Assignment Python (Pandas)-2-Q2.ipynb

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    "\n",
    "'''\n",
    "WAP to generate the statistical summary of all the numerical features present in the given dataframe.\n",
    "\n",
    "    eid                         ename                 stipend               position\n",
    "\n",
    "0    1                           Sid                   10000                employee\n",
    "\n",
    "1    2                          Ramesh                 10000                employee\n",
    "\n",
    "2    3                           Ron                    5000                 intern\n",
    "\n",
    "3    4                          Harry                  15000                senior_employee\n",
    "\n",
    "'''"
   ]
  },
  {
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   "id": "60174f1e",
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    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eid</th>\n",
       "      <th>stipend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.290994</td>\n",
       "      <td>4082.482905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.750000</td>\n",
       "      <td>8750.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>10000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>11250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            eid       stipend\n",
       "count  4.000000      4.000000\n",
       "mean   2.500000  10000.000000\n",
       "std    1.290994   4082.482905\n",
       "min    1.000000   5000.000000\n",
       "25%    1.750000   8750.000000\n",
       "50%    2.500000  10000.000000\n",
       "75%    3.250000  11250.000000\n",
       "max    4.000000  15000.000000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = {\"eid\":[1,2,3,4],\"ename\":[\"Sid\",\"Ramesh\",\"Ron\",\"Harry\"],\"stipend\":[10000,10000,5000,15000],\"position\":[\"employee\",\"employee\",\"intern\",\"senior_employee\"]}\n",
    "df = pd.DataFrame(data)\n",
    "df.describe()"
   ]
  }
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