593 lines
69 KiB
Plaintext
593 lines
69 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "7f5a4d1e-cb5e-4def-bd86-c76aae2a5b21",
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"metadata": {},
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"outputs": [],
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"source": [
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "f6b278a3-d01a-46d6-8648-65bdc934b874",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"<div>\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>survived</th>\n",
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" <th>pclass</th>\n",
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" <th>sex</th>\n",
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" <th>age</th>\n",
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" <th>sibsp</th>\n",
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" <th>parch</th>\n",
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" <th>fare</th>\n",
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" <th>embarked</th>\n",
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" <th>class</th>\n",
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" <th>who</th>\n",
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" <th>adult_male</th>\n",
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" <th>deck</th>\n",
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" <th>embark_town</th>\n",
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" <th>alive</th>\n",
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" <th>alone</th>\n",
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" <td>7.2500</td>\n",
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" <td>man</td>\n",
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" <td>True</td>\n",
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" <td>Southampton</td>\n",
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" <td>C</td>\n",
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" <td>First</td>\n",
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" <td>woman</td>\n",
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" <td>False</td>\n",
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" <td>C</td>\n",
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" <td>Cherbourg</td>\n",
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" <td>yes</td>\n",
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" <td>False</td>\n",
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" <td>True</td>\n",
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" <td>no</td>\n",
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" <th>887</th>\n",
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" <td>1</td>\n",
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" <td>30.0000</td>\n",
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" <td>First</td>\n",
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" <td>woman</td>\n",
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" <td>False</td>\n",
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" <td>B</td>\n",
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" <td>Southampton</td>\n",
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" <td>yes</td>\n",
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" <td>True</td>\n",
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" <td>1</td>\n",
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" <td>2</td>\n",
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" <td>S</td>\n",
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" <td>woman</td>\n",
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" <td>False</td>\n",
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" <td>NaN</td>\n",
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" <td>Southampton</td>\n",
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" <td>no</td>\n",
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" <td>False</td>\n",
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" </tr>\n",
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" <th>889</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>male</td>\n",
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" <td>26.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>30.0000</td>\n",
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" <td>man</td>\n",
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" <td>True</td>\n",
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" <td>C</td>\n",
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" <td>Cherbourg</td>\n",
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" <td>yes</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>890</th>\n",
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" <td>0</td>\n",
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" <td>3</td>\n",
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" <td>male</td>\n",
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" <td>32.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>7.7500</td>\n",
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" <td>Q</td>\n",
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" <td>Third</td>\n",
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" <td>man</td>\n",
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" <td>True</td>\n",
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" <td>Queenstown</td>\n",
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" <td>no</td>\n",
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" <td>True</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>891 rows × 15 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" survived pclass sex age sibsp parch fare embarked class \\\n",
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"0 0 3 male 22.0 1 0 7.2500 S Third \n",
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"1 1 1 female 38.0 1 0 71.2833 C First \n",
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"2 1 3 female 26.0 0 0 7.9250 S Third \n",
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"3 1 1 female 35.0 1 0 53.1000 S First \n",
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"4 0 3 male 35.0 0 0 8.0500 S Third \n",
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".. ... ... ... ... ... ... ... ... ... \n",
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"886 0 2 male 27.0 0 0 13.0000 S Second \n",
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"887 1 1 female 19.0 0 0 30.0000 S First \n",
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"889 1 1 male 26.0 0 0 30.0000 C First \n",
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"890 0 3 male 32.0 0 0 7.7500 Q Third \n",
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"\n",
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" who adult_male deck embark_town alive alone \n",
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"0 man True NaN Southampton no False \n",
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"1 woman False C Cherbourg yes False \n",
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"2 woman False NaN Southampton yes True \n",
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"3 woman False C Southampton yes False \n",
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"4 man True NaN Southampton no True \n",
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".. ... ... ... ... ... ... \n",
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"886 man True NaN Southampton no True \n",
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"887 woman False B Southampton yes True \n",
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"888 woman False NaN Southampton no False \n",
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"889 man True C Cherbourg yes True \n",
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"890 man True NaN Queenstown no True \n",
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"\n",
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"[891 rows x 15 columns]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = sns.load_dataset('titanic')\n",
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"df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "061602d8-3b30-46ae-8e5b-86fd3eb3cabc",
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"metadata": {},
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"outputs": [
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{
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"data": {
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" <thead>\n",
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" <th></th>\n",
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" <th>survived</th>\n",
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" <th>pclass</th>\n",
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" <th>sex</th>\n",
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" <th>age</th>\n",
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||
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||
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||
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||
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||
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||
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||
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||
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|
||
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|
||
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||
],
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||
"text/plain": [
|
||
" survived pclass sex age sibsp parch fare embarked class \\\n",
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||
"0 0 3 male 22.0 1 0 7.2500 S Third \n",
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||
"1 1 1 female 38.0 1 0 71.2833 C First \n",
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||
"2 1 3 female 26.0 0 0 7.9250 S Third \n",
|
||
"3 1 1 female 35.0 1 0 53.1000 S First \n",
|
||
"4 0 3 male 35.0 0 0 8.0500 S Third \n",
|
||
"\n",
|
||
" who adult_male deck embark_town alive alone \n",
|
||
"0 man True NaN Southampton no False \n",
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"1 woman False C Cherbourg yes False \n",
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"2 woman False NaN Southampton yes True \n",
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"3 woman False C Southampton yes False \n",
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||
"4 man True NaN Southampton no True "
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||
]
|
||
},
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"df.head()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "6e9a36b2-e779-46cb-8f57-e1a25704ac71",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='sex', ylabel='age'>"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"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": [
|
||
"sns.boxplot(x=\"sex\", y=\"age\", data=df, hue=\"survived\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "5de91e98-fe89-4601-8603-84e78889293a",
|
||
"metadata": {
|
||
"scrolled": true
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<Axes: xlabel='sex', ylabel='age'>"
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"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": [
|
||
"sns.boxplot(x=\"sex\", y=\"age\", data=df, hue=\"survived\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 23,
|
||
"id": "a7fd01bf-5a59-4504-8e9d-0146179d3d36",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<seaborn.axisgrid.FacetGrid at 0x722dce27ad10>"
|
||
]
|
||
},
|
||
"execution_count": 23,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 570.486x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"sns.catplot(x=\"sex\", hue=\"survived\", data=df, kind=\"count\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "41409512-132c-4500-ae5c-81494b7119f6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.10.12"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|