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premier exemple
2023-05-02 15:53:53 +02:00
{
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{
"cell_type": "code",
"execution_count": 1,
"id": "935dd393-0cdb-470f-b5e7-cf2af42855fd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"url = \"https://pixees.fr/informatiquelycee/n_site/asset/titanic.csv\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "a4ff45d8-079a-4a2e-a220-87760831d4ba",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#Importation des données \n",
"import pandas as pd\n",
"data = pd.io.parsers.read_csv(url)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3f9f39d2-aced-4bba-acf7-b00505f93a21",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
"\n",
" Name Sex Age SibSp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
"\n",
" Parch Ticket Fare Cabin Embarked \n",
"0 0 A/5 21171 7.2500 NaN S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 NaN S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 NaN S "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f4b6f349-e8d5-406b-84f0-ee876df95e1f",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>PassengerId</th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Name</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" <th>SibSp</th>\n",
" <th>Parch</th>\n",
" <th>Ticket</th>\n",
" <th>Fare</th>\n",
" <th>Cabin</th>\n",
" <th>Embarked</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.00</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.00</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.45</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.00</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.75</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name \\\n",
"886 887 0 2 Montvila, Rev. Juozas \n",
"887 888 1 1 Graham, Miss. Margaret Edith \n",
"888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n",
"889 890 1 1 Behr, Mr. Karl Howell \n",
"890 891 0 3 Dooley, Mr. Patrick \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"886 male 27.0 0 0 211536 13.00 NaN S \n",
"887 female 19.0 0 0 112053 30.00 B42 S \n",
"888 female NaN 1 2 W./C. 6607 23.45 NaN S \n",
"889 male 26.0 0 0 111369 30.00 C148 C \n",
"890 male 32.0 0 0 370376 7.75 NaN Q "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.tail()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "86cc0139-bc42-49eb-9261-946db3ce517e",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data = data.drop(['Name', 'PassengerId', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "84dcd03a-9f74-467d-92f8-720fd5dc57a2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
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" <th></th>\n",
" <th>Survived</th>\n",
" <th>Pclass</th>\n",
" <th>Sex</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
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" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
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],
"text/plain": [
" Survived Pclass Sex Age\n",
"0 0 3 male 22.0\n",
"1 1 1 female 38.0\n",
"2 1 3 female 26.0\n",
"3 1 1 female 35.0\n",
"4 0 3 male 35.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8fa36ab8-f947-46b2-9b26-5c608058fd48",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"data = data.dropna(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "b4eeb852-30d2-43b6-82f6-23d1c5db4139",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(714, 4)\n"
]
}
],
"source": [
"print(data.shape)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "3a91513f-f8e5-4ef3-b0d8-967f98539a50",
"metadata": {
"tags": []
},
"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>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>714.000000</td>\n",
" <td>714.000000</td>\n",
" <td>714.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.406162</td>\n",
" <td>2.236695</td>\n",
" <td>29.699118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.491460</td>\n",
" <td>0.838250</td>\n",
" <td>14.526497</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.420000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>20.125000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>2.000000</td>\n",
" <td>28.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>38.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>3.000000</td>\n",
" <td>80.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Pclass Age\n",
"count 714.000000 714.000000 714.000000\n",
"mean 0.406162 2.236695 29.699118\n",
"std 0.491460 0.838250 14.526497\n",
"min 0.000000 1.000000 0.420000\n",
"25% 0.000000 1.000000 20.125000\n",
"50% 0.000000 2.000000 28.000000\n",
"75% 1.000000 3.000000 38.000000\n",
"max 1.000000 3.000000 80.000000"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "5e52ca0e-69fe-47eb-b4f9-0ae4f1d38a27",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"3 355\n",
"1 186\n",
"2 173\n",
"Name: Pclass, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data['Pclass'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "61a70d41-bea2-4872-8372-7a999a8e73ac",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['Pclass'].value_counts().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f77de794-b854-4697-811a-c404b14eb680",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<Axes: >"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data['Age'].hist()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "124a3f8b-2884-40b9-ab56-3a3366c499ee",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"text/plain": [
" Survived Pclass Age\n",
"Sex \n",
"female 0.754789 2.065134 27.915709\n",
"male 0.205298 2.335541 30.726645"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Sex']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "895a8e68-0e8d-4580-bfd0-be8f287d6342",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <th>2</th>\n",
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],
"text/plain": [
" Survived Age\n",
"Sex Pclass \n",
"female 1 0.964706 34.611765\n",
" 2 0.918919 28.722973\n",
" 3 0.460784 21.750000\n",
"male 1 0.396040 41.281386\n",
" 2 0.151515 30.740707\n",
" 3 0.150198 26.507589"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Sex', 'Pclass']).mean()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "af916e1f-1acc-419d-bb9c-aae85976e218",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" <td>185.287955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.075528</td>\n",
" <td>165.706451</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.250922</td>\n",
" <td>162.051980</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"3\" valign=\"top\">male</th>\n",
" <th>1</th>\n",
" <td>0.241584</td>\n",
" <td>229.206594</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.129870</td>\n",
" <td>218.859292</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.128145</td>\n",
" <td>147.853777</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Survived Age\n",
"Sex Pclass \n",
"female 1 0.034454 185.287955\n",
" 2 0.075528 165.706451\n",
" 3 0.250922 162.051980\n",
"male 1 0.241584 229.206594\n",
" 2 0.129870 218.859292\n",
" 3 0.128145 147.853777"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data.groupby(['Sex', 'Pclass']).var()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "42a4b1ae-7a0f-4f43-9ebb-df2f6929e241",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/onyxia\n"
]
}
],
"source": [
"cd"
]
}
],
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Reference in New Issue Copy Permalink