sppu-te-comp-content/DataScienceAndBigDataAnalytics
sppu-te-comp-content/DataScienceAndBigDataAnalytics
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DataScienceAndBigDataAnalytics/Notebooks/Notebook-A9 (Data visualisation-2).ipynb

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Added jupyter notebook for A9.
2025-01-24 11:08:34 +05:30
{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"id": "7f5a4d1e-cb5e-4def-bd86-c76aae2a5b21",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f6b278a3-d01a-46d6-8648-65bdc934b874",
"metadata": {},
"outputs": [
{
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>survived</th>\n",
" <th>pclass</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
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" <td>0</td>\n",
" <td>7.7500</td>\n",
" <td>Q</td>\n",
" <td>Third</td>\n",
" <td>man</td>\n",
" <td>True</td>\n",
" <td>NaN</td>\n",
" <td>Queenstown</td>\n",
" <td>no</td>\n",
" <td>True</td>\n",
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"text/plain": [
" survived pclass sex age sibsp parch fare embarked class \\\n",
"0 0 3 male 22.0 1 0 7.2500 S Third \n",
"1 1 1 female 38.0 1 0 71.2833 C First \n",
"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",
"886 0 2 male 27.0 0 0 13.0000 S Second \n",
"887 1 1 female 19.0 0 0 30.0000 S First \n",
"888 0 3 female NaN 1 2 23.4500 S Third \n",
"889 1 1 male 26.0 0 0 30.0000 C First \n",
"890 0 3 male 32.0 0 0 7.7500 Q Third \n",
"\n",
" who adult_male deck embark_town alive alone \n",
"0 man True NaN Southampton no False \n",
"1 woman False C Cherbourg yes False \n",
"2 woman False NaN Southampton yes True \n",
"3 woman False C Southampton yes False \n",
"4 man True NaN Southampton no True \n",
".. ... ... ... ... ... ... \n",
"886 man True NaN Southampton no True \n",
"887 woman False B Southampton yes True \n",
"888 woman False NaN Southampton no False \n",
"889 man True C Cherbourg yes True \n",
"890 man True NaN Queenstown no True \n",
"\n",
"[891 rows x 15 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = sns.load_dataset('titanic')\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "061602d8-3b30-46ae-8e5b-86fd3eb3cabc",
"metadata": {},
"outputs": [
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>survived</th>\n",
" <th>pclass</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>fare</th>\n",
" <th>embarked</th>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" survived pclass sex age sibsp parch fare embarked class \\\n",
"0 0 3 male 22.0 1 0 7.2500 S Third \n",
"1 1 1 female 38.0 1 0 71.2833 C First \n",
"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",
"1 woman False C Cherbourg yes False \n",
"2 woman False NaN Southampton yes True \n",
"3 woman False C Southampton yes False \n",
"4 man True NaN Southampton no True "
]
},
"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
}
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