Solution/Day_35_Solution.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業:\n",
    "課程範例以 訓練資料集來檢視，先看一下測試資料特性，再把測試資料集和訓練資料集合併，並回答下列問題，   \n",
    "目的:讓大家熟悉對應這樣的問題，我們要提取怎樣的函數來進行計算。   \n",
    "\n",
    "* Q1: 觀察測試(test)資料集和訓練(Train)資料集的變數的差異性?\n",
    "* Q2: 測試資料集是否有遺失值?\n",
    "* Q3: 從合併資料選取一個變數，嘗試去做各種不同遺失值的處理，並透過圖形或數值來做輔助判斷，補值前與後的差異，你覺得以這個變數而言，試著說明每一個方法的差異。   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "collapsed": true,
    "executionInfo": {
     "elapsed": 1558,
     "status": "ok",
     "timestamp": 1578021044012,
     "user": {
      "displayName": "Mora chen",
      "photoUrl": "https://lh3.googleusercontent.com/a-/AAuE7mB40f7sDArbZ5_DYq02nNcnLD0Ryaf7AhsASSQeLQ=s64",
      "userId": "03171203089166907199"
     },
     "user_tz": -480
    },
    "id": "yz28_IgkYdBW",
    "outputId": "a12f486c-18b3-4fb5-d06c-f162aebd9444"
   },
   "outputs": [],
   "source": [
    "#把需要的 library import 進來\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": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#顯示圖形的函數，可不先不用理解，直接用\n",
    "from IPython.display import display\n",
    "from IPython.display import display_html\n",
    "def display_side_by_side(*args):\n",
    "    html_str=''\n",
    "    for df in args:\n",
    "        html_str+=df.to_html()\n",
    "    display_html(html_str.replace('table','table style=\"display:inline\"'),raw=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把兩個訓練資料集和測試資料集讀進來\n",
    "df_train = pd.read_csv(\"Titanic_train.csv\")\n",
    "df_test = pd.read_csv(\"Titanic_test.csv\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q1: 判斷 測試資料集和訓練資料集欄位變數是否有差異性?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
      "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n",
      "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
      "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Q1: 判斷 測試資料集和訓練資料集欄位變數是否有差異性?\n",
    "'''\n",
    "暗示，可以用那些函數，來看出資料的欄位變數\n",
    "'''\n",
    "print(df_test.columns)\n",
    "print(df_train.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A1 : Test 資料集沒有 'Survived'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q2: 測試資料集是否有遺失值?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "資料筆數= (418, 11)\n"
     ]
    }
   ],
   "source": [
    "#測試資料集的特性\n",
    "print(\"資料筆數=\",df_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PassengerId    False\n",
      "Pclass         False\n",
      "Name           False\n",
      "Sex            False\n",
      "Age             True\n",
      "SibSp          False\n",
      "Parch          False\n",
      "Ticket         False\n",
      "Fare            True\n",
      "Cabin           True\n",
      "Embarked       False\n",
      "dtype: bool\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# 判斷測試資料集，是否有遺失值\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_test.isnull().any())\n",
    "\n",
    "# 統計 data 裡有空值的變數個數\n",
    "print(df_test.isnull().any().sum())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  A2: 測試資料集有遺失值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Q3: 從合併資料選取一個變數，嘗試去做各種不同遺失值的處理，並透過圖形來做輔助判斷，補值前與後的差異，你覺得以這個變數而言，試著說明每一個方法的差異。   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  1309 non-null   int64  \n",
      " 1   Survived     891 non-null    float64\n",
      " 2   Pclass       1309 non-null   int64  \n",
      " 3   Name         1309 non-null   object \n",
      " 4   Sex          1309 non-null   object \n",
      " 5   Age          1046 non-null   float64\n",
      " 6   SibSp        1309 non-null   int64  \n",
      " 7   Parch        1309 non-null   int64  \n",
      " 8   Ticket       1309 non-null   object \n",
      " 9   Fare         1308 non-null   float64\n",
      " 10  Cabin        295 non-null    object \n",
      " 11  Embarked     1307 non-null   object \n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n",
      "None\n",
      "cabin 遺失個數= 1014\n"
     ]
    }
   ],
   "source": [
    "#合併資料\n",
    "data = df_train.append(df_test)\n",
    "\n",
    "print(data.info())\n",
    "\n",
    "print('cabin 遺失個數=',data['Cabin'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C23 C25 C27        6\n",
      "G6                 5\n",
      "B57 B59 B63 B66    5\n",
      "F4                 4\n",
      "C22 C26            4\n",
      "                  ..\n",
      "A6                 1\n",
      "B61                1\n",
      "T                  1\n",
      "D9                 1\n",
      "C47                1\n",
      "Name: Cabin, Length: 186, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 以 Cabin 為例，先看 Cabin  出現值的特性\n",
    "print(data[\"Cabin\"].value_counts())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### cabin 不能隨意補值，須先進一步觀察和處理 \n",
    "* 方法1:遺失的屬於另一類。  \n",
    "* 方法2:看 cabin 和其他變數有無關係，可以進行補值。\n",
    "* 方法3:遺失比例太高，可以先不放入模型。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NoCabin', 'C', 'E', 'G', 'D', 'A', 'B', 'F', 'T'], dtype=object)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#* 方法1:遺失的屬於另一類。 \n",
    "data['Cabin'].head(10)\n",
    "data[\"Cabin\"] = data['Cabin'].apply(lambda x : str(x)[0] if not pd.isnull(x) else 'NoCabin')\n",
    "data[\"Cabin\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x168f3c8e7b8>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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otDJJUo9r+RRQEQIGgST1YXs0dLYkqW8yFCRJpcpCISJGR8T/i4inI2J5RMws2g+PiPsj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\nBrdE1sIBt9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 挑整後的 Cabin 觀察遺失的樣態\n",
    "sns.countplot(data['Cabin'], hue=data['Survived'])\n",
    "#結論，遺失的死亡率比較高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>Cabin</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.757576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>0.744681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>0.593220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>G</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>0.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NoCabin</td>\n",
       "      <td>0.299854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>T</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Cabin  Survived\n",
       "3        D  0.757576\n",
       "4        E  0.750000\n",
       "1        B  0.744681\n",
       "5        F  0.615385\n",
       "2        C  0.593220\n",
       "6        G  0.500000\n",
       "0        A  0.466667\n",
       "7  NoCabin  0.299854\n",
       "8        T  0.000000"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#數值計算\n",
    "data[['Cabin', 'Survived']].groupby(['Cabin'], as_index=False).mean().sort_values(by='Survived', ascending=False)\n",
    "# NoCabin的比例和 T 較接近"
   ]
  }
 ],
 "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"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}