D21_Data_Visualization_Day_4作業.ipynb

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    "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, \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"
   ]
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     "text": [
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "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"
   ]
  },
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     "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: 80.7+ KB\n"
     ]
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    "df.info()"
   ]
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       "      <td>False</td>\n",
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       "      <td>Queenstown</td>\n",
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       "     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]"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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",
    "在這邊coding\n",
    "sns.barplot\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sex', ylabel='survived'>"
      ]
     },
     "execution_count": 4,
     "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.barplot(x = \"sex\", y = \"survived\", hue = \"class\", data = df)"
   ]
  },
  {
   "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": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 225
    },
    "colab_type": "code",
    "id": "6AfkVr8FKaiw",
    "outputId": "96ad0acb-4b11-433c-ab87-b184a750ae1b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21bfd594ba8>"
      ]
     },
     "execution_count": 8,
     "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": [
    "# 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",
    "'''\n",
    "data = sns.load_dataset('titanic')\n",
    "g = sns.FacetGrid(data,col='sex')\n",
    "g.map(plt.hist,\"survived\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "colab_type": "code",
    "id": "r6gKtgS1Emes",
    "outputId": "b5f59c6f-8f4a-4faa-9b25-e37902d04245"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pclass,sex'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "#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()\n",
    "\n",
    "#加上性別\n",
    "survived=df.groupby(['pclass','sex']).survived.sum()\n",
    "survived.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 10,
     "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": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 341
    },
    "colab_type": "code",
    "id": "rt_WucueJXfa",
    "outputId": "f28f5184-bd49-48cf-c56b-0943566d8d52"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pclass,sex'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "'''\n",
    "在這邊coding\n",
    "使用survived_counts.plot做對照組\n",
    "'''\n",
    "survived_counts.plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "colab_type": "code",
    "id": "pRYfeRVMKetm",
    "outputId": "565c85ff-00f7-40be-9c24-d6ce8d4b9439"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='survived'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "# 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",
    "'''\n",
    "在這邊coding\n",
    "sns.violinplot\n",
    "'''\n",
    "sns.violinplot(data=survived_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 433
    },
    "colab_type": "code",
    "id": "Pk8RXLriMe-c",
    "outputId": "f6f23946-f7da-496b-b564-f1cdf94fe172",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "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",
    "# PS: 跟第一次做 Face.Grid 有何不同??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x21bfdc17a90>"
      ]
     },
     "execution_count": 16,
     "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"
    },
    {
     "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": [
    "g = sns.FacetGrid(data=df, col=\"survived\")\n",
    "g.map(plt.hist, \"pclass\")\n",
    "g = sns.FacetGrid(data=df, col=\"survived\")\n",
    "g.map(plt.hist, \"sex\")"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}