practice5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "faa8b3a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SN,Name,Age,Address\n",
      "1. Hari,45,Kathmandu\n",
      "2. Santosh,44,Solukhumbu\n",
      "3. Ram,34,Tanhu\n",
      "4. Basnet,25,Sagarmatha\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "b = open(\"santosh.csv\",\"r\")\n",
    "print(b.read())\n",
    "b.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "75ca06b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>SN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "      <td>Sagarmatha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SN     Name  Age     Address\n",
       "0   1     Hari   45   Kathmandu\n",
       "1   2  Santosh   44  Solukhumbu\n",
       "2   3      Ram   34       Tanhu\n",
       "3   4   Basnet   25  Sagarmatha"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d13a867c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\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>SN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "      <td>Sagarmatha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SN     Name  Age     Address\n",
       "0   1     Hari   45   Kathmandu\n",
       "1   2  Santosh   44  Solukhumbu\n",
       "2   3      Ram   34       Tanhu\n",
       "3   4   Basnet   25  Sagarmatha"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('santosh.csv',nrows = 5)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1e9c0500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>SN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SN     Name  Age     Address\n",
       "0   1     Hari   45   Kathmandu\n",
       "1   2  Santosh   44  Solukhumbu\n",
       "2   3      Ram   34       Tanhu"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\")\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "07c6bf9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>SN</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "      <td>Sagarmatha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SN     Name  Age     Address\n",
       "1   2  Santosh   44  Solukhumbu\n",
       "2   3      Ram   34       Tanhu\n",
       "3   4   Basnet   25  Sagarmatha"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\")\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2e479e47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "      <td>Sagarmatha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Age     Address\n",
       "SN                          \n",
       "1      Hari   45   Kathmandu\n",
       "2   Santosh   44  Solukhumbu\n",
       "3       Ram   34       Tanhu\n",
       "4    Basnet   25  Sagarmatha"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e0182323",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Address</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santosh</td>\n",
       "      <td>44</td>\n",
       "      <td>Solukhumbu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "      <td>Tanhu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "      <td>Sagarmatha</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Name  Age     Address\n",
       "SN                          \n",
       "1      Hari   45   Kathmandu\n",
       "2   Santosh   44  Solukhumbu\n",
       "3       Ram   34       Tanhu\n",
       "4    Basnet   25  Sagarmatha"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\",usecols = [\"SN\",\"Name\",\"Age\",\"Address\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f303d130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SN\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4    False\n",
       "Name: Age, dtype: bool"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\")\n",
    "df['Age']>40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "324514a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Address</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Hari</td>\n",
       "      <td>45</td>\n",
       "      <td>Kathmandu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Age    Address\n",
       "SN                      \n",
       "1   Hari   45  Kathmandu"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\")\n",
    "df[df[\"Address\"] == \"Kathmandu\"] or ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5849e53e",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-418f4d4d4d18>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"santosh.csv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mindex_col\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"SN\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Address\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"Kathmandu\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"Age\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[1;36m46\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__nonzero__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1440\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1441\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__nonzero__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1442\u001b[1;33m         raise ValueError(\n\u001b[0m\u001b[0;32m   1443\u001b[0m             \u001b[1;34mf\"The truth value of a {type(self).__name__} is ambiguous. \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1444\u001b[0m             \u001b[1;34m\"Use a.empty, a.bool(), a.item(), a.any() or a.all().\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()."
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\")\n",
    "df[(df[\"Address\"] == \"Kathmandu\") and (df[\"Age\"]>46)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96150114",
   "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ram</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Basnet</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age\n",
       "SN             \n",
       "3      Ram   34\n",
       "4   Basnet   25"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"santosh.csv\",index_col = \"SN\")\n",
    "df.iloc[2:4,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "47640cde",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "fields = ['Santosh',35,'Kathmandu']\n",
    "with open('basnet.csv','a') as f:\n",
    "    x = csv.writer(f)\n",
    "    x.writerow(fields)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "282bc1c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "fields = ['Santosh',35,'Kathmandu']\n",
    "f = open('basnet.csv','a')\n",
    "x = csv.writer(f)\n",
    "x.writerow(fields)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8653103c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter n  = 2\n",
      "Enter name = Santoh\n",
      "Enter physics = 25\n",
      "Enter chemistry = 35\n",
      "Enter math = 25\n",
      "Enter english = 35\n",
      "Enter name = Hari\n",
      "Enter physics = 25\n",
      "Enter chemistry = 25\n",
      "Enter math = 26\n",
      "Enter english = 35\n",
      "[[1, 'Santoh', 25, 35, 25, 35, 120, 30.0], [2, 'Hari', 25, 25, 26, 35, 111, 27.75]]\n"
     ]
    }
   ],
   "source": [
    "final = []\n",
    "n = int(input(\"enter n  = \"))\n",
    "for i in range(1,n+1):\n",
    "    name = input(\"Enter name = \")\n",
    "    physics = int(input(\"Enter physics = \"))\n",
    "    chemistry = int(input(\"Enter chemistry = \"))\n",
    "    math = int(input(\"Enter math = \"))\n",
    "    english = int(input(\"Enter english = \"))\n",
    "    total = sum([physics,chemistry,math,english])\n",
    "    per = total/4\n",
    "    marks = [i,name,physics,chemistry,math,english,total,per]\n",
    "    final.append(marks)\n",
    "print(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03fa6c29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "f = open('final.csv','w')\n",
    "x = csv.writer(f)\n",
    "for i in final:\n",
    "    x.writerow(i)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8ff2013e",
   "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>physics</th>\n",
       "      <th>Chemistry</th>\n",
       "      <th>math</th>\n",
       "      <th>english</th>\n",
       "      <th>Total</th>\n",
       "      <th>per</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SN</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Santoh</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>25</td>\n",
       "      <td>35</td>\n",
       "      <td>120</td>\n",
       "      <td>30.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hari</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>35</td>\n",
       "      <td>111</td>\n",
       "      <td>27.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  physics  Chemistry  math  english  Total    per\n",
       "SN                                                         \n",
       "1   Santoh       25         35    25       35    120  30.00\n",
       "2     Hari       25         25    26       35    111  27.75"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"final.csv\",index_col = \"SN\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "eb969eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = {'Santosh': ['9865623658', '9865986586'], 'Ram': ['9685689575', '9856895648']}\n",
    "df = pd.DataFrame(a)\n",
    "df.to_csv('ram.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "20f4725b",
   "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>Unnamed: 0</th>\n",
       "      <th>Santosh</th>\n",
       "      <th>Ram</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9865623658</td>\n",
       "      <td>9685689575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>9865986586</td>\n",
       "      <td>9856895648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Santosh         Ram\n",
       "0           0  9865623658  9685689575\n",
       "1           1  9865986586  9856895648"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"ram.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "70b1baa0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'SN': '1', 'Name': 'Hari', 'Age': '45', 'Address': 'Kathmandu'}\n",
      "{'SN': '2', 'Name': 'Santosh', 'Age': '44', 'Address': 'Solukhumbu'}\n",
      "{'SN': '3', 'Name': 'Ram', 'Age': '34', 'Address': 'Tanhu'}\n",
      "{'SN': '4', 'Name': 'Basnet', 'Age': '25', 'Address': 'Sagarmatha'}\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "b = open(\"santosh.csv\")\n",
    "reader = csv.DictReader(b)\n",
    "for row in reader:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "03e7496a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'SN': '1', 'Name': 'Hari', 'Age': '45', 'Address': 'Kathmandu'}, {'SN': '2', 'Name': 'Santosh', 'Age': '44', 'Address': 'Solukhumbu'}, {'SN': '3', 'Name': 'Ram', 'Age': '34', 'Address': 'Tanhu'}, {'SN': '4', 'Name': 'Basnet', 'Age': '25', 'Address': 'Sagarmatha'}]\n"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "final_data = []\n",
    "b = open('santosh.csv')\n",
    "reader = csv.DictReader(b)\n",
    "for row in reader:\n",
    "    final_data.append(dict(row))\n",
    "print(final_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34245471",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'SN': '1', 'Name': 'Hari', 'Age': '45', 'Address': 'Kathmandu'},\n",
       " {'SN': '2', 'Name': 'Ramprasad', 'Age': '44', 'Address': 'Solukhumbu'},\n",
       " {'SN': '3', 'Name': 'Ram', 'Age': '34', 'Address': 'Tanhu'},\n",
       " {'SN': '4', 'Name': 'Basnet', 'Age': '25', 'Address': 'Sagarmatha'}]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data[1]['Name'] = \"Ramprasad\"\n",
    "final_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "aa57511e",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DictWriter' object has no attribute 'writerheader'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-49-373105415c00>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"santosh.csv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mwriter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDictWriter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfieldnames\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcsv_columns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriterheader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwriterow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DictWriter' object has no attribute 'writerheader'"
     ]
    }
   ],
   "source": [
    "import csv\n",
    "csv_columns = ['SN', 'Name', 'Age', 'Address']\n",
    "dict = final_data\n",
    "b = open(\"santosh.csv\",\"w\")\n",
    "writer = csv.DictWriter(b, fieldnames=csv_columns)\n",
    "writer.writerheader()\n",
    "for data in dict:\n",
    "    writer.writerow(data)\n",
    "b.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e47390a7",
   "metadata": {},
   "outputs": [
    {
     "ename": "EmptyDataError",
     "evalue": "No columns to parse from file",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mEmptyDataError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-f41d2d254b45>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'santosh.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mread_csv\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options)\u001b[0m\n\u001b[0;32m    608\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    609\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 610\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    611\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    612\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    460\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    461\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 462\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    463\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    464\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    817\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 819\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    820\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    821\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1048\u001b[0m             )\n\u001b[0;32m   1049\u001b[0m         \u001b[1;31m# error: Too many arguments for \"ParserBase\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1050\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mmapping\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# type: ignore[call-arg]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1051\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1052\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_failover_to_python\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Other\\anaconda\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1896\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1897\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1898\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1899\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1900\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mEmptyDataError\u001b[0m: No columns to parse from file"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('santosh.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d757dc61",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'mysql'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-33004ba6f486>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmysql\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconnector\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m database = mysql.connector.connect(\n\u001b[0;32m      3\u001b[0m     \u001b[0mhost\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"localhost\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0muser\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"root\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mpassword\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'mysql'"
     ]
    }
   ],
   "source": [
    "import mysql.connector\n",
    "database = mysql.connector.connect(\n",
    "    host=\"localhost\",\n",
    "    user=\"root\",\n",
    "    password=\"\",\n",
    "    database = \"class6pm\"\n",
    ")\n",
    "db = database.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80ba1292",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "database = mysql.connector.connect(\n",
    ")"
   ]
  }
 ],
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