Solution/Day_37_Solution.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "在鐵達尼資料集中，\n",
    "透過進階補值方法 KNN，將鐵達尼號中的 Titanic_train.csv 中的 age 的遺失值進行補值。    \n",
    "* step1: 觀察 Age 和 Pclass 和 Sex 是否有關連性?\n",
    "* step2: 如果有關連性，運用 KNN ，取出 Age 、 Pclass、 Sex的資料，以 Sex 與 Pclass 補 Age 遺失值。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "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": [
    "# import library\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import math\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 讀入資料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"Titanic_train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             missing_rate\n",
      "PassengerId      0.000000\n",
      "Survived         0.000000\n",
      "Pclass           0.000000\n",
      "Name             0.000000\n",
      "Sex              0.000000\n",
      "Age              0.198653\n",
      "SibSp            0.000000\n",
      "Parch            0.000000\n",
      "Ticket           0.000000\n",
      "Fare             0.000000\n",
      "Cabin            0.771044\n",
      "Embarked         0.002245\n"
     ]
    }
   ],
   "source": [
    "#計算每一行是否有遺失值，計算遺失比例\n",
    "missing_vals = df_train.isnull().sum()/len(df_train)\n",
    "missing_vals.sort_values(ascending=False)\n",
    "missing_vals = pd.DataFrame(missing_vals,columns=['missing_rate'])\n",
    "print(missing_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Age  Pclass\n",
       "5      1  NaN       2\n",
       "17     1  NaN       1\n",
       "19     0  NaN       2\n",
       "26     1  NaN       2\n",
       "28     0  NaN       2\n",
       "..   ...  ...     ...\n",
       "859    1  NaN       2\n",
       "863    0  NaN       2\n",
       "868    1  NaN       2\n",
       "878    1  NaN       2\n",
       "888    0  NaN       2\n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 細看遺失資料的樣態\n",
    "missing_index=data.isnull()\n",
    "display(data[missing_index.values==True])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 觀察 Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29.69911764705882\n",
      "14.526497332334042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0xfb08f58>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"Pclass\", y=\"Age\", hue=\"Sex\",\n",
    "               data=df_train,kind=\"box\")\n",
    "#男生女生的身高特性有差異\n",
    "# 觀察到不同艙等的年紀不太相同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 我們適著用 Pclass和 Sex 來透過 KNN 來補值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Pclass</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Age  Pclass\n",
       "5      1  NaN       2\n",
       "17     1  NaN       1\n",
       "19     0  NaN       2\n",
       "26     1  NaN       2\n",
       "28     0  NaN       2\n",
       "..   ...  ...     ...\n",
       "859    1  NaN       2\n",
       "863    0  NaN       2\n",
       "868    1  NaN       2\n",
       "878    1  NaN       2\n",
       "888    0  NaN       2\n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0     1    2\n",
       "5    1.0  22.0  2.0\n",
       "17   1.0  35.0  1.0\n",
       "19   0.0  26.0  2.0\n",
       "26   1.0  22.0  2.0\n",
       "28   0.0  26.0  2.0\n",
       "..   ...   ...  ...\n",
       "859  1.0  22.0  2.0\n",
       "863  0.0  26.0  2.0\n",
       "868  1.0  22.0  2.0\n",
       "878  1.0  22.0  2.0\n",
       "888  0.0  26.0  2.0\n",
       "\n",
       "[177 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ValueError: could not convert string to float: 'Male', sklearn 中的 KNN 只能處理數值型態\n",
    "#轉型 \n",
    "#透過 preprocessing 轉換\n",
    "from sklearn import preprocessing\n",
    "#下面兩行程式，讓 Male=1, FeMale-0.\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "df_train['Pclass']=le.fit_transform(df_train['Pclass'])\n",
    "\n",
    "df_train['Sex']=le.fit_transform(df_train['Sex'])\n",
    "\n",
    "data=df_train[['Sex','Age','Pclass']]\n",
    "# KNN\n",
    "#設定用多少鄰居資訊來補值\n",
    "value_neighbors=1\n",
    "from sklearn.impute import KNNImputer\n",
    "#進行 KNN的一些設定\n",
    "imputer = KNNImputer(n_neighbors=value_neighbors)\n",
    "#把資料放進去，進行補值\n",
    "df_filled = pd.DataFrame(imputer.fit_transform(data))\n",
    "#只呈現有遺失值的資料\n",
    "display(data[missing_index.values==True])\n",
    "#只呈現有遺失值的資料-呈現補值的結果\n",
    "display(df_filled[missing_index.values==True])\n"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}