D21 運用實際資料集進行資料視覺化練習 Data_Visualization_Day_4作業.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q-gigy72KsQ7"
   },
   "source": [
    "目標: 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: 不同型態的類別要套用的對應圖像是不一樣的\n",
    "\n",
    "作業: 取得另一個 dataset: titanic, tips\n",
    "\n",
    "(1) 做條形圖\n",
    "\n",
    "(2) 異常值落點分析\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "1aQwvVYyLQAa"
   },
   "outputs": [],
   "source": [
    "# 導入必要的程式庫\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 取得資料集\n",
    "df = sns.load_dataset('titanic')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 408
    },
    "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()\n",
    "# 選取特徵值: sex, survived, 主要是這個資料集用來做存活率做預測, 所以Y label 一定是用survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "\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",
       "  </tbody>\n",
       "</table>\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",
       "     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  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "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",
    "# x, y 表示特徵值, data 給定dataframe \n",
    "\n",
    "ax = sns.barplot(x=\"sex\", y=\"survived\", data=df, hue=\"class\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "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": {
    "id": "Xdz4PCtKmMJY"
   },
   "source": [
    "使用範例的圖形檢視另一個資料集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1021,
     "status": "ok",
     "timestamp": 1614245329175,
     "user": {
      "displayName": "陳宇春",
      "photoUrl": "",
      "userId": "17550852567328422073"
     },
     "user_tz": -480
    },
    "id": "-3AZcdq3eFvP",
    "outputId": "fc8a1725-c287-4926-b28d-dcb66e9da13a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   total_bill  244 non-null    float64 \n",
      " 1   tip         244 non-null    float64 \n",
      " 2   sex         244 non-null    category\n",
      " 3   smoker      244 non-null    category\n",
      " 4   day         244 non-null    category\n",
      " 5   time        244 non-null    category\n",
      " 6   size        244 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.3 KB\n"
     ]
    }
   ],
   "source": [
    "# 某些情況下, 會需要把兩種類型的圖混合\n",
    "# 在箱型圖上繪製分類散點圖, 可以更容易理解異常值的分布\n",
    "# 導入必要的程式庫\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 取得資料集\n",
    "df2 = sns.load_dataset('tips')\n",
    "# 利用df.info 檢查有哪些特徵值\n",
    "df2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.boxplot(data=df2, orient=\"h\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "AmE-61EJhAjd"
   },
   "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": [
    "# 先行挑選兩組特徵: \n",
    "\n",
    "sns.boxplot(x=\"total_bill\", y=\"time\", data=df2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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": [
    "sns.stripplot(x=\"smoker\", y=\"time\", data=df2, jitter=True)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}