{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a1b2c3d4",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e5f6a7b8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <th></th>\n",
" <th>name</th>\n",
" <th>age</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>30</td>\n",
" <td>85.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>25</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Charlie</td>\n",
" <td>35</td>\n",
" <td>78.0</td>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" name age score\n",
"0 Alice 30 85.5\n",
"1 Bob 25 90.0\n",
"2 Charlie 35 78.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame({\n",
" \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n",
" \"age\": [30, 25, 35],\n",
" \"score\": [85.5, 90.0, 78.0]\n",
"})\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c9d0e1f2",
"metadata": {},
"outputs": [
{
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" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alice</td>\n",
" <td>30</td>\n",
" <td>85.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bob</td>\n",
" <td>25</td>\n",
" <td>90.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Charlie</td>\n",
" <td>35</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name age score\n",
"0 Alice 30 85.5\n",
"1 Bob 25 90.0\n",
"2 Charlie 35 78.0"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "d3e4f5a6",
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>age</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3.0</td>\n",
" <td>3.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>30.0</td>\n",
" <td>84.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.0</td>\n",
" <td>6.082763</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>25.0</td>\n",
" <td>78.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>27.5</td>\n",
" <td>81.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>30.0</td>\n",
" <td>85.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>32.5</td>\n",
" <td>87.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>35.0</td>\n",
" <td>90.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age score\n",
"count 3.0 3.000000\n",
"mean 30.0 84.500000\n",
"std 5.0 6.082763\n",
"min 25.0 78.000000\n",
"25% 27.5 81.750000\n",
"50% 30.0 85.500000\n",
"75% 32.5 87.750000\n",
"max 35.0 90.000000"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
}
],
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"display_name": "Python 3 (ipykernel)",
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