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justhomework/AIandML/e1_python_basics/.ipynb_checkpoints/e1.3_pandas-checkpoint.ipynb

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fix(AIandML): fix something
2022-10-15 14:46:19 +00:00
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 人工智能与机器学习-实验1\n",
"## Part IV. Pandas库的使用\n",
"\n",
"|学号 |姓名 |\n",
"|----------|--------|\n",
"|***REMOVED***|***REMOVED***|\n",
"|2020113874|何一涛|\n",
"\n",
"本部分的实验，需要自己在网络学习相关基础函数使用。"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 1: 导入可能需要的库"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import numpy as np\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2: 读取数据集"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"titanic = pd.read_csv('titanic.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step3: 显示数据集的前5行和后5行"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\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": [
"titanic.head(5)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\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>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": [
"titanic.tail(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 4: 该数据集有多少行和列？"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(891, 12)\n"
]
}
],
"source": [
"print(titanic.shape)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 5: 将PassengerID设置为索引"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</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>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",
" <tr>\n",
" <th>PassengerId</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\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>2</th>\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>3</th>\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>4</th>\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>5</th>\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",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\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>13.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\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>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\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>23.4500</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\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>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>891</th>\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>7.7500</td>\n",
" <td>NaN</td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" Survived Pclass \\\n",
"PassengerId \n",
"1 0 3 \n",
"2 1 1 \n",
"3 1 3 \n",
"4 1 1 \n",
"5 0 3 \n",
"... ... ... \n",
"887 0 2 \n",
"888 1 1 \n",
"889 0 3 \n",
"890 1 1 \n",
"891 0 3 \n",
"\n",
" Name Sex Age \\\n",
"PassengerId \n",
"1 Braund, Mr. Owen Harris male 22.0 \n",
"2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n",
"3 Heikkinen, Miss. Laina female 26.0 \n",
"4 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n",
"5 Allen, Mr. William Henry male 35.0 \n",
"... ... ... ... \n",
"887 Montvila, Rev. Juozas male 27.0 \n",
"888 Graham, Miss. Margaret Edith female 19.0 \n",
"889 Johnston, Miss. Catherine Helen \"Carrie\" female NaN \n",
"890 Behr, Mr. Karl Howell male 26.0 \n",
"891 Dooley, Mr. Patrick male 32.0 \n",
"\n",
" SibSp Parch Ticket Fare Cabin Embarked \n",
"PassengerId \n",
"1 1 0 A/5 21171 7.2500 NaN S \n",
"2 1 0 PC 17599 71.2833 C85 C \n",
"3 0 0 STON/O2. 3101282 7.9250 NaN S \n",
"4 1 0 113803 53.1000 C123 S \n",
"5 0 0 373450 8.0500 NaN S \n",
"... ... ... ... ... ... ... \n",
"887 0 0 211536 13.0000 NaN S \n",
"888 0 0 112053 30.0000 B42 S \n",
"889 1 2 W./C. 6607 23.4500 NaN S \n",
"890 0 0 111369 30.0000 C148 C \n",
"891 0 0 370376 7.7500 NaN Q \n",
"\n",
"[891 rows x 11 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.set_index('PassengerId')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 6：数据中有缺失值吗？"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PassengerId False\n",
"Survived False\n",
"Pclass False\n",
"Name False\n",
"Sex False\n",
"Age True\n",
"SibSp False\n",
"Parch False\n",
"Ticket False\n",
"Fare False\n",
"Cabin True\n",
"Embarked True\n",
"dtype: bool"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.isnull().any()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 7: 乘客的最大年龄和最小年龄是多少？"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"80.0\n"
]
}
],
"source": [
"print(titanic['Age'].max())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.42\n"
]
}
],
"source": [
"print(titanic['Age'].min())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 8: 有多少人生还？"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"342\n"
]
}
],
"source": [
"survived = titanic[titanic['Survived'] == 1]\n",
"\n",
"print(survived.shape[0])\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 9: 男性和女性的生还比例分别是多少？"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"male:0.18890814558058924\n"
]
}
],
"source": [
"male=titanic[titanic['Sex']=='male']\n",
"survived=male[male['Survived'] == 1]\n",
"print(\"male:\" ,end=\"\")\n",
"print(survived.shape[0]/male.shape[0])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"female: 0.7420382165605095\n"
]
}
],
"source": [
"female=titanic[titanic['Sex']=='female']\n",
"survived=female[female['Survived'] == 1]\n",
"print(\"female: \",end=\"\")\n",
"print(survived.shape[0]/female.shape[0])\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 10: 按照船票价格降序排列"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\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>258</th>\n",
" <td>259</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Ward, Miss. Anna</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17755</td>\n",
" <td>512.3292</td>\n",
" <td>NaN</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>737</th>\n",
" <td>738</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Lesurer, Mr. Gustave J</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>PC 17755</td>\n",
" <td>512.3292</td>\n",
" <td>B101</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>679</th>\n",
" <td>680</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cardeza, Mr. Thomas Drake Martinez</td>\n",
" <td>male</td>\n",
" <td>36.0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>PC 17755</td>\n",
" <td>512.3292</td>\n",
" <td>B51 B53 B55</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>89</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Fortune, Miss. Mabel Helen</td>\n",
" <td>female</td>\n",
" <td>23.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>19950</td>\n",
" <td>263.0000</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Fortune, Mr. Charles Alexander</td>\n",
" <td>male</td>\n",
" <td>19.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>19950</td>\n",
" <td>263.0000</td>\n",
" <td>C23 C25 C27</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>633</th>\n",
" <td>634</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Parr, Mr. William Henry Marsh</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112052</td>\n",
" <td>0.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>413</th>\n",
" <td>414</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Cunningham, Mr. Alfred Fleming</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>239853</td>\n",
" <td>0.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>822</th>\n",
" <td>823</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>Reuchlin, Jonkheer. John George</td>\n",
" <td>male</td>\n",
" <td>38.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19972</td>\n",
" <td>0.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>732</th>\n",
" <td>733</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Knight, Mr. Robert J</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>239855</td>\n",
" <td>0.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>674</th>\n",
" <td>675</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Watson, Mr. Ennis Hastings</td>\n",
" <td>male</td>\n",
" <td>NaN</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>239856</td>\n",
" <td>0.0000</td>\n",
" <td>NaN</td>\n",
" <td>S</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" PassengerId Survived Pclass Name \\\n",
"258 259 1 1 Ward, Miss. Anna \n",
"737 738 1 1 Lesurer, Mr. Gustave J \n",
"679 680 1 1 Cardeza, Mr. Thomas Drake Martinez \n",
"88 89 1 1 Fortune, Miss. Mabel Helen \n",
"27 28 0 1 Fortune, Mr. Charles Alexander \n",
".. ... ... ... ... \n",
"633 634 0 1 Parr, Mr. William Henry Marsh \n",
"413 414 0 2 Cunningham, Mr. Alfred Fleming \n",
"822 823 0 1 Reuchlin, Jonkheer. John George \n",
"732 733 0 2 Knight, Mr. Robert J \n",
"674 675 0 2 Watson, Mr. Ennis Hastings \n",
"\n",
" Sex Age SibSp Parch Ticket Fare Cabin Embarked \n",
"258 female 35.0 0 0 PC 17755 512.3292 NaN C \n",
"737 male 35.0 0 0 PC 17755 512.3292 B101 C \n",
"679 male 36.0 0 1 PC 17755 512.3292 B51 B53 B55 C \n",
"88 female 23.0 3 2 19950 263.0000 C23 C25 C27 S \n",
"27 male 19.0 3 2 19950 263.0000 C23 C25 C27 S \n",
".. ... ... ... ... ... ... ... ... \n",
"633 male NaN 0 0 112052 0.0000 NaN S \n",
"413 male NaN 0 0 239853 0.0000 NaN S \n",
"822 male 38.0 0 0 19972 0.0000 NaN S \n",
"732 male NaN 0 0 239855 0.0000 NaN S \n",
"674 male NaN 0 0 239856 0.0000 NaN S \n",
"\n",
"[891 rows x 12 columns]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"titanic.sort_values(by='Fare', ascending=False)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 11: 绘制一个展示船票价格的直方图"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot: ylabel='Frequency'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic['Fare'].plot(kind='hist', bins=20, figsize=(10, 5))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 12: 绘制乘客年龄与船票价格的散点图"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot: xlabel='Age', ylabel='Fare'>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1000x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"titanic.plot(kind='scatter', x='Age', y='Fare', figsize=(10, 5))\n"
]
}
],
"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": "3.10.7"
},
"vscode": {
"interpreter": {
"hash": "1f0d395e06aa83586067b19165efc9b683889967164248deef4bbf1fa27cfb00"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink