Homework/Day_21_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q-gigy72KsQ7"
   },
   "source": [
    "目標:  \n",
    "使用 Seaborn 自帶的dataset, 利用 PANDAS 處理來繪製資料集的可是畫圖表\n",
    "\n",
    "重點:  \n",
    "不同型態的類別要套用的對應圖像是不一樣的\n",
    "\n",
    "作業:  \n",
    "取得另一個 dataset: titanic, \n",
    "\n",
    "(1) 做條形圖\n",
    "\n",
    "(2) 利用 facet grid 繪圖並分析\n",
    "\n",
    "(3) 繪製小提琴圖\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1aQwvVYyLQAa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>Second</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>B</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>23.4500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.7500</td>\n",
       "      <td>Q</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Queenstown</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
       "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
       "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
       "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
       "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
       "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
       "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
       "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
       "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
       "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
       "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
       "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
       "\n",
       "       who  adult_male deck  embark_town alive  alone  \n",
       "0      man        True  NaN  Southampton    no  False  \n",
       "1    woman       False    C    Cherbourg   yes  False  \n",
       "2    woman       False  NaN  Southampton   yes   True  \n",
       "3    woman       False    C  Southampton   yes  False  \n",
       "4      man        True  NaN  Southampton    no   True  \n",
       "..     ...         ...  ...          ...   ...    ...  \n",
       "886    man        True  NaN  Southampton    no   True  \n",
       "887  woman       False    B  Southampton   yes   True  \n",
       "888  woman       False  NaN  Southampton    no  False  \n",
       "889    man        True    C    Cherbourg   yes   True  \n",
       "890    man        True  NaN   Queenstown    no   True  \n",
       "\n",
       "[891 rows x 15 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取得資料集\n",
    "df = sns.load_dataset('titanic')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "colab_type": "code",
    "id": "XYhQE52jLhRW",
    "outputId": "b309f0ed-b76b-4cec-99a7-5cc8ff97c971"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 15 columns):\n",
      " #   Column       Non-Null Count  Dtype   \n",
      "---  ------       --------------  -----   \n",
      " 0   survived     891 non-null    int64   \n",
      " 1   pclass       891 non-null    int64   \n",
      " 2   sex          891 non-null    object  \n",
      " 3   age          714 non-null    float64 \n",
      " 4   sibsp        891 non-null    int64   \n",
      " 5   parch        891 non-null    int64   \n",
      " 6   fare         891 non-null    float64 \n",
      " 7   embarked     889 non-null    object  \n",
      " 8   class        891 non-null    category\n",
      " 9   who          891 non-null    object  \n",
      " 10  adult_male   891 non-null    bool    \n",
      " 11  deck         203 non-null    category\n",
      " 12  embark_town  889 non-null    object  \n",
      " 13  alive        891 non-null    object  \n",
      " 14  alone        891 non-null    bool    \n",
      "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
      "memory usage: 80.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "9ydYgLopMvK-",
    "outputId": "3de39179-5f0c-4629-d90b-7f1ef5b4bdee"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直接使用 PANDAS DataFrame 當作參數\n",
    "# 條形圖()顯示分類變數和連續變數之間的關係。數據以矩形條表示，其中條的長度表示該類別中數據的比例。\n",
    "\n",
    "sns.barplot( x='sex', y ='survived', hue='class', data=df )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YBNLtpZT9cUn"
   },
   "source": [
    "在上面的示例中,我們可以看到每個班級中男性和女性的平均存活率。從情節中,我們可以理解,女性存活人數比男性多。在男性和女性中,更多的存活率來自頭等艙。\n",
    "\n",
    "barplot 中的特殊情況是顯示每個類別中的觀測值的\"否\",而不是計算第二個變數的統計資訊。\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AJW0HUUryOnQ"
   },
   "source": [
    "\n",
    "繪製數據子集的小倍數。 FacetGrid 示例,\n",
    "\n",
    "FacetGrid 類有助於可視化一個變數的分佈,以及使用多個面板在數據集子集中分別顯示多個變數之間的關係。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "6AfkVr8FKaiw",
    "outputId": "96ad0acb-4b11-433c-ab87-b184a750ae1b"
   },
   "outputs": [
    {
     "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": [
    "# 瞭解性別在各艙等的分布的存活率\n",
    "\n",
    "g = sns.FacetGrid( df, col='sex' )\n",
    "g.map( sns.barplot, 'pclass', 'survived', ci=None, order=[1,2,3], palette='Blues' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass\n",
       "1    136\n",
       "2     87\n",
       "3    119\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 先檢視各艙位存活人數，此時可以使用groupby函數進行分類\n",
    "# 其中 survived＝1表示存活，survived＝0表示死亡，將survived加總即為各艙等生存人數。\n",
    "\n",
    "df.groupby( 'pclass' ).survived.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass  sex   \n",
       "1       female    91\n",
       "        male      45\n",
       "2       female    70\n",
       "        male      17\n",
       "3       female    72\n",
       "        male      47\n",
       "Name: survived, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加上性別\n",
    "survived = df.groupby( ['pclass','sex'] ).survived.sum()\n",
    "survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "colab_type": "code",
    "id": "r6gKtgS1Emes",
    "outputId": "b5f59c6f-8f4a-4faa-9b25-e37902d04245"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived.plot( kind='bar' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "colab_type": "code",
    "id": "HQvoM0jKEmg0",
    "outputId": "3c1451e7-672e-4d70-a977-a511ae47e448"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>survived</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>female</th>\n",
       "      <td>3</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>77</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>female</th>\n",
       "      <td>6</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>91</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>female</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>300</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "survived         0   1\n",
       "pclass sex            \n",
       "1      female    3  91\n",
       "       male     77  45\n",
       "2      female    6  70\n",
       "       male     91  17\n",
       "3      female   72  72\n",
       "       male    300  47"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 使用 pd.crosstab 函數繪製交叉表，交叉表可以很直觀的依據艙位等級及性別來查看存活人數及死亡人數。\n",
    "# 繪製堆疊條形圖，x軸代表依據艙等分成男性及女性，y軸代表人數，其中藍色代表死亡人數，橘色代表存活人數。\n",
    "survived_counts = pd.crosstab( [df.pclass, df.sex], df.survived )\n",
    "survived_counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "colab_type": "code",
    "id": "rt_WucueJXfa",
    "outputId": "f28f5184-bd49-48cf-c56b-0943566d8d52"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_counts.plot.bar( stacked=True )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "pRYfeRVMKetm",
    "outputId": "565c85ff-00f7-40be-9c24-d6ce8d4b9439"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直接使用 PANDAS Dataframe 當作參數\n",
    "# 條形圖()顯示分類變數和連續變數之間的關係。數據以矩形條表示，其中條的長度表示該類別中數據的比例。\n",
    "\n",
    "sns.violinplot( data=survived_counts )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "id": "Pk8RXLriMe-c",
    "outputId": "f6f23946-f7da-496b-b564-f1cdf94fe172"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "# 瞭解性別在各艙等的分布的存活率\n",
    "'''\n",
    "在這邊coding\n",
    "g = sns.FacetGrid\n",
    "g.map\n",
    "h = sns.FacetGrid\n",
    "h.map\n",
    "'''\n",
    "g = sns.FacetGrid( df, col='survived' )\n",
    "g.map( plt.hist, 'pclass' )\n",
    "plt.show()\n",
    "\n",
    "h = sns.FacetGrid( df, col='survived' )\n",
    "h.map( plt.hist, 'sex' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ylzsxRLV4MJm"
   },
   "source": [
    "\n",
    "可以嘗試其他的參數對照組合\n",
    "\n",
    " 0   survived        \n",
    " 1   pclass         \n",
    " 2   sex            \n",
    " 3   age          \n",
    " 5   parch           \n",
    " 6   fare        \n",
    " 7   embarked   \n",
    " 8   class   \n",
    " 9   who           \n",
    " 10  adult_male   \n",
    " 11  deck      \n",
    " 12  embark_town   \n",
    " 13  alive         \n",
    " 14  alone        "
   ]
  }
 ],
 "metadata": {
  "colab": {
   "name": "Data Visualization_Day-4作業.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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}