第一章：第一节数据载入及初步观察-课程.ipynb

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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**复习：**这门课程得主要目的是通过真实的数据，以实战的方式了解数据分析的流程和熟悉数据分析python的基本操作。知道了课程的目的之后，我们接下来我们要正式的开始数据分析的实战教学，完成kaggle上[泰坦尼克的任务](https://www.kaggle.com/c/titanic/overview)，实战数据分析全流程。\n",
    "这里有两份资料：\n",
    "教材《Python for Data Analysis》和 baidu.com &\n",
    "google.com（善用搜索引擎）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 第一章：数据载入及初步观察"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 载入数据\n",
    "数据集下载 https://www.kaggle.com/c/titanic/overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.1 任务一：导入numpy和pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\datawhale\\\\hands-on-data-analysis\\\\第一单元项目集合'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'D:\\\\datawhale\\\\hands-on-data-analysis\\\\第一单元项目集合'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【提示】如果加载失败，学会如何在你的python环境下安装numpy和pandas这两个库"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.2 任务二：载入数据\n",
    "(1) 使用相对路径载入数据  \n",
    "(2) 使用绝对路径载入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df = pd.read_csv('train.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2,1,1,\"Cumings, Mrs. John Bradley (Florence Br...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3,1,3,\"Heikkinen, Miss. Laina\",female,26,0,0,S...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "  PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n",
       "0  1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/...                             \n",
       "1  2,1,1,\"Cumings, Mrs. John Bradley (Florence Br...                             \n",
       "2  3,1,3,\"Heikkinen, Miss. Laina\",female,26,0,0,S...                             "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df = pd.read_table('train.csv')\n",
    "df.head(3)#这样读出的全挤在一列里面，csv文件使用‘,'作为分隔符"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【提示】相对路径载入报错时，尝试使用os.getcwd()查看当前工作目录。  \n",
    "【思考】知道数据加载的方法后，试试pd.read_csv()和pd.read_table()的不同，如果想让他们效果一样，需要怎么做？了解一下'.tsv'和'.csv'的不同，如何加载这两个数据集？  \n",
    "【总结】加载的数据是所有工作的第一步，我们的工作会接触到不同的数据格式（eg:.csv;.tsv;.xlsx）,但是加载的方法和思路都是一样的，在以后工作和做项目的过程中，遇到之前没有碰到的问题，要多多查资料吗，使用googel，了解业务逻辑，明白输入和输出是什么。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### read_table默认是'\\t'(也就是tab)切割数据集的;\n",
    "#### read_csv默认是','(也就是逗号)切割数据集的;\n",
    "#### 其他,除了方法名不一样,都一样..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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   "outputs": [
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       "      <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",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <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",
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       "    <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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_table('train.csv',sep=',')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "<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",
       "  </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",
       "\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",
       "\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  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:\\\\datawhale\\\\hands-on-data-analysis\\\\第一单元项目集合\\\\train.csv')\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.3 任务三：每1000行为一个数据模块，逐块读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "chunker = pd.read_csv('train.csv', chunksize=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】什么是逐块读取？为什么要逐块读取呢？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用pandas.read_table()去读大文件，差不多花了近2分钟，最后打开发现差不多3千万行数据。这仅仅是打开，如果要处理不知得多费劲。\n",
    "\n",
    "解决：我翻了一下文档，这一类读取文件的函数有两个参数：chunksize、iterator\n",
    "\n",
    "原理就是不一次性把文件数据读入内存中，而是分多次。\n",
    "版权声明：本文为CSDN博主「lzw2016」的原创文章，遵循CC 4.0 BY-SA版权协议，转载请附上原文出处链接及本声明。\n",
    "原文链接：https://blog.csdn.net/lzw2016/article/details/80471771"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.1.4 任务四：将表头改成中文，索引改为乘客ID [对于某些英文资料，我们可以通过翻译来更直观的熟悉我们的数据]\n",
    "PassengerId => 乘客ID  \n",
    "Survived    => 是否幸存   \n",
    "Pclass      => 乘客等级(1/2/3等舱位)  \n",
    "Name        => 乘客姓名  \n",
    "Sex         => 性别                 \n",
    "Age         => 年龄                 \n",
    "SibSp       => 堂兄弟/妹个数  \n",
    "Parch       => 父母与小孩个数  \n",
    "Ticket      => 船票信息             \n",
    "Fare        => 票价                \n",
    "Cabin       => 客舱                \n",
    "Embarked    => 登船港口             "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
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       "    }\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>是否幸存</th>\n",
       "      <th>仓位等级</th>\n",
       "      <th>姓名</th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄</th>\n",
       "      <th>兄弟姐妹个数</th>\n",
       "      <th>父母子女个数</th>\n",
       "      <th>船票信息</th>\n",
       "      <th>票价</th>\n",
       "      <th>客舱</th>\n",
       "      <th>登船港口</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乘客ID</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      是否幸存  仓位等级                                                 姓名      性别  \\\n",
       "乘客ID                                                                          \n",
       "1        0     3                            Braund, Mr. Owen Harris    male   \n",
       "2        1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "3        1     3                             Heikkinen, Miss. Laina  female   \n",
       "4        1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "5        0     3                           Allen, Mr. William Henry    male   \n",
       "\n",
       "        年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  \n",
       "乘客ID                                                              \n",
       "1     22.0       1       0         A/5 21171   7.2500   NaN    S  \n",
       "2     38.0       1       0          PC 17599  71.2833   C85    C  \n",
       "3     26.0       0       0  STON/O2. 3101282   7.9250   NaN    S  \n",
       "4     35.0       1       0            113803  53.1000  C123    S  \n",
       "5     35.0       0       0            373450   8.0500   NaN    S  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df = pd.read_csv('train.csv', names=['乘客ID','是否幸存','仓位等级','姓名','性别','年龄','兄弟姐妹个数','父母子女个数','船票信息','票价','客舱','登船港口'],index_col='乘客ID',header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】所谓将表头改为中文其中一个思路是：将英文额度表头替换成中文。还有其他的方法吗？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "也可以通过字典形式？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 初步观察\n",
    "导入数据后，你可能要对数据的整体结构和样例进行概览，比如说，数据大小、有多少列，各列都是什么格式的，是否包含null等"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.1 任务一：查看数据的基本信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   是否幸存    891 non-null    int64  \n",
      " 1   仓位等级    891 non-null    int64  \n",
      " 2   姓名      891 non-null    object \n",
      " 3   性别      891 non-null    object \n",
      " 4   年龄      714 non-null    float64\n",
      " 5   兄弟姐妹个数  891 non-null    int64  \n",
      " 6   父母子女个数  891 non-null    int64  \n",
      " 7   船票信息    891 non-null    object \n",
      " 8   票价      891 non-null    float64\n",
      " 9   客舱      204 non-null    object \n",
      " 10  登船港口    889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#写入代码\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【提示】有多个函数可以这样做，你可以做一下总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "describe,info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.2 任务二：观察表格前10行的数据和后15行的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
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       "      <td>0</td>\n",
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       "      <td>S</td>\n",
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       "      <th>8</th>\n",
       "      <td>0</td>\n",
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       "      <td>male</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
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       "      <td>female</td>\n",
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       "      是否幸存  仓位等级                                                 姓名      性别  \\\n",
       "乘客ID                                                                          \n",
       "1        0     3                            Braund, Mr. Owen Harris    male   \n",
       "2        1     1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   \n",
       "3        1     3                             Heikkinen, Miss. Laina  female   \n",
       "4        1     1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female   \n",
       "5        0     3                           Allen, Mr. William Henry    male   \n",
       "6        0     3                                   Moran, Mr. James    male   \n",
       "7        0     1                            McCarthy, Mr. Timothy J    male   \n",
       "8        0     3                     Palsson, Master. Gosta Leonard    male   \n",
       "9        1     3  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female   \n",
       "10       1     2                Nasser, Mrs. Nicholas (Adele Achem)  female   \n",
       "\n",
       "        年龄  兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  \n",
       "乘客ID                                                              \n",
       "1     22.0       1       0         A/5 21171   7.2500   NaN    S  \n",
       "2     38.0       1       0          PC 17599  71.2833   C85    C  \n",
       "3     26.0       0       0  STON/O2. 3101282   7.9250   NaN    S  \n",
       "4     35.0       1       0            113803  53.1000  C123    S  \n",
       "5     35.0       0       0            373450   8.0500   NaN    S  \n",
       "6      NaN       0       0            330877   8.4583   NaN    Q  \n",
       "7     54.0       0       0             17463  51.8625   E46    S  \n",
       "8      2.0       3       1            349909  21.0750   NaN    S  \n",
       "9     27.0       0       2            347742  11.1333   NaN    S  \n",
       "10    14.0       1       0            237736  30.0708   NaN    C  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11767</td>\n",
       "      <td>83.1583</td>\n",
       "      <td>C50</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Shelley, Mrs. William (Imanita Parrish Hall)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>230433</td>\n",
       "      <td>26.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Markun, Mr. Johann</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>349257</td>\n",
       "      <td>7.8958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Dahlberg, Miss. Gerda Ulrika</td>\n",
       "      <td>female</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7552</td>\n",
       "      <td>10.5167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Banfield, Mr. Frederick James</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>C.A./SOTON 34068</td>\n",
       "      <td>10.5000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Sutehall, Mr. Henry Jr</td>\n",
       "      <td>male</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SOTON/OQ 392076</td>\n",
       "      <td>7.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Rice, Mrs. William (Margaret Norton)</td>\n",
       "      <td>female</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>382652</td>\n",
       "      <td>29.1250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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",
       "</div>"
      ],
      "text/plain": [
       "      是否幸存  仓位等级                                             姓名      性别    年龄  \\\n",
       "乘客ID                                                                            \n",
       "877      0     3                  Gustafsson, Mr. Alfred Ossian    male  20.0   \n",
       "878      0     3                           Petroff, Mr. Nedelio    male  19.0   \n",
       "879      0     3                             Laleff, Mr. Kristo    male   NaN   \n",
       "880      1     1  Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)  female  56.0   \n",
       "881      1     2   Shelley, Mrs. William (Imanita Parrish Hall)  female  25.0   \n",
       "882      0     3                             Markun, Mr. Johann    male  33.0   \n",
       "883      0     3                   Dahlberg, Miss. Gerda Ulrika  female  22.0   \n",
       "884      0     2                  Banfield, Mr. Frederick James    male  28.0   \n",
       "885      0     3                         Sutehall, Mr. Henry Jr    male  25.0   \n",
       "886      0     3           Rice, Mrs. William (Margaret Norton)  female  39.0   \n",
       "887      0     2                          Montvila, Rev. Juozas    male  27.0   \n",
       "888      1     1                   Graham, Miss. Margaret Edith  female  19.0   \n",
       "889      0     3       Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN   \n",
       "890      1     1                          Behr, Mr. Karl Howell    male  26.0   \n",
       "891      0     3                            Dooley, Mr. Patrick    male  32.0   \n",
       "\n",
       "      兄弟姐妹个数  父母子女个数              船票信息       票价    客舱 登船港口  \n",
       "乘客ID                                                        \n",
       "877        0       0              7534   9.8458   NaN    S  \n",
       "878        0       0            349212   7.8958   NaN    S  \n",
       "879        0       0            349217   7.8958   NaN    S  \n",
       "880        0       1             11767  83.1583   C50    C  \n",
       "881        0       1            230433  26.0000   NaN    S  \n",
       "882        0       0            349257   7.8958   NaN    S  \n",
       "883        0       0              7552  10.5167   NaN    S  \n",
       "884        0       0  C.A./SOTON 34068  10.5000   NaN    S  \n",
       "885        0       0   SOTON/OQ 392076   7.0500   NaN    S  \n",
       "886        0       5            382652  29.1250   NaN    Q  \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  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df.tail(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.2.4 任务三：判断数据是否为空，为空的地方返回True，其余地方返回False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>是否幸存</th>\n",
       "      <th>仓位等级</th>\n",
       "      <th>姓名</th>\n",
       "      <th>性别</th>\n",
       "      <th>年龄</th>\n",
       "      <th>兄弟姐妹个数</th>\n",
       "      <th>父母子女个数</th>\n",
       "      <th>船票信息</th>\n",
       "      <th>票价</th>\n",
       "      <th>客舱</th>\n",
       "      <th>登船港口</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>乘客ID</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",
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       "    </tr>\n",
       "  </thead>\n",
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       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       是否幸存   仓位等级     姓名     性别     年龄  兄弟姐妹个数  父母子女个数   船票信息     票价     客舱  \\\n",
       "乘客ID                                                                           \n",
       "1     False  False  False  False  False   False   False  False  False   True   \n",
       "2     False  False  False  False  False   False   False  False  False  False   \n",
       "3     False  False  False  False  False   False   False  False  False   True   \n",
       "4     False  False  False  False  False   False   False  False  False  False   \n",
       "5     False  False  False  False  False   False   False  False  False   True   \n",
       "\n",
       "       登船港口  \n",
       "乘客ID         \n",
       "1     False  \n",
       "2     False  \n",
       "3     False  \n",
       "4     False  \n",
       "5     False  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#写入代码\n",
    "df.isnull().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【总结】上面的操作都是数据分析中对于数据本身的观察"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【思考】对于一个数据，还可以从哪些方面来观察？找找答案，这个将对下面的数据分析有很大的帮助"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "还可以通过数据类型来观察，比如票价这种是数字型的，人名这种属于字符型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 保存数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.3.1 任务一：将你加载并做出改变的数据，在工作目录下保存为一个新文件train_chinese.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写入代码\n",
    "df.to_csv('train_chinese.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "【总结】数据的加载以及入门，接下来就要接触数据本身的运算，我们将主要掌握numpy和pandas在工作和项目场景的运用。"
   ]
  }
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