \n",
       "\n",
       "                      spotify_track_uri reason_start reason_end  shuffle  \\\n",
       "0  spotify:track:4xqIYGwwZTEem9U8A42SPF      playbtn    endplay    False   \n",
       "1  spotify:track:6ahGShWSkIHVoBd278gYnC     clickrow  trackdone    False   \n",
       "2  spotify:track:0MONybFd78nzNA7Au93yb2    trackdone    endplay    False   \n",
       "3  spotify:track:2XpV9sHBexcNrz0Gyf3l18     clickrow    endplay    False   \n",
       "4  spotify:track:1yTTMcUhL7rtz08Dsgb7Qb     clickrow  trackdone    False   \n",
       "\n",
       "   offline  offline_timestamp  incognito_mode  \n",
       "0    False      1630750333278           False  \n",
       "1    False      1630750341690           False  \n",
       "2    False      1630750578721           False  \n",
       "3    False      1630750627276           False  \n",
       "4    False      1630750779709           False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "533df239",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 39815 entries, 0 to 39814\n",
      "Data columns (total 14 columns):\n",
      " #   Column                             Non-Null Count  Dtype \n",
      "---  ------                             --------------  ----- \n",
      " 0   ts                                 39815 non-null  object\n",
      " 1   platform                           39815 non-null  object\n",
      " 2   ms_played                          39815 non-null  int64 \n",
      " 3   conn_country                       39815 non-null  object\n",
      " 4   master_metadata_track_name         39815 non-null  object\n",
      " 5   master_metadata_album_artist_name  39815 non-null  object\n",
      " 6   master_metadata_album_album_name   39815 non-null  object\n",
      " 7   spotify_track_uri                  39815 non-null  object\n",
      " 8   reason_start                       39815 non-null  object\n",
      " 9   reason_end                         39815 non-null  object\n",
      " 10  shuffle                            39815 non-null  bool  \n",
      " 11  offline                            39815 non-null  bool  \n",
      " 12  offline_timestamp                  39815 non-null  int64 \n",
      " 13  incognito_mode                     39815 non-null  bool  \n",
      "dtypes: bool(3), int64(2), object(9)\n",
      "memory usage: 3.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9865aa4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <td>2024-01-15T16:46:58Z</td>\n",
       "      <td>ios</td>\n",
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       "      <td>Alan Walker</td>\n",
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       "      <th>39811</th>\n",
       "      <td>2024-01-15T16:51:28Z</td>\n",
       "      <td>ios</td>\n",
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       "      <td>LK</td>\n",
       "      <td>Dandelions - slowed + reverb</td>\n",
       "      <td>Ruth B.</td>\n",
       "      <td>Dandelions</td>\n",
       "      <td>spotify:track:6fhG2KJgwO7CrNflRaRmvp</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337219</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>39812</th>\n",
       "      <td>2024-01-15T16:54:36Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>187541</td>\n",
       "      <td>LK</td>\n",
       "      <td>WAP (feat. Megan Thee Stallion)</td>\n",
       "      <td>Cardi B</td>\n",
       "      <td>WAP (feat. Megan Thee Stallion)</td>\n",
       "      <td>spotify:track:4Oun2ylbjFKMPTiaSbbCih</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337488</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39813</th>\n",
       "      <td>2024-01-15T16:59:11Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>274560</td>\n",
       "      <td>LK</td>\n",
       "      <td>春夏秋冬</td>\n",
       "      <td>sumika</td>\n",
       "      <td>Chime</td>\n",
       "      <td>spotify:track:3TQKZoYr4tH2YeJ02bcw1m</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337676</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39814</th>\n",
       "      <td>2024-01-16T03:28:03Z</td>\n",
       "      <td>ios</td>\n",
       "      <td>32888</td>\n",
       "      <td>LK</td>\n",
       "      <td>TO THE MOON</td>\n",
       "      <td>JNR CHOI</td>\n",
       "      <td>TO THE MOON</td>\n",
       "      <td>spotify:track:5vUnjhBzRJJIAOJPde6zDx</td>\n",
       "      <td>trackdone</td>\n",
       "      <td>logout</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1705337951</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         ts platform  ms_played conn_country  \\\n",
       "39810  2024-01-15T16:46:58Z      ios     184889           LK   \n",
       "39811  2024-01-15T16:51:28Z      ios     268717           LK   \n",
       "39812  2024-01-15T16:54:36Z      ios     187541           LK   \n",
       "39813  2024-01-15T16:59:11Z      ios     274560           LK   \n",
       "39814  2024-01-16T03:28:03Z      ios      32888           LK   \n",
       "\n",
       "            master_metadata_track_name master_metadata_album_artist_name  \\\n",
       "39810                     Heading Home                       Alan Walker   \n",
       "39811     Dandelions - slowed + reverb                           Ruth B.   \n",
       "39812  WAP (feat. Megan Thee Stallion)                           Cardi B   \n",
       "39813                             春夏秋冬                            sumika   \n",
       "39814                      TO THE MOON                          JNR CHOI   \n",
       "\n",
       "      master_metadata_album_album_name                     spotify_track_uri  \\\n",
       "39810                     Heading Home  spotify:track:22O2Zdfj3jnJZDSzlDMAJp   \n",
       "39811                       Dandelions  spotify:track:6fhG2KJgwO7CrNflRaRmvp   \n",
       "39812  WAP (feat. Megan Thee Stallion)  spotify:track:4Oun2ylbjFKMPTiaSbbCih   \n",
       "39813                            Chime  spotify:track:3TQKZoYr4tH2YeJ02bcw1m   \n",
       "39814                      TO THE MOON  spotify:track:5vUnjhBzRJJIAOJPde6zDx   \n",
       "\n",
       "      reason_start reason_end  shuffle  offline  offline_timestamp  \\\n",
       "39810    trackdone  trackdone     True    False         1705337034   \n",
       "39811    trackdone  trackdone     True    False         1705337219   \n",
       "39812    trackdone  trackdone     True    False         1705337488   \n",
       "39813    trackdone  trackdone     True    False         1705337676   \n",
       "39814    trackdone     logout     True    False         1705337951   \n",
       "\n",
       "       incognito_mode  \n",
       "39810           False  \n",
       "39811           False  \n",
       "39812           False  \n",
       "39813           False  \n",
       "39814           False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3caebe8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ts', 'platform', 'ms_played', 'conn_country',\n",
       "       'master_metadata_track_name', 'master_metadata_album_artist_name',\n",
       "       'master_metadata_album_album_name', 'spotify_track_uri', 'reason_start',\n",
       "       'reason_end', 'shuffle', 'offline', 'offline_timestamp',\n",
       "       'incognito_mode'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "145e958e",
   "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>ms_played</th>\n",
       "      <th>offline_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>39815.000000</td>\n",
       "      <td>3.981500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>164832.787166</td>\n",
       "      <td>1.072167e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>84677.642530</td>\n",
       "      <td>7.832921e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>128635.000000</td>\n",
       "      <td>1.688829e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>182160.000000</td>\n",
       "      <td>1.635623e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218364.000000</td>\n",
       "      <td>1.647909e+12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>613026.000000</td>\n",
       "      <td>1.665782e+12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ms_played  offline_timestamp\n",
       "count   39815.000000       3.981500e+04\n",
       "mean   164832.787166       1.072167e+12\n",
       "std     84677.642530       7.832921e+11\n",
       "min         0.000000       0.000000e+00\n",
       "25%    128635.000000       1.688829e+09\n",
       "50%    182160.000000       1.635623e+12\n",
       "75%    218364.000000       1.647909e+12\n",
       "max    613026.000000       1.665782e+12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f6e6ee83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>platform</th>\n",
       "      <th>ms_played</th>\n",
       "      <th>conn_country</th>\n",
       "      <th>master_metadata_track_name</th>\n",
       "      <th>master_metadata_album_artist_name</th>\n",
       "      <th>master_metadata_album_album_name</th>\n",
       "      <th>spotify_track_uri</th>\n",
       "      <th>reason_start</th>\n",
       "      <th>reason_end</th>\n",
       "      <th>shuffle</th>\n",
       "      <th>offline</th>\n",
       "      <th>offline_timestamp</th>\n",
       "      <th>incognito_mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39810</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39811</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39812</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39813</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39814</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39815 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          ts  platform  ms_played  conn_country  master_metadata_track_name  \\\n",
       "0      False     False      False         False                       False   \n",
       "1      False     False      False         False                       False   \n",
       "2      False     False      False         False                       False   \n",
       "3      False     False      False         False                       False   \n",
       "4      False     False      False         False                       False   \n",
       "...      ...       ...        ...           ...                         ...   \n",
       "39810  False     False      False         False                       False   \n",
       "39811  False     False      False         False                       False   \n",
       "39812  False     False      False         False                       False   \n",
       "39813  False     False      False         False                       False   \n",
       "39814  False     False      False         False                       False   \n",
       "\n",
       "       master_metadata_album_artist_name  master_metadata_album_album_name  \\\n",
       "0                                  False                             False   \n",
       "1                                  False                             False   \n",
       "2                                  False                             False   \n",
       "3                                  False                             False   \n",
       "4                                  False                             False   \n",
       "...                                  ...                               ...   \n",
       "39810                              False                             False   \n",
       "39811                              False                             False   \n",
       "39812                              False                             False   \n",
       "39813                              False                             False   \n",
       "39814                              False                             False   \n",
       "\n",
       "       spotify_track_uri  reason_start  reason_end  shuffle  offline  \\\n",
       "0                  False         False       False    False    False   \n",
       "1                  False         False       False    False    False   \n",
       "2                  False         False       False    False    False   \n",
       "3                  False         False       False    False    False   \n",
       "4                  False         False       False    False    False   \n",
       "...                  ...           ...         ...      ...      ...   \n",
       "39810              False         False       False    False    False   \n",
       "39811              False         False       False    False    False   \n",
       "39812              False         False       False    False    False   \n",
       "39813              False         False       False    False    False   \n",
       "39814              False         False       False    False    False   \n",
       "\n",
       "       offline_timestamp  incognito_mode  \n",
       "0                  False           False  \n",
       "1                  False           False  \n",
       "2                  False           False  \n",
       "3                  False           False  \n",
       "4                  False           False  \n",
       "...                  ...             ...  \n",
       "39810              False           False  \n",
       "39811              False           False  \n",
       "39812              False           False  \n",
       "39813              False           False  \n",
       "39814              False           False  \n",
       "\n",
       "[39815 rows x 14 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "790bf411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ts                                   0\n",
       "platform                             0\n",
       "ms_played                            0\n",
       "conn_country                         0\n",
       "master_metadata_track_name           0\n",
       "master_metadata_album_artist_name    0\n",
       "master_metadata_album_album_name     0\n",
       "spotify_track_uri                    0\n",
       "reason_start                         0\n",
       "reason_end                           0\n",
       "shuffle                              0\n",
       "offline                              0\n",
       "offline_timestamp                    0\n",
       "incognito_mode                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41a08a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['The New Toronto 3', 'Bonnona',\n",
       "       'Chart Backing Tracks 2017, Vol. 14', ...,\n",
       "       'Enter The Wu-Tang (36 Chambers) [Expanded Edition]',\n",
       "       'The Anthology', 'The Blixky Tape 2'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"master_metadata_album_album_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "72877a0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['LK', 'US', 'GB', 'SG'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"conn_country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "94608585",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Windows 10 (10.0.22000; x64; AppX)', 'iOS 15.0 (iPhone9,1)',\n",
       "       'Partner SCEI sony_tv;ps4;9b18101888dd42948afd0b8792122bec;;tpapi',\n",
       "       'Partner windows_tv microsoft;xbox_series_s;1fc2d01aa9074dde950e8d3dc0eb1729;;tpapi',\n",
       "       'OS X 11.6.0 [arm 2]', 'iOS 15.0.1 (iPhone9,1)',\n",
       "       'iOS 15.0.2 (iPhone9,1)', 'OS X 12.0.1 [arm 2]',\n",
       "       'iOS 15.1 (iPhone10,6)', 'iOS 15.1 (iPhone12,1)',\n",
       "       'iOS 15.2 (iPhone12,1)', 'iOS 15.2.1 (iPhone12,1)',\n",
       "       'iOS 15.3 (iPhone14,2)', 'iOS 15.3.1 (iPhone14,2)',\n",
       "       'iOS 15.4.1 (iPhone14,2)', 'iOS 16.0 (iPhone14,2)',\n",
       "       'Windows 10 (10.0.19043; x64; AppX)', 'OS X 12.1.0 [arm 2]',\n",
       "       'OS X 12.5.0 [arm 2]', 'OS X 12.5.1 [arm 2]',\n",
       "       'OS X 12.6.0 [arm 2]', 'ios', 'osx', 'playstation', 'xbox',\n",
       "       'android'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"platform\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f7a08616",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Tory Lanez', 'DJ JNK', 'Covered Up', ..., 'Wu-Tang Clan',\n",
       "       'A Tribe Called Quest', '22Gz'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"master_metadata_album_artist_name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b7e6097e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='platform',data=df)\n",
    "plt.title(\"screening platforms\")\n",
    "plt.xlabel(\"platform\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "70a9a073",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='conn_country',data=df)\n",
    "plt.title(\"screening platforms\")\n",
    "plt.xlabel(\"platform\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1013c526",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "master_metadata_track_name\n",
      "Gammane              84\n",
      "hot girl bummer      81\n",
      "Heartless            80\n",
      "Aradhana             78\n",
      "Falling              71\n",
      "Never Be Like You    69\n",
      "Off The Grid         69\n",
      "Bonnona              66\n",
      "Beautiful            65\n",
      "Praise God           62\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#top 10 tracks\n",
    "\n",
    "\n",
    "top_tracks=df[\"master_metadata_track_name\"].value_counts().head(10)\n",
    "print(top_tracks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "07a841eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "master_metadata_album_artist_name\n",
      "Juice WRLD     754\n",
      "Costa          667\n",
      "Drake          506\n",
      "Daddy          455\n",
      "Pop Smoke      397\n",
      "Post Malone    367\n",
      "Eminem         354\n",
      "Kanye West     351\n",
      "Roddy Ricch    348\n",
      "Lil Baby       341\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "top_artist=df[\"master_metadata_album_artist_name\"].value_counts().head(10)\n",
    "print(top_artist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d8161458",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "master_metadata_album_album_name\n",
      "Legends Never Die                        401\n",
      "Please Excuse Me for Being Antisocial    309\n",
      "Shoot For The Stars Aim For The Moon     302\n",
      "Massina                                  247\n",
      "Arabiya                                  200\n",
      "13 Reasons Why                           184\n",
      "Hollywood's Bleeding                     165\n",
      "My Turn                                  159\n",
      "BLAME IT ON BABY                         157\n",
      "Donda                                    157\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "top_albums=df[\"master_metadata_album_album_name\"].value_counts().head(10)\n",
    "print(top_albums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "504d58fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top albums\n",
    "plt.figure(figsize=(14,6))\n",
    "top_albums.plot(kind=\"bar\")\n",
    "plt.title(\"top 10 most streamed albums\")\n",
    "plt.xlabel(\"songs\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4e024edd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 artist \n",
    "plt.figure(figsize=(14,6))\n",
    "top_artist.plot(kind=\"bar\")\n",
    "plt.title(\"top 10 artist \")\n",
    "plt.xlabel(\"artist\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52689cdd",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unterminated string literal (detected at line 35) (2754659873.py, line 35)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[21], line 35\u001b[1;36m\u001b[0m\n\u001b[1;33m    Hollywood's Bleeding                     165\u001b[0m\n\u001b[1;37m             ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m unterminated string literal (detected at line 35)\n"
     ]
    }
   ],
   "source": [
    "summary\n",
    "colums:Index(['ts', 'platform', 'ms_played', 'conn_country',\n",
    "       'master_metadata_track_name', 'master_metadata_album_artist_name',\n",
    "       'master_metadata_album_album_name', 'spotify_track_uri', 'reason_start',\n",
    "       'reason_end', 'shuffle', 'offline', 'offline_timestamp',\n",
    "       'incognito_mode'],\n",
    "      dtype='object')\n",
    "unique_artist=array(['Tory Lanez', 'DJ JNK', 'Covered Up', ..., 'Wu-Tang Clan',\n",
    "       'A Tribe Called Quest', '22Gz'], dtype=object)\n",
    "unique_albums=array(['The New Toronto 3', 'Bonnona',\n",
    "       'Chart Backing Tracks 2017, Vol. 14', ...,\n",
    "       'Enter The Wu-Tang (36 Chambers) [Expanded Edition]',\n",
    "       'The Anthology', 'The Blixky Tape 2'], dtype=object)\n",
    "top 10 tracks=master_metadata_track_name\n",
    "Gammane              84\n",
    "hot girl bummer      81\n",
    "Heartless            80\n",
    "Aradhana             78\n",
    "Falling              71\n",
    "Never Be Like You    69\n",
    "Off The Grid         69\n",
    "Bonnona              66\n",
    "Beautiful            65\n",
    "Praise God           62\n",
    "Name: count, dtype: int64\n",
    "\n",
    "        \n",
    "top 10 albums=master_metadata_album_album_name\n",
    "Legends Never Die                        401\n",
    "Please Excuse Me for Being Antisocial    309\n",
    "Shoot For The Stars Aim For The Moon     302\n",
    "Massina                                  247\n",
    "Arabiya                                  200\n",
    "13 Reasons Why                           184\n",
    "Hollywood's Bleeding                     165\n",
    "My Turn                                  159\n",
    "BLAME IT ON BABY                         157\n",
    "Donda                                    157\n",
    "Name: count, dtype: int64\n",
    "        \n",
    "        \n",
    "        \n",
    "top 10 artist=master_metadata_album_artist_name\n",
    "Juice WRLD     754\n",
    "Costa          667\n",
    "Drake          506\n",
    "Daddy          455\n",
    "Pop Smoke      397\n",
    "Post Malone    367\n",
    "Eminem         354\n",
    "Kanye West     351\n",
    "Roddy Ricch    348\n",
    "Lil Baby       341\n",
    "Name: count, dtype: int64    \n",
    "        \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "feb42dab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(12,6))\n",
    "sns.histplot(df[\"ms_played\"],bins=50,kde=True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d056faec",
   "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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>@@@@@@</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>ArsiaEx</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Mahla T</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>18-Jun</td>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>SollyCl</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>488.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13320 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area_type   availability                  location  \\\n",
       "0      Super built-up  Area         19-Dec  Electronic City Phase II   \n",
       "1                Plot  Area  Ready To Move          Chikka Tirupathi   \n",
       "2            Built-up  Area  Ready To Move               Uttarahalli   \n",
       "3      Super built-up  Area  Ready To Move        Lingadheeranahalli   \n",
       "4      Super built-up  Area  Ready To Move                  Kothanur   \n",
       "...                     ...            ...                       ...   \n",
       "13315        Built-up  Area  Ready To Move                Whitefield   \n",
       "13316  Super built-up  Area  Ready To Move             Richards Town   \n",
       "13317        Built-up  Area  Ready To Move     Raja Rajeshwari Nagar   \n",
       "13318  Super built-up  Area         18-Jun           Padmanabhanagar   \n",
       "13319  Super built-up  Area  Ready To Move              Doddathoguru   \n",
       "\n",
       "            size  society total_sqft  bath  balcony   price  \n",
       "0          2 BHK  Coomee        1056   2.0      1.0   39.07  \n",
       "1      4 Bedroom  Theanmp       2600   5.0      3.0  120.00  \n",
       "2          3 BHK   @@@@@@       1440   2.0      3.0   62.00  \n",
       "3          3 BHK  Soiewre       1521   3.0      1.0   95.00  \n",
       "4          2 BHK      NaN       1200   2.0      1.0   51.00  \n",
       "...          ...      ...        ...   ...      ...     ...  \n",
       "13315  5 Bedroom  ArsiaEx       3453   4.0      0.0  231.00  \n",
       "13316      4 BHK      NaN       3600   5.0      NaN  400.00  \n",
       "13317      2 BHK  Mahla T       1141   2.0      1.0   60.00  \n",
       "13318      4 BHK  SollyCl       4689   4.0      1.0  488.00  \n",
       "13319      1 BHK      NaN        550   1.0      1.0   17.00  \n",
       "\n",
       "[13320 rows x 9 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Bengaluru_House_Data_gwec.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4da59102",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a5da66d",
   "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>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>19-Dec</td>\n",
       "      <td>Electronic City Phase II</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Coomee</td>\n",
       "      <td>1056</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>39.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plot  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Chikka Tirupathi</td>\n",
       "      <td>4 Bedroom</td>\n",
       "      <td>Theanmp</td>\n",
       "      <td>2600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>120.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Uttarahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>@@@@@@</td>\n",
       "      <td>1440</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>62.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Lingadheeranahalli</td>\n",
       "      <td>3 BHK</td>\n",
       "      <td>Soiewre</td>\n",
       "      <td>1521</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Kothanur</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>51.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              area_type   availability                  location       size  \\\n",
       "0  Super built-up  Area         19-Dec  Electronic City Phase II      2 BHK   \n",
       "1            Plot  Area  Ready To Move          Chikka Tirupathi  4 Bedroom   \n",
       "2        Built-up  Area  Ready To Move               Uttarahalli      3 BHK   \n",
       "3  Super built-up  Area  Ready To Move        Lingadheeranahalli      3 BHK   \n",
       "4  Super built-up  Area  Ready To Move                  Kothanur      2 BHK   \n",
       "\n",
       "   society total_sqft  bath  balcony   price  \n",
       "0  Coomee        1056   2.0      1.0   39.07  \n",
       "1  Theanmp       2600   5.0      3.0  120.00  \n",
       "2   @@@@@@       1440   2.0      3.0   62.00  \n",
       "3  Soiewre       1521   3.0      1.0   95.00  \n",
       "4      NaN       1200   2.0      1.0   51.00  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8f658421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13315</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Whitefield</td>\n",
       "      <td>5 Bedroom</td>\n",
       "      <td>ArsiaEx</td>\n",
       "      <td>3453</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>231.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13316</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Richards Town</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3600</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13317</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Raja Rajeshwari Nagar</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Mahla T</td>\n",
       "      <td>1141</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13318</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>18-Jun</td>\n",
       "      <td>Padmanabhanagar</td>\n",
       "      <td>4 BHK</td>\n",
       "      <td>SollyCl</td>\n",
       "      <td>4689</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13319</th>\n",
       "      <td>Super built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Doddathoguru</td>\n",
       "      <td>1 BHK</td>\n",
       "      <td>NaN</td>\n",
       "      <td>550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  area_type   availability               location       size  \\\n",
       "13315        Built-up  Area  Ready To Move             Whitefield  5 Bedroom   \n",
       "13316  Super built-up  Area  Ready To Move          Richards Town      4 BHK   \n",
       "13317        Built-up  Area  Ready To Move  Raja Rajeshwari Nagar      2 BHK   \n",
       "13318  Super built-up  Area         18-Jun        Padmanabhanagar      4 BHK   \n",
       "13319  Super built-up  Area  Ready To Move           Doddathoguru      1 BHK   \n",
       "\n",
       "       society total_sqft  bath  balcony  price  \n",
       "13315  ArsiaEx       3453   4.0      0.0  231.0  \n",
       "13316      NaN       3600   5.0      NaN  400.0  \n",
       "13317  Mahla T       1141   2.0      1.0   60.0  \n",
       "13318  SollyCl       4689   4.0      1.0  488.0  \n",
       "13319      NaN        550   1.0      1.0   17.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bc45f67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 13320 entries, 0 to 13319\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   area_type     13320 non-null  object \n",
      " 1   availability  13320 non-null  object \n",
      " 2   location      13319 non-null  object \n",
      " 3   size          13304 non-null  object \n",
      " 4   society       7819 non-null   object \n",
      " 5   total_sqft    13320 non-null  object \n",
      " 6   bath          13247 non-null  float64\n",
      " 7   balcony       12711 non-null  float64\n",
      " 8   price         13320 non-null  float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 936.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "942a1714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13247.000000</td>\n",
       "      <td>12711.000000</td>\n",
       "      <td>13320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.692610</td>\n",
       "      <td>1.584376</td>\n",
       "      <td>112.565627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.341458</td>\n",
       "      <td>0.817263</td>\n",
       "      <td>148.971674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>120.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3600.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               bath       balcony         price\n",
       "count  13247.000000  12711.000000  13320.000000\n",
       "mean       2.692610      1.584376    112.565627\n",
       "std        1.341458      0.817263    148.971674\n",
       "min        1.000000      0.000000      8.000000\n",
       "25%        2.000000      1.000000     50.000000\n",
       "50%        2.000000      2.000000     72.000000\n",
       "75%        3.000000      2.000000    120.000000\n",
       "max       40.000000      3.000000   3600.000000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1c5eec87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area_type</th>\n",
       "      <th>availability</th>\n",
       "      <th>location</th>\n",
       "      <th>size</th>\n",
       "      <th>society</th>\n",
       "      <th>total_sqft</th>\n",
       "      <th>bath</th>\n",
       "      <th>balcony</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5795</th>\n",
       "      <td>Built-up  Area</td>\n",
       "      <td>Ready To Move</td>\n",
       "      <td>Bisuvanahalli</td>\n",
       "      <td>2 BHK</td>\n",
       "      <td>Prityel</td>\n",
       "      <td>700</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           area_type   availability       location   size  society total_sqft  \\\n",
       "5795  Built-up  Area  Ready To Move  Bisuvanahalli  2 BHK  Prityel        700   \n",
       "\n",
       "      bath  balcony  price  \n",
       "5795   1.0      1.0   32.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9404f5f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['area_type', 'availability', 'location', 'size', 'society',\n",
       "       'total_sqft', 'bath', 'balcony', 'price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba7b974a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb885dec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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