Sample Code/Day_35_SampleCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 今天的範例，帶大家實際操作鐵達尼號的資料集\n",
    "關於重覆和遺失值處理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from IPython.display import display\n",
    "#讓圖可以在 jupyter notebook顯示\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 輸入訓練資料(loading data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#印出前幾筆看資料的狀態\n",
    "display(df_train.head(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# 資料的比數\n",
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索性分析:\n",
    "*  觀察-是否有重覆"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no duplicates\n"
     ]
    }
   ],
   "source": [
    "#產生一個不重覆的資料集，透過 drop_duplicates 函數\n",
    "nodup_df_train=df_train.drop_duplicates(subset=None, keep='first', inplace=False)\n",
    "\n",
    "if nodup_df_train.shape[1]==df_train.shape[1]:\n",
    " print(\"no duplicates\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2      False\n",
       "3      False\n",
       "4      False\n",
       "       ...  \n",
       "886    False\n",
       "887    False\n",
       "888    False\n",
       "889    False\n",
       "890    False\n",
       "Length: 891, dtype: bool"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#顯示有重覆的資訊:\n",
    "df_train.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 探索性分析:\n",
    "*  觀察-是否有遺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
      "0        False     False   False  False  False  False  False  False   False   \n",
      "1        False     False   False  False  False  False  False  False   False   \n",
      "2        False     False   False  False  False  False  False  False   False   \n",
      "3        False     False   False  False  False  False  False  False   False   \n",
      "4        False     False   False  False  False  False  False  False   False   \n",
      "\n",
      "    Fare  Cabin  Embarked  \n",
      "0  False   True     False  \n",
      "1  False  False     False  \n",
      "2  False   True     False  \n",
      "3  False  False     False  \n",
      "4  False   True     False  \n",
      "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\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "#判斷每一個資料點，是否為空值:\n",
    "print(df_train.isnull().head(5))\n",
    "\n",
    "\n",
    "# 会判断哪些”列”存在缺失值\n",
    "# any：判斷一個tuple或者list是否全為空，0，False。如果全為空，0，False，則返回False；如果不全為空，則返回True。\n",
    "print(df_train.isnull().any())\n",
    "\n",
    "# 統計 data 裡有空值的變數個數\n",
    "print(df_train.isnull().any().sum())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# 當有些資料用 \"null\" 代表空值時，需要先做轉換，\n",
    "df_train= df_train.replace('null',np.NaN)\n",
    "# 統計 data 裡每一列是否有空值\n",
    "print(df_train.isnull().any())\n",
    "\n",
    "# 統計 data 裡有空值的變數個數\n",
    "print(df_train.isnull().any().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <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>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>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",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
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       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Moran, Mr. James</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>330877</td>\n",
       "      <td>8.4583</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</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>885</th>\n",
       "      <td>886</td>\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>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>13.0000</td>\n",
       "      <td>NaN</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>23.4500</td>\n",
       "      <td>NaN</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>23.4500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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>7.7500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Q</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>866 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "0              1         0       3                   Braund, Mr. Owen Harris   \n",
       "2              3         1       3                    Heikkinen, Miss. Laina   \n",
       "4              5         0       3                  Allen, Mr. William Henry   \n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "5              6         0       3                          Moran, Mr. James   \n",
       "..           ...       ...     ...                                       ...   \n",
       "885          886         0       3      Rice, Mrs. William (Margaret Norton)   \n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "0      male  22.0      1      0         A/5 21171   7.2500   NaN        S  \n",
       "2    female  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4      male  35.0      0      0            373450   8.0500   NaN        S  \n",
       "5      male   NaN      0      0            330877   8.4583   NaN        Q  \n",
       "5      male   NaN      0      0            330877   8.4583   NaN        Q  \n",
       "..      ...   ...    ...    ...               ...      ...   ...      ...  \n",
       "885  female  39.0      0      5            382652  29.1250   NaN        Q  \n",
       "886    male  27.0      0      0            211536  13.0000   NaN        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "888  female   NaN      1      2        W./C. 6607  23.4500   NaN        S  \n",
       "890    male  32.0      0      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[866 rows x 12 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可以只顯示存在缺失值的行列，清楚地確定缺失值的狀態\n",
    "# 代表有缺時的地方才顯示df_train.isnull().values==True\n",
    "df_train[df_train.isnull().values==True]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 補值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "14.526497332334044\n",
      "23.79929292929293\n",
      "17.596074065915886\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x292ee83bba8>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJDXJgJIkNcmAkiQ1yYCSJDXJgFoEkvxukkpyyrhrkVpmr0wWA2pxuBj4PN2XDiXtn70y\nQQyoCdfPfXYucBl90yV5WpKr++uybEzyr0ku6J87K8m/J7kzyc17piCRFjt7ZfIYUJPvfLprxXwN\n+E6SM4HfA1YAvwT8Md1lGvbMlfb3wAVVdRbwIeBvxlG0NAb2yoRZMu4CNG8X0106ALqJLy8GDgWu\nr6ongUeS3NY/fzLwIuCWbqosDqGbFl+aBvbKhDGgJliSo+kuuPaiJEXXRAX88/7eAmytqnMWqESp\nCfbKZHIX32S7APhoVf18Va2oqhPorr65C3hdv3/9WLoJMaG7suWyJD/ZjZHkheMoXFpg9soEMqAm\n28Xs+wnwRrqLhW2nm1H4A3RXNn28qn5I16jvSXI33YzTL124cqWxsVcmkLOZL1JJjqiqJ/pdG18A\nzq3u2jKSZrBX2uUxqMVrY5JnA4cB77LhpP2yVxrlCEqS1CSPQUmSmmRASZKaZEBJkppkQEmSmmRA\nSZKa9P/v+s4vni4LugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tXae9MbTamu+Nro7m+2MueqPpPSimnzJp9xHVslFJ9gKeA1wB7FZVawC6+6eOrrJH/T3w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\nSU0yoCRJTTKgJElNMqAkSU36X1F0cMwkGdj0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我們以  Age 為例，進行補植，補固定值\n",
    "#補值前 平均值\n",
    "print(df_train['Age'].mean()) #age平均\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)\n",
    "\n",
    "#補值後\n",
    "df_train['Age']=df_train['Age'].fillna(0)\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "df_train[df_train['Age'].isnull().values==True]\n",
    "#補完就沒有遺失值了，但是看到這樣的補值效果，平均值上有很明顯的差異，\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 可以看出死亡族群，年齡遺失的比例比較高\n",
    "用0 來補不是一個好的策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "14.526497332334044\n",
      "29.699117647058763\n",
      "13.002015226002884\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x292ee835c50>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJDXJgJIkNcmAkiQ1yYCSJDXJgFoEkvxukkpyyrhrkVpmr0wWA2pxuBj4PN2XDiXtn70y\nQQyoCdfPfXYucBl90yV5WpKr++uybEzyr0ku6J87K8m/J7kzyc17piCRFjt7ZfIYUJPvfLprxXwN\n+E6SM4HfA1YAvwT8Md1lGvbMlfb3wAVVdRbwIeBvxlG0NAb2yoRZMu4CNG8X0106ALqJLy8GDgWu\nr6ongUeS3NY/fzLwIuCWbqosDqGbFl+aBvbKhDGgJliSo+kuuPaiJEXXRAX88/7eAmytqnMWqESp\nCfbKZHIX32S7APhoVf18Va2oqhPorr65C3hdv3/9WLoJMaG7suWyJD/ZjZHkheMoXFpg9soEMqAm\n28Xs+wnwRrqLhW2nm1H4A3RXNn28qn5I16jvSXI33YzTL124cqWxsVcmkLOZL1JJjqiqJ/pdG18A\nzq3u2jKSZrBX2uUxqMVrY5JnA4cB77LhpP2yVxrlCEqS1CSPQUmSmmRASZKaZEBJkppkQEmSmmRA\nSZKa9P/v+s4vni4LugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZVtev0tuccjdwV3d5C3AEvR2u3+6uDx92rdNqPpOndwI3WSewGtjS/Vz/ETisxVqBjwLf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\ngg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我們以  Age 為例，進行補植，補平均數\n",
    "#補值前 平均值\n",
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)\n",
    "#補值後\n",
    "df_train['Age']=df_train['Age'].fillna(df_train['Age'].mean())\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "df_train[df_train['Age'].isnull().values==True]\n",
    "#補平均數，平均數上沒有差異性，標準差接近\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 圖形中高點的位置，就是平均數的位置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "14.526497332334044\n",
      "29.87056116722783\n",
      "14.59766765730238\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x292ee9b5518>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJDXJgJIkNcmAkiQ1yYCSJDXJgFoEkvxukkpyyrhrkVpmr0wWA2pxuBj4PN2XDiXtn70y\nQQyoCdfPfXYucBl90yV5WpKr++uybEzyr0ku6J87K8m/J7kzyc17piCRFjt7ZfIYUJPvfLprxXwN\n+E6SM4HfA1YAvwT8Md1lGvbMlfb3wAVVdRbwIeBvxlG0NAb2yoRZMu4CNG8X0106ALqJLy8GDgWu\nr6ongUeS3NY/fzLwIuCWbqosDqGbFl+aBvbKhDGgJliSo+kuuPaiJEXXRAX88/7eAmytqnMWqESp\nCfbKZHIX32S7APhoVf18Va2oqhPorr65C3hdv3/9WLoJMaG7suWyJD/ZjZHkheMoXFpg9soEMqAm\n28Xs+wnwRrqLhW2nm1H4A3RXNn28qn5I16jvSXI33YzTL124cqWxsVcmkLOZL1JJjqiqJ/pdG18A\nzq3u2jKSZrBX2uUxqMVrY5JnA4cB77LhpP2yVxrlCEqS1CSPQUmSmmRASZKaZEBJkppkQEmSmmRA\nSZKa9P/v+s4vni4LugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fruk+V1VX96sfSLK0f34p8OCo6pvidOB1Se6lm13+lUk+S5u1bgO2VdWmfvkqukZssdZX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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我們以  Age 為例，進行，由後往前補\n",
    "#補值前 平均值\n",
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)\n",
    "#補值後\n",
    "df_train['Age']=df_train['Age'].fillna(method='bfill')\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "df_train[df_train['Age'].isnull().values==True]\n",
    "#補平均數，平均數上沒有差異性，標準差接近\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 由後往前補\n",
    "比較平均，不像前兩個方法，集中在某一些值上面。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "14.526497332334044\n",
      "29.581560044893376\n",
      "14.554589813600963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x292ee9569b0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SJDXJgJIkNcmAkiQ1yYCSJDXJgFoEkvxukkpyyrhrkVpmr0wWA2pxuBj4PN2XDiXtn70y\nQQyoCdfPfXYucBl90yV5WpKr++uybEzyr0ku6J87K8m/J7kzyc17piCRFjt7ZfIYUJPvfLprxXwN\n+E6SM4HfA1YAvwT8Md1lGvbMlfb3wAVVdRbwIeBvxlG0NAb2yoRZMu4CNG8X0106ALqJLy8GDgWu\nr6ongUeS3NY/fzLwIuCWbqosDqGbFl+aBvbKhDGgJliSo+kuuPaiJEXXRAX88/7eAmytqnMWqESp\nCfbKZHIX32S7APhoVf18Va2oqhPorr65C3hdv3/9WLoJMaG7suWyJD/ZjZHkheMoXFpg9soEMqAm\n28Xs+wnwRrqLhW2nm1H4A3RXNn28qn5I16jvSXI33YzTL124cqWxsVcmkLOZL1JJjqiqJ/pdG18A\nzq3u2jKSZrBX2uUxqMVrY5JnA4cB77LhpP2yVxrlCEqS1CSPQUmSmmRASZKaZEBJkppkQEmSmmRA\nSZKa9P/v+s4vni4LugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我們以  Age 為例，進行，由後往前補\n",
    "#補值前 平均值\n",
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)\n",
    "#補值後\n",
    "df_train['Age']=df_train['Age'].fillna(method='pad')\n",
    "print(df_train['Age'].mean())\n",
    "print(df_train['Age'].std()) #age標準差\n",
    "df_train[df_train['Age'].isnull().values==True]\n",
    "#補平均數，平均數上沒有差異性，標準差接近\n",
    "# 連續型用分布圖行來看\n",
    "g = sns.FacetGrid(df_train, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 由後往前補\n",
    "比較平均，不像前兩個方法，集中在某一些值上面。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 大家可以透過這樣的方式，自己嘗試玩另外兩種遺失的資料。"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Day33.YOLO 細節理解 - 網路輸出的後處理_HW.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}