{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [EDA] 了解變數分布狀態: Bar & KDE (density plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [教學目標]\n",
    "- 以下程式碼將示範如何繪製各種分布圖, 更直覺地看出單一變數本身的分布情形\n",
    "- 分布圖包含 KDE(Kenel Density Estimation) 曲線, 以及 Bar(區間長條圖), 提供兩種不同感受\n",
    "- 順便熟悉 matplotlib 的幾種繪圖設定"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [範例重點]\n",
    "- 各種樣式的長條圖(Bar) (In[4], Out[4])   \n",
    "- 不同的 KDE 曲線與繪圖設定 (In[5], Out[5]) 以及切換不同 Kernel function 的效果 (In[6], Out[6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # 另一個繪圖-樣式套件\n",
    "%matplotlib inline\n",
    "\n",
    "# 忽略警告訊息\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path of read in data: ./data/application_train.csv\n"
     ]
    },
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 讀取檔案\n",
    "f_app = os.path.join(dir_data, 'application_train.csv')\n",
    "print('Path of read in data: %s' % (f_app))\n",
    "app_train = pd.read_csv(f_app)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 資料整理 ( 'DAYS_BIRTH'全部取絕對值 )\n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 以生存年數繪製分布圖\n",
    "plt.hist(app_train['DAYS_BIRTH'] / 365, edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');\n",
    "plt.show()\n",
    "\n",
    "# 改變繪圖樣式 (style)\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "# 改變樣式後再繪圖一次, 比較效果\n",
    "plt.hist(app_train['DAYS_BIRTH'] / 365, edgecolor = 'k', bins = 25)\n",
    "plt.title('Age of Client'); plt.xlabel('Age (years)'); plt.ylabel('Count');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 設定繪圖區域的長與寬\n",
    "plt.figure(figsize = (6, 4))\n",
    "\n",
    "# Kenel Density Estimation (KDE) plot: 會準時還貸者 - 下圖紅線\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'target == 0')\n",
    "# KDE plot: 不會準時還貸者 - 下圖藍線\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 1, 'DAYS_BIRTH'] / 365, label = 'target == 1')\n",
    "\n",
    "# 設定標題與 X, y 軸的說明\n",
    "plt.xlabel('Age (years)'); plt.ylabel('Density'); plt.title('Distribution of Ages');\n",
    "\n",
    "# 輸出繪圖 : 有的ipynb環境, 會在一格結束時自動輸出繪圖, 但加上這行能保證在不同環境都會畫出圖形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE, 比較不同的 kernel function\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Gaussian esti.', kernel='gau')\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Cosine esti.', kernel='cos')\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Triangular esti.', kernel='tri')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE, 比較不同的 kernel function\n",
    "# sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Gaussian esti.', kernel='gau')\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Cosine esti.', kernel='cos')\n",
    "# sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Triangular esti.', kernel='tri')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KDE, 比較不同的 kernel function\n",
    "# sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Gaussian esti.', kernel='gau')\n",
    "# sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Cosine esti.', kernel='cos')\n",
    "sns.kdeplot(app_train.loc[app_train['TARGET'] == 0, 'DAYS_BIRTH'] / 365, label = 'Triangular esti.', kernel='tri')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 補充說明: \n",
    "1. [KDE](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py)\n",
    "2. [什麼是 KDE](https://blog.csdn.net/unixtch/article/details/78556499)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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StNakpaWxceNGlFLs27ePAwcOABAdHc2gQYMA12UOCQkJ7l6TCRMmUFRUBMC6devYunWr+5xHjx6tNxmc7sorryQrK+us3V9vv/32edWtpXlsIvjAgQMZOHBgrfumTJni/tnX15f77rvvrOf42c9+1ug5hRCipb311luUlZXxwQcf4OPjw9ChQ93XcgQGBrrLnW3PQ8MwWLlyZbMuKpSWihBCtANBQUG1PqgrKioIDw/Hx8fnrNfE9e/fn/nz5/PDDz9gt9v5z3/+Q58+fQAYNWoUr7zyCjNnzgQgPz+fSy+9tM5znU5aKkII0Qoam3kIYLXZGp3V1VRhYWEMHjyYsWPHMmbMGGbNmsXUqVO55ppriIuLo1evXvUe17VrV2bPns2ECROIioqid+/eBAcHA/Dkk0/yyCOPkJycTE1NDUOHDuVPf/oT48aN46677uLDDz9s9YH6vLw8ZsyYweHDh/n4449ZsGABq1evbtHn6JB71MsmXQ2TKcW1tcc6gffXq7Kysla3UlO05NTb83Hs2DGCgoKoqalhxowZ/PznP+eaa85+XczZmFGv+n7/XjWlWAghOooFCxawbt06Tpw4wahRo7j66sZbWe2JJBUhhGhBjz32mNkhmEo26RJCeJ0O2CvvVc7n9y9JRQjhdSwWi1eMj3RENTU153XFvXR/CSG8jr+/P1VVVZw4caLJy7D7+fm1y50fPVmv03d+PFeSVIQQXkcp1eydB719Rtu5amv1ku4vIYQQLUaSihBCiBYjSUUIIUSLkaQihBCixUhSEUII0WIkqQghhGgxklSEEEK0GEkqQgghWozHLn7My8tj6dKlGIZBUlISkyZNqvV4dXU16enpFBUVERwcTEpKChERERQWFrJkyRJ3ucmTJzNkyBAAZs2ahb+/PxaLBavVSlpamqeqI4QQoh4eSSqGYZCRkcG8efNwOBw8/PDDxMfHEx0d7S6TlZVFUFAQixYtIjs7m2XLljF37lxiYmJIS0vDarVy6NAhHnzwQQYNGoTVagXg8ccfJyQkxBPVEOegof1ZmrLpkhCi7fFI91dhYSFRUVFERkZis9lITEwkJyenVpnc3FxGjx4NQEJCAvn5+Wit8fPzcyeQ6urqJq8DJIQQwvM80lIpLy/H4XC4bzscDgoKChosY7VaCQwMpKKigpCQEAoKCli8eDEHDhxg9uzZ7iQDkJqaCsC4ceNITk6u9/kzMzPJzMwEIC0tjfDw8Bat37mw2WxeEceZKu32Bh8LrCde44dyKv6xGEtIZ6oiLyDwzJzfwPnqO5c38tbX6Xy1x3q1xzpB26uXR5JKfWvzn9niOFuZ3r17s3DhQnbv3s0LL7xA//798fX15cknnyQsLIzDhw/zxz/+kW7dutGvX78650lOTq6VcLxhcTZvXSTOOHq0wccqf4xXa43+fC36syz47mswDFcBmw9EREHfK1Ddup/1eSq9sO718dbX6Xy1x3q1xzqB99TLq7YTdjgclJWVuW+XlZURGhpabxmHw4HT6aSyshL7Gd9yo6Oj8ff3p7i4mNjYWMLCwgDo1KkTgwcPprCwsN6kIlrGqfERnf8lfLUBgoKhX3+I6QHHjuJTXkr19kL45D30Rb0gfjgqoHn7jAsh2jaPjKnExsZSUlJCaWkpNTU1rF+/nvj4+FplBg0axJo1awDYsGEDcXFxKKUoLS3F6XQCcODAAfbu3UuXLl2oqqri+PHjAFRVVbFp0ya6dz/7t2Nx/nRJMeRthIt6wY23oQYkoMIjURfG4j9iHNxwK1w+GHYVwco30Du3mR2yEMKDPNJSsVqtTJ8+ndTUVAzDYMyYMcTExLB8+XJiY2OJj49n7NixpKenM3v2bOx2OykpKQBs2bKFd955B6vVisViYcaMGYSEhLB//36effZZAJxOJ8OHD6d///6eqE6HpY9VwLqPISQUEsbUO2lCWa1wxWBXS2V9Fqz9ED1wGPTrL5MshOgAlO6Am0Hv3bvX7BC8pp/0TA1NAdZOJ3z4Nhw5BNdORoV0rlPGbrdz9LQxGe2sgexPYOc2uORSV3fYj9uUtpUpxd76Op2v9liv9lgn8J56NXVMRa6oF01T9D2UlcKwsfUmlPooqw1GjHeNu3yfD//9GH1qUF8I0S7JdsKiaQo2Q+cw6N6zWYcppWBQIjogEL5YD8qCvjKplYIUQphNkopolC4/4GqlDB5+zuMiql9/17TxLz8Di0KPvAplsTZ+oBCiTZHuL9G4gs1gtUKPi8/rNCpuAFwxBIq2ov/+onSFCdEOSUtFnJWurobtW+HCXig///M+n7o8Hm0Y6P9+DL5+8PNfy6wwIdoRSSri7HYUQHU19G7Bi0qvGIyKugD98bvg5wc3/lISixDthCQVcXYFm6FTGHSJarFTKqVQk6fDyRPoD1a4lne5/hZJLEK0A5JURIN0+UHXAH38uQ/QN0QpBbfeDdXV6H//E44fh8nT3NexCCHaJkkqomG7t7v+73l+A/QNURYLTJ0NAYHozHeh8ij88l7XVfnNJPu2COEdJKmIhpXsBkeXFhmgb4iyWGDKHRAUjF75OrriMJZpv0EFd2q15xRCtB7paxD10tUn4cB+iIpuvPB5Ukphuf7nqF/MhO/yMObPRm/KafxAIYTXkaQi6rd/L2gDusZ47Ckto6/B8uhCCO6EsehJjJefQ+/e4bHnF0KcP+n+EvUr2e264DGi5WZ9NYWKvgjLowvR7y5DZ72H/mw19OqHShzr2vgrohvYg0FrOHkCqirh8A/oPTuh6vhPG4YpBZ1C0SdPoHz9PFoHIToySSqifvt2Q0RX16KQHqY/+8SVzG68DbZtga356NfScS+nbbNBTU2TzmV8+LZrzbKefaB3P5SPj/sxGcQXouVJUhF16OOV8EM59LzE1DiUnz/064/uewVUHMbSPRZduhd+OAS+vuDnD37+qJBQjO3fg38AWCyuVorTCYfKoPwA7NsDX2TDN7noSy6DfldI60WIViJJRdRVstv1fysO0jc0Bbg+SikI6Yy6fDANXS2jjh6ue2dwJ/eqyvrAPvj2K/gmF7ZvRY8Ydw5RCyEaIwP1oq6SYte6XGHhZkfSYlSXKNToa+CqG13jMavexvjwLVnUUogW5rGWSl5eHkuXLsUwDJKSkpg0aVKtx6urq0lPT6eoqIjg4GBSUlKIiIigsLCQJUuWuMtNnjyZIUOGNOmcovm01q7xlK7R7XLZFBXRFX3dZNiwBv3mK65dKaenoGw+jR4rhGicR5KKYRhkZGQwb948HA4HDz/8MPHx8URH/9S9kpWVRVBQEIsWLSI7O5tly5Yxd+5cYmJiSEtLw2q1cujQIR588EEGDRqEUqrRc4pzcOQHqDzmketTzKL8/NEjr4LNeeicdei9u2DkVSjbT38OMogvxLnxSFIpLCwkKiqKyMhIABITE8nJyamVAHJzc5k8eTIACQkJvPzyy2it8fP7aUC1urra/e25KecU52DfHtf/Xb3v99iccZjGKKUgbgDaxxc2fgpZ76HHXIvy8W2x5xCiI/JIUikvL8fhcLhvOxwOCgoKGixjtVoJDAykoqKCkJAQCgoKWLx4MQcOHGD27NlYrdYmnfOUzMxMMjMzAUhLSyM83PyxApvN5hVxnKnshzKcAUEERXVrdveX1WLFbre3UmStZOBQqoODqfrkfaxrVhEwYTLKx4fAH18bb32dzld7rFd7rBO0vXp5JKlorevcd+YH1tnK9O7dm4ULF7J7925eeOEF+vfv36RznpKcnExycrL79sGDB5sVf2sIDw/3ijjO5CzZDY4Ijh071uxj7XY7R48ebYWoWlnX7jB8HM51H3H0gxUw+hoqf3xtvPV1Ol/tsV7tsU7gPfXq1q1bk8p5ZPaXw+GgrKzMfbusrIzQ0NAGyzidTiorK+t8642Ojsbf35/i4uImnVM0jz56BCoOQ3iE2aF4nLqoFwwdBXt2QXaWzAoT4hx5JKnExsZSUlJCaWkpNTU1rF+/nvj4+FplBg0axJo1awDYsGEDcXFxKKUoLS3F6XQCcODAAfbu3UuXLl2adE7RTNt/7D4MjzQ3DpOoi+NgQALsKED/86V6W8NCiLPzSPeX1Wpl+vTppKamYhgGY8aMISYmhuXLlxMbG0t8fDxjx44lPT2d2bNnY7fbSUlJAWDLli288847WK1WLBYLM2bMICQkBKDec4pzp7d/7/qhA7ZU3OIGQNVx9Or3wdEFfnGn2REJ0aYo3QG/ju3du9fsELymn/R0zufnw+4dqOt/fk7Ht9kxlTNorVFbvkHn/peQ++ZzrO9As0Nqcd74/jtf7bFO4D318qoxFeH9tNZQtLXDdn2dTimFmp4CF8dx5C+p6O+/MTskIdoMSSrCZf9e13a+klQAUD6+WO55FGvUBRgvPIXes8vskIRoEySpCAD09q2uHySpuKkgO6G/XwC+fhh/mY8+VNb4QUJ0cJJUhEvR9+AXAJ1kWja4rt431q7iRN5GGJ4MRw5jPP2Aa1sAIUSDJKkI4MeWykW9UBZ5S5xJhYXDqKvgh0MYLz6Frq42OyQhvJbspyLQJ0/A7u2o8bLKc0NUt+7oYaNhfRZG2m9hxLg6CVgWoRRCWioCYNc2cDpRJu/06O1UbB8YlOj6feWsk4sjhaiHJBWBLvpxkL6HJJXGqH79XRdIbv0Wvs4xOxwhvI50fwnYUQBhXVCdQpHv3k0wIAGqjrv2vLfZUJe2v4sjhThXklQEekcBXNTb7DDaDKUUOmE0OJ3w1Qa0xYrqd4XZYQnhFaT7q4PTFUfgwD5UD0kqzaEsFrgyCbrHwhfZ6C1y1b0QIElF7HStTKykpdJsymKBEckQ3QNy1mF8/K7ZIQlhOkkqHZzeXgBKwYW9zA6lTVIWK4wcDxfGov+VgfHeP2VWmOjQZEylg9M7CiAqGhUQaHYobZayWtHDx6G6XYh+93XXIP7Nv3LvRGqsXdXgsXJti2hvJKl0YFpr2L4Vdekgs0Np85TFgvrVHPDzR3/4NlQcgdtnoWzyJyY6FnnHd2TlB1zbB/e42OxI2gVlscCtd0FIZ/TK19FHfsBy12/NDksIj5IxlY5shwzStzSlFJbrf4765b3w7VcYzz4qi1CKDsVjLZW8vDyWLl2KYRgkJSUxaVLtdaaqq6tJT0+nqKiI4OBgUlJSiIiIYNOmTSxbtoyamhpsNhu33347l156KQDz58/n0KFD+Pr6AjBv3jw6derkqSq1eXr7VrDZIPois0NpdywjxqNDOmP87RlY9RZ67HUoWQFadAAeSSqGYZCRkcG8efNwOBw8/PDDxMfHEx0d7S6TlZVFUFAQixYtIjs7m2XLljF37lyCg4N56KGHCAsLY9euXaSmprJkyRL3cXPmzCE2NtYT1Wh39I5CiO6B8vExO5R2SV0xBMsDT2MsnOdKLKOvQUU2bUtWIdoqj3R/FRYWEhUVRWRkJDabjcTERHJyaq+blJuby+jRowFISEggPz8frTU9evQgLCwMgJiYGKqrq6mWpcfPmzacsKNQLnpsZapHb7j6ZvAPgMyV6J3bzA5JiFblkZZKeXk5DofDfdvhcFBQUNBgGavVSmBgIBUVFYSEhLjLbNy4kR49euBz2jfrF198EYvFwtChQ7n55pvd0zhPl5mZSWZmJgBpaWmEh4e3aP3Ohc1mMzWOmuLtlJ04TvBlgwg4LY5Ku/2cz2m1WLGfx/HeqDl1Cmzg9azs2g198+0c/+AtnGs/xHdEMr4/rhfW0DGtzez3X2toj3WCtlcvjySV+i4GO/PDv7EyxcXFLFu2jEcffdR935w5cwgLC+P48eMsWLCAtWvXMmrUqDrnSU5OJjk52X374MGD51SPlhQeHm5qHMaXnwNwNDyKY6fFYRw9es7ntNvtHD2P471Rc+pU2cDreep3qsdcB+s+4sS6TE4cKof+Qxs8prWZ/f5rDe2xTuA99erWrWldt03u/srNzcXpdJ5TMA6Hg7Kyn/b3LisrIzQ0tMEyTqeTyspK9zfEsrIynn32WWbNmkVUVJT7mFPdYgEBAQwfPpzCwsJziq9D2v69q0sm8gKzI+kwlM0Go66GXv0g/0v4fC3aMMwOS4gW1eSksnz5cu68804yMjLqdF01JjZ/YXfIAAAgAElEQVQ2lpKSEkpLS6mpqWH9+vXEx8fXKjNo0CDWrFkDwIYNG4iLi0MpxbFjx0hLS+OWW26hT58+7vJOp5MjR44AUFNTwxdffEFMTEyz4urIdOF3ENtHtg/2MGWxQMIo954seunz6HP8siaEN2py99ef//xnduzYwbp161iwYAF+fn6MHDmSESNGEBERcdZjrVYr06dPJzU1FcMwGDNmDDExMSxfvpzY2Fji4+MZO3Ys6enpzJ49G7vdTkpKCgCrVq1i3759rFixghUrVgCuqcN+fn6kpqbidDoxDIPLLrusVheXaJg+VgF7dqIGjzA7lA5JKYUekAA2H/SG1ejqE1juuB9lk1l4ou1T+hxWv9Na88033/D3v/+dXbt20adPH5KTk7nyyiuxtIFvvnv37jU7BFP7SfXXn2Ok/xHLA0+hLrm01mNnW6eqMR19TOWcVFWh/9/L0D8By12/9diyLt7ST9+S2mOdwHvq1dQxlWa/g/ft28e6detYt24dSimmTJlCeHg4q1atYuPGjTzwwAPNDlZ4li7YDFYbyHRi01nGT8KwWtH/fAnjpT9j+fWDsl6YaNOa/O5dtWoV69atY9++fQwbNox7772Xiy/+ac2ooUOHcscdd7RKkKJl6cLNcFEvlK+f2aEIwJJ0PYbW6OX/h/HSs1h+/YAkFtFmNfmdm5eXx4QJExg8eDC2et7wfn5+0kppA/TJE66LHpMnmh2KOI0leaIrsfwrA/3yc3DHfa69WoRoY5o8ANKvXz+GDRtWJ6G899577p+vuEL26fZ62wvAWYPqHWd2JOIMlnE3oP7nV+icdehXFsl0Y9EmNbmlsmLFCiZOrPvtdsWKFUyYMKFFgxKtRxd86/qhV19zAxH1slx1E8bJk66l8w/ug6Gj6lwoLBt7CW/WaFLJz88HXNeFnPr5lP379xMQENA6kYlWoQs2wwUXooLa13Iq7YmaMAVdtMV1gaTFih48vN7lh4TwRo0mlcWLFwOupelP/QyuufadO3dm+vTprRedaFHa6YRtW1DDRpsdivhRg1O4+w8FpxO++xqsFvTAREksok1oNKm88MILAKSnp3Pvvfe2ekCiFe3eASeOu5YJEV5NKYUelAiGAZu/drVY+g+VxCK8XpPHVCShtH26cDMAqrcklbZAKYUePBwM549dYRb05YPNDkuIszprUpk7dy7PPfccADNnzmyw3OndYsJ76S3fgCMCFdbF7FBEEyml0ENHuVosm3LBMNAjr5YWi/BaZ00qd911l/vn2bNnt3owovXo6pOw+StUYpLZoYhmUkqhh40BZYH8L9ErXoWbp0piEV7prEnl9FWB+/WTLpM2bcsmOHkCdcWQ81rfS5hDKYVOGAUWhf7wLaiphp/NkFWmhddp8jvyvffeY8eOHQBs3bqVmTNncu+997J169bWik20IJ23EfwC4JLLzA5FnCOlFAwZiUqeiP7k3+il/4uuqTE7LCFqaXJSef/9991L3L/xxhtMmDCBm266iVdeeaW1YhMtRBsG+uscuHQAykeWV2/LlFKon81ATboNvWENxotPoU+cMDssIdyanFQqKysJDAzk+PHj7Nixg2uuuYaxY8d6xTLyohE7t8HhctQVQ82ORLQApRSW636Guv0eyP8SY8Gj6B/KzQ5LCKAZScXhcPD999+TnZ1N3759sVgsVFZWton9Uzo6/fVGsFhQl8c3Xli0GZaRV2OZ+TvYsxPjqQfQu7aZHZIQTb9O5bbbbmPhwoXYbDbuv/9+AL788kt69erVasGJlqG//hx69UMFBZsdimgBdSZajLsBVv8H40+/wzLtN6j44eYEJgTNSCoDBw5kyZIlte5LSEggISGhScfn5eWxdOlSDMMgKSmJSZMm1Xq8urqa9PR0ioqKCA4OJiUlhYiICDZt2sSyZcuoqanBZrNx++23c+mlrt0Ki4qKeOGFFzh58iQDBgxg2rRpMs3yDPrgfti9AzVZltNpr1RYOPra/4GvNmAseQa1NR81eTrKx9fs0EQH1Ky+q8rKSgoLC8nPzyc/P58tW7awZcuWRo8zDIOMjAweeeQRnnvuObKzs9m9e3etMllZWQQFBbFo0SKuu+46li1bBkBwcDAPPfQQCxYsYNasWSxatMh9zEsvvcRdd93FX/7yF/bt20deXl5zqtMh6K9zAFBXDDE5EtGaVEAglgdSUeMnoVf/B+PpB9H79pgdluiAmtxSWbNmDRkZGfj7++Pr+9M3IKUU6enpZz22sLCQqKgoIiMjAUhMTCQnJ4fo6Gh3mdzcXCZPngy4WkAvv/wyWmt69OjhLhMTE0N1dTXV1dUcPXqU48ePu3efHDlyJDk5OQwYMKCpVeoQ9JfZ0DUGFdm0/aVF26XXfwKR3WDMtbA+C2P+bNfClH0uwzr6WrPDEx1Ek5PKG2+8wX333XdOH9rl5eU4HA73bYfDQUFBQYNlrFYrgYGBVFRUEBIS4i6zceNGevTogY+PT73nLC+vfwZMZmYmmZmZAKSlpREeHt7sOrQ0m83W6nHUFG+nbOu32H95D0GnPVelvXWWvbdarNhb6dxmaZN16nMpRvSFVK39COcX2Vh2b6fz0BHYYn76guaJ95+ntcc6QdurV5OTimEY57yzo9a6zn1njn00Vqa4uJhly5bx6KOPNli+IcnJySQnJ7tvHzx4sMnHtpbw8PBWj8N4959gs1HZfxjHT3su4+jRVnk+u93O0VY6t1nabp0UesR4iCnEyFlHWcrtqJFXo67/OSqks0fef57WHusE3lOvbt2a1tvR5DGVG264gRUrVmCcwxanDoeDsrIy9+2ysjJCQ0MbLON0OqmsrHR/QywrK+PZZ59l1qxZREVFNXjOsLCwZsfWXukTVej1WaiBV6KCO5kdjjCBUgrVozdMvAU18mr02lUYj96F8e9/Yhz5wezwRDvV5JbK+++/zw8//MDKlSvrdAc0tkpxbGwsJSUllJaWEhYWxvr165kzZ06tMoMGDWLNmjVcfPHFbNiwgbi4OJRSHDt2jLS0NG655ZZaa5GFhoYSEBDA1q1b6d27N2vXruXqq9vvNqsNrdfV0NayOmcdHD+GGtV+fyeiaZR/AJZf3I0eOwHjrVfRK1/nwAdvogaPcL0/Luota4iJFtPkpHI+qxRbrVamT59OamoqhmEwZswYYmJiWL58ObGxscTHxzN27FjS09OZPXs2drudlJQUAFatWsW+fftYsWIFK1asAGDevHl06tSJO+64gxdffJGTJ0/Sv39/GaQ/jV7zAXTrDrJ3iviR6hqNddaj6D278PvsE46v/o9rcD+4E6pff+h7Bapbd4joJttNi3OmdHMGJ9oJb1haprn9pM1pqegdBRip96NuuRPL2AlNPtf5arvjDw1rj3UCV70qysugeDuqxone/BUcPfJTgaBg8PEB/0AICISQTtApFEJCoXNYg9eDNdRy9gRvGXtoad5Sr6aOqTS5pVJdXc2bb75JdnY2FRUVvPrqq3z99deUlJS0626ntkh/ugp8/VAJY8wORXgx5esHsX2wjLwabRiwfw/s34PeXwIH96G3bYGqSijdC9tPW408yI6+qLer2yys7cxKEp7R5KTy6quvUl5ezpw5c3jqqacA13Ujr776qiQVL6IPH0JvWINKHIsKDDI7HNFGKIsFusa4rmn68b7TW7S6phqOHIbyA64FSjfnwbdfoS+4EAYPl8kgwq3JSeXzzz/nL3/5C/7+/u6mb1hYWIPXhghz6E/+Dc4a1PgbzQ5FtBFN6Q5VNh8IC3f969UXXXUctm1xbXH873+iLx0EcTKmKZqRVGw2W53pxEeOHCE4WBYp9Bb6eKVrgH7gMLmCXrQq5R8AcQPQPXpDbjZ8/Tns3YmOl1ZLR9fkeYQJCQmkp6dTWloKwKFDh8jIyCAxMbHVghPNo9euguPHsFx9s9mhiA5CBdpRI6+CkeOh/KBrzbGS3Y0fKNqtJieVW2+9lYiICO6//34qKyuZM2cOoaGh7vW6hLl0dTX645WuaaEX9TY7HNHBqAt7wbhJUHUcI+1B9Pf5ZockTNLk7q99+/ZxwQUXcOONN2IYBkOGDKF79+6tGZtoBr1hNRwuxzI9xexQRAelukSiHnkW4y9/wPjLfCxz5qMuudTssISHNZpUtNYsXryYTz/9FIfDQWhoKOXl5bz55puMHDmSmTNnyh4mJjLWrnKtg7byDQjrgnGgBLV2H2DuNQOiY9Kbv4Irk+DjdzGeewySJrjH9+T92DE0mlQyMzPZvHkzqamptXZ5LCws5Pnnn+fjjz9m/PjxrRqkaMS+PXDkB7gyqVaCb62LHIU4GxUQiB53A3z0DmS9h066HhXR1eywhIc0Oqaydu1apk2bVmfb4F69evGrX/2KdevWtVpwookKvgVfP+gea3YkQgCuxMK4GyDQ7kosB/ebHZLwkEaTyu7du+nXr/71o/r161dnB0fhWfp4JezaDrF9ULYmD5EJ0epUYBCMmwh+AfDJe+jd280OSXhAo0nFMAwCAgLqfSwgIOCclsIXLajwO9CGLBwpvJIKtLsSi82GsfAxdEmx2SGJVtboV1un00l+fsPTAyWpmEcbBhRshsgLUJ1CGz9ACBMoe4hrjGXNBxjPPorl/j+6VkMW7VKjSaVTp05n3S/l9O1+hYeVFMOxChg4zOxIhDgrFdIZdX8qxoJ5rsRy3x9Q0T0aP1C0OY0mlRdeeMETcYhzsXUz+AdAjPxxCu+nukZjefCpHxPLPCxz/4C6UCaXtDey3VsbpU9UwZ4d0PMSlNVqdjhCNImK7IblwafAzx/j2UfQ+V+aHZJoYZJU2qo9O0FrmUYs2hzVJQrLQ3+C8CiMRX/A+FSup2pPPDYHNS8vj6VLl2IYBklJSUyaNKnW49XV1aSnp1NUVERwcDApKSlERERQUVHBwoULKSwsZPTo0cyYMcN9zPz58zl06BC+vr7AT9sMdwjFO1w78oVHmB2JEE1S52Lc4Umw7iP0P17E2LcHdfNUmRbfDnjkFTQMg4yMDObNm4fD4eDhhx8mPj6e6Ohod5msrCyCgoJYtGgR2dnZLFu2jLlz5+Lj48OUKVPYtWsXxcV1pyPOmTOH2NiO9W1dO2tg707ocbEskSPaLOXjix59LeRmozPfRedtgBHjUPafJv/I0i5tj0e6vwoLC4mKiiIyMhKbzUZiYiI5OTm1yuTm5jJ69GjAtcx+fn4+Wmv8/f3p06ePuzUicC3LUlMjA/SizVMWC2rICBgxDg4fgvf+hd5ZaHZY4jx4pKVSXl6Ow+Fw33Y4HBQUFDRYxmq1EhgYSEVFRaNTll988UUsFgtDhw7l5ptvrvebe2ZmJpmZmQCkpaURHm7+vto2m61ZcVTa7e6fq0qKqfbxwd7rEpTVe7oLrBYr9tPibA/aY53AC+t16QCM7j04/vG/MdZ+hLXHdvyGJ2Fvxt9Ic/+m2oq2Vi+PfCJprevcd+aHf1PKnGnOnDmEhYVx/PhxFixYwNq1axk1alSdcsnJySQnJ7tvHzx4sKmht5rw8PBmxWEcPQr8+HvaUQjdunPseFVrhXdO7HY7R3+Ms71oj3UCL62XxYYeNxG+20TN1znUvJFB5clq1OjrmjTW0ty/qbbCW+rVrVvTdpP1SPeXw+GgrKzMfbusrIzQ0NAGyzidTiorKxv9JhUWFga4losZPnw4hYUdoNl8cD8crwS5cEy0Q8piRcUNgOt/DhFd0cszMB6/F/3l+nq/eArv45GWSmxsLCUlJZSWlhIWFsb69euZM2dOrTKDBg1izZo1XHzxxWzYsIG4uLiztlScTifHjh0jJCSEmpoavvjiCy677LLWror5ireDssAFssyFaL9UcAh67HVYQsMx3nwFY3EaxPbBctNU1MVxQN3ZZJV2O8bRozK4bzKPJBWr1cr06dNJTU3FMAzGjBlDTEwMy5cvJzY2lvj4eMaOHUt6ejqzZ8/GbreTkvLTDoazZs2isrKSmpoacnJymDdvHuHh4aSmpuJ0OjEMg8suu6xWF1e7VbwdIruh/PzNjkSIVqWUQl0+GEvcQHR2JnrlGxh/fhgui8cy6TazwxMNULoDtin37t1rdgjNH1NZuwp9tALe/jvEX4nqe0UrRnduvLKf/jy1xzpB26nX6a0OfeIEevV76A9WwPFj0ONiGDDUtRIyP9WpvbVU2tqYivdMHRKN27/H9X/kBebGIYQJlJ8f6uqb0SOvQv/nTfTH78DObei4/hA3wOzwxI8kqbQl+/e6dngMdTReVoh2SgXaUf/zK5wBgfDVZ7ApF4q2UjNqPITJChNmk7W/2pL9eyGim1xFLwSuwXw18ioYfwNYLBx//0302g/Rhw+ZHVqHJkmljdDHjsLRIxDVtH5NIToKFXkBTJiC75DhULwD4/F7MTZ+KlOQTSLdX23F/h8nF0RKUhEdR51FKBugrFb8BiVyMioGvvkC/X8L0F+ux/KLmaiQzq0cpTidtFTaiv17XOMpnWU8RYiGqE6hWH73J9T//Ao25WLMn43O22h2WB2KJJW2Yv9eiOiKsshLJsTZKIsVy1U3YZn3HHQOw3ghFePVReiqSrND6xDkE6oN0D+UQcVh6foSohnUBd2xPPIs6pr/QWd/gvHkXPSOgsYPFOdFxlTaAP19vusHSSpCNKrOOEx4BIybCP/NxEj7LerG21HjJkmrv5XIb7Ut2Pot+PhCaNtZ/loIb6Iiu8GEn8EVQ9FvvoKR/kf0cekOaw2SVNoAvfUbGU8R4jwpP38sdz+EuvUu2PwVxtMPovebv2RTeyPdX15OHznk2ulx4DCzQxGizVNKocZch+7WHeOvaRhP3Y9l5sPo0oaTS3tbS6y1yVdfb7fte9f/XaLMjUOIdsBYu8q1OOv+PZB8A/j4Yfzv4+h9e8wOrd2QpOLl9LYtYLWBo4vZoQjRrqjgENcSL/ZOkPW+dIW1EEkqXk4XbYHuPb1qL3oh2gvlH+CaGRZkh6z30KUlZofU5klS8WK6pgZ2FqJi+5gdihDtlgoIhHE3QEAQfLoKXXnM7JDaNEkq3mz3djh5EnpKUhGiNanAIBh9DVRXw38/RhuG2SG1WR7rU8nLy2Pp0qUYhkFSUhKTJk2q9Xh1dTXp6ekUFRURHBxMSkoKERERVFRUsHDhQgoLCxk9ejQzZsxwH1NUVMQLL7zAyZMnGTBgANOmTWtXy8LrHwfpVewl6PwvTI5GiPZNdQ5DDx0J67Pgm1y4YojZIbVJHkkqhmGQkZHBvHnzcDgcPPzww8THxxMdHe0uk5WVRVBQEIsWLSI7O5tly5Yxd+5cfHx8mDJlCrt27aK4uLjWeV966SXuuusuevfuzdNPP01eXh4DBrSjHeCKtkBnByqsC7KItxCtT8X2cQ3Yb8pFR3RFdY1pcKVkmWpcP490fxUWFhIVFUVkZCQ2m43ExERycnJqlcnNzWX06NEAJCQkkJ+fj9Yaf39/+vTpg6+vb63yhw4d4vjx41x88cUopRg5cmSdc7Z1etsW6HmJ2WEI0bEMHgGdQmF9Frq62uxo2hyPtFTKy8txOH5ast3hcFBQUNBgGavVSmBgIBUVFYSEhDT5nOXl5fWWzczMJDMzE4C0tDTCw81f7sRms501DuehMg6WlWK//mcEhYdTabd7MLpzY7VYsbeBOJujPdYJ2me9WrJONWOu4fg7r+NbkI/fkBH1lgn00OdIY58V3sYjSaW+HdjOHPtoSpnGyjckOTmZ5ORk9+2DBw82+djWEh4eftY49JefAVAZGcPxgwcxjh71VGjnzG63c7QNxNkc7bFO0D7r1aJ1Cu4MPXpz8qvPORnTExXcqU6RSg99jjT2WeEp3bo1bUFbj3R/ORwOysrK3LfLysoIDQ1tsIzT6aSysvKs3zrqO2dYWFgLR24eXfTjRY8XxpodihAd08BhYFGQu97sSNoUjySV2NhYSkpKKC0tpaamhvXr1xMfH1+rzKBBg1izZg0AGzZsIC4u7qwtldDQUAICAti6dStaa9auXVvnnG2ZLvreddGjj2/jhYUQLU4F2uGyeNi9Hb13l9nhtBke6f6yWq1Mnz6d1NRUDMNgzJgxxMTEsHz5cmJjY4mPj2fs2LGkp6cze/Zs7HY7KSkp7uNnzZpFZWUlNTU15OTkMG/ePKKjo7njjjt48cUXOXnyJP379283M7/cFz3K7BIhzNX3Cij8DnKz0ROiZaXwJlC6OYMT7cTeveav8XO2flK9owAj9X7Unb/FMng4UM/GQ15I+unbjvZYr9aqk95RCOs+ghHjUBf1dt/vqSnFMqYizpsu/A5AlmcRwht07+maYrzpi2ZNEOqoJKl4o21bICwcFdZ2phEK0V4piwUuGwSHy2FXkdnheD1JKl5Ib9uCiu1rdhhCiFMu7AUhneGbXGmtNELWU/cixtpV6GMVcOggGt0mxlGE6AiUxYK+bBBkfwLF211dYqJe0lLxNgf2uf6XnR6F8C4X9YbgTtJaaYQkFW9zYJ/rosdQR+NlhRAeoywWiBsA5Qdh326zw/FaklS8Tek+CI9AWaxmRyKEOFPPSyAgEL7NMzsSryVJxYvo6mo4dBC6dDU7FCFEPZTVCpdcBiXF6N3bzQ7HK0lS8SZlpaA1RMh4ihBe6+I4sNnQH75jdiReSZKKNzk1SB8eaW4cQogGKT9/6NUPnbMWXX7A7HC8jiQVb3JgH3QKdb1phRDeq+/loDX6k/fMjsTrSFLxEtowXElFphIL4fWUPQQ16Er02lXoymNmh+NVJKl4i/174OQJSSpCtBHqqpug6jhaLlKuRZKKl9BbvnH9ENG0lUCFEOZSF8ZC3yvQmf+WvexPI0nFS+jv8iAoGIJDzA5FCNFElqtvhsPl6A2rzQ7Fa0hS8QLacML330DX6LPudimE8DJ9r4DuPdEfve0aFxWSVLzCziKoPAZR0WZHIoRoBqUU6uqbYd8e+Ppzs8PxCh5bpTgvL4+lS5diGAZJSUlMmjSp1uPV1dWkp6dTVFREcHAwKSkpREREAPD222+TlZWFxWJh2rRp9O/fH3BtM+zv74/FYsFqtZKWluap6rQo/d2PSz5EXWBuIEKIZlMDE9HhkRirVmDpP7TD9zZ4JKkYhkFGRgbz5s3D4XDw8MMPEx8fT3T0T9/Ms7KyCAoKYtGiRWRnZ7Ns2TLmzp3L7t27Wb9+PQsXLuTQoUM8+eSTPP/881h+3Cv68ccfJySkbY9D6C2bIPoiVECg2aEIIZpJWa2o8TeiX/8rbNnk6hLrwDzS/VVYWEhUVBSRkZHYbDYSExPJycmpVSY3N5fRo0cDkJCQQH5+PlprcnJySExMxMfHh4iICKKioigsLPRE2B6hT56Ags2oDv5GFKItU8PHQWg4xrvLOvyy+B5pqZSXl+Nw/LSUu8PhoKCgoMEyVquVwMBAKioqKC8vp3fv3u5yYWFhlJeXu2+npqYCMG7cOJKTk+t9/szMTDIzMwFIS0sjPNz8bXptNhvh4eGc+DqHH2qq6TR0BM6yUrPDOi9WixW73W52GC2qPdYJ2me9PF2nwDM+RyqnTKfir88QsqsQv0HDWux5Tn1WtBUeSSr1Ze4z+x0bKnO2rP/kk08SFhbG4cOH+eMf/0i3bt3o169fnXLJycm1Es7BgwebE36rCA8P5+DBgxgb14LVypHIGPTOtr3/td1u5+jRo2aH0aLaY52gfdbL03WqPONzRF8xFBwR/PD3xVi692qxsZVTnxVm69atadfQeaT7y+FwUFZW5r5dVlZGaGhog2WcTieVlZXY7fY6x5aXlxMWFgbg/r9Tp04MHjy4TXaL6c1fQ89LUP4BZocihDgPyuaDuv7nsLMQvt5odjim8UhSiY2NpaSkhNLSUmpqali/fj3x8fG1ygwaNIg1a9YAsGHDBuLi4lBKER8fz/r166murqa0tJSSkhJ69epFVVUVx48fB6CqqopNmzbRvXt3T1SnxehjFbBrG6pvf7NDEUK0AJUwBiK6Yrz7eoe9bsUj3V9Wq5Xp06eTmpqKYRiMGTOGmJgYli9fTmxsLPHx8YwdO5b09HRmz56N3W4nJSUFgJiYGIYNG8Z9992HxWJhxowZWCwWDh8+zLPPPgu4WjbDhw93TzVuK/TmPNAa1fdys0MRQrQAZbWirr8FnbEQ/VkW6sr6x3nbM6U74FSFvXv3mh0C4eHh7H9iLmwvwPJMBspixWjjC9NJP33b0R7r5ek6WUZeXe/92jAwnvkdlJZgeXIxKuj8Jg/ImIpoEuNYBeR/gRo8QvajF6IdURYLllvvhqMV6Hf/YXY4HuexK+pFbSc++xRqalBDRpodihDiHJytZ8Ey8mrU6KvRa1ahh49DdY/1YGTmkpaKSarWfQQRXeGiXmaHIoRoBeqG28AejPH6kg41aC9JxQT6h3JO5n+JGjKyw68TJER7pYLsqJt/Bdu2oLP+bXY4HiNJxQQ6979gGNL1JUQ7pxLHwhVD0CteRe/aZnY4HiFJxQT687XYevRGdY0xOxQhRCsw1q7CWLsKve5DuDgOfPwwnn8CXXXc7NBanSQVD9OlJbB9K/4jxpkdihDCA5R/AAxPhiM/oP/5ktnhtDpJKh6mV78PFgv+wzveRVFCdFQq6gK4dCA6OxNj7Ydmh9OqZEqxB+myA+g1/0ElJmHtEgVecEGTEMJDrhgMgF62GN0pDPXj7fZGWioepN9fDoCa8HOTIxFCeJqyWLHc9RDE9MT42zPo7VvNDqlVSFLxEL1vDzo7EzXqGpSji9nhCCFMoPwDsMz5PYR0xvjLH9Alu80OqcVJUvEQvfJ18PFFXfs/ZocihDCRCgnF8pv5oBTGMw+1uxaLJBUP0Du3oXPWoZImokJCGz9ACNEuuacab/0Gxk4AFMaffofzn38zO7QWI0mllenDhzAWPw3BnVBXTTI7HCGEl1AhneDqmyCkM2T9B2P1++1if3tJKq1IV4hube8AAA5mSURBVFVi/OUPUHEYy+zHUIHta09wIcT5UQGBMP4G6BqNfn0JxqIn0UcOmR3WeZGk0kp0TQ3GX/8Eu7djueu3qB69zQ5JCOGFlK8fjL0OdcudsGUTxvw5GNmfoJ1Os0M7J5JUWoHeuQ3j2Ufg269Qt92Durx9zkcXQrQMpRSWsROwzFsIjgj0K89jPDYLY8PqNpdcPHbxY15eHkuXLsUwDJKSkpg0qfb4QnV1Nenp6RQVFREcHExKSgoREREAvP3222RlZWGxWJg2bZp72+DGzulp+sA+9Advov/7MdhDUNPnYhk2xtSYhBBth+rWHcsjz0LeRoyVr6MznuPgilfR/YeiBl0JveNQVu/e1M8jScUwDDIyMpg3bx4Oh4OHH36Y+Ph4oqOj3WWysrIICgpi0aJFZGdns2zZMubOncvu3btZv349Cxcu5NChQzz55JM8//zzAI2esyVprcFZAydPwMmTcPQIHCpDHzoIO7ehv8uDA/vAanXN8rp+ioyhCCGaTSkFAxKwXDEENn2Oz1cbOLE+C73mA/DxheiLUN17QlQ0dApDdQ4Dewj4+YFfAPj6uj6HTNpR1iNJpbCwkKioKCIjIwFITEwkJyenVgLIzc1l8uTJACQkJPDyyy+jtSYnJ4fExER8fHyIiIggKiqKwsJCgEbP2ZKMx++FkuL6H/QPgEsuQyVdj7p8MKpLVKvEIIToOJTFAv0T6Jw8gQN7dkP+l+ht3/3/9u49KKryDeD4d3cREBi5rLcWxFyBSeimIoqCqFFMjdM0jVA5TZkZppmT5YyYXZxRB2aAWUspbH4Vk2UXm3TG/ujCTKJgltfJEB0wEURkgRUEdhF39/z+4Of5RWoCLWwHn88MM5zl7Ps+z3JmnznvOed9UWr+QPl1Pzg6ALjp/WI6HegNoAN0etCB3vJZ9zWcATQoRcVms2E0GtVto9FIZWXlTfcxGAwEBATQ1taGzWYjOvr/F7nDwsKw2WxqO3/X5jXFxcUUFxcDkJOTg8lk6nsS/9nV9/fcwnVxPLnY430MthBvBzAAhmJOMDTzGoo5AYRPMMMEs7fD6JVBuVB/o3uv/7ri4c32udl9271p85rU1FRycnLIycnpTbiDIisry9sheJzkpB1DMa+hmBNoL69BKSpGo5Hm5mZ1u7m5mdDQ0Jvu43K5sNvtBAUFXfdem81GWFhYr9oUQggxuAalqEycOJH6+nqsVitOp5MDBw4QHx/fY5+pU6eyd+9eAA4ePEhcXBw6nY74+HgOHDjA1atXsVqt1NfXExUV1as2hRBCDC7D+vXr1w90J3q9nrFjx7Jlyxa+++47kpOTmTFjBl9++SWdnZ2YTCYiIyMpLS1lx44dVFdXk5mZSVBQEMHBwbS3t7Nt2zZKS0tZvHgxJpPppm1qidmsjTHSvpCctGMo5jUUcwJt5aVThsJkM0IIIf4V5Il6IYQQHiNFRQghhMfIGvUDrKmpiYKCAlpaWtDpdKSmpvLII4/Q3t6OxWKhsbGRUaNGsWrVKoKCtPMEfldXF2+//TZOpxOXy8WMGTPIyMjAarWyefNm2tvbmTBhAi+//DI+Pto6zNxuN1lZWYSFhZGVlaX5nF566SX8/f3R6/UYDAZycnI0f/wBdHR0UFhYSG1tLTqdjmXLlmEymTSb14ULF7BYLOq21WolIyODlJQUbeWkiAFls9mUM2fOKIqiKHa7XVm5cqVSW1urbN++Xdm1a5eiKIqya9cuZfv27d4Ms8/cbrficDgURVGUq1evKmvXrlVOnz6t5OfnK6WlpYqiKMq2bduU77//3pth9suePXuUzZs3K9nZ2YqiKJrPafny5Upra2uP17R+/CmKomzZskUpLi5WFKX7GGxvbx8SeSmKorhcLmXJkiWK1WrVXE4y/DXAQkND1Ts3hg8fTnh4ODabjUOHDpGSkgJASkoKhw4d8maYfabT6fD39we6nytyuVzodDrKy8vVu/DmzJmjubyam5s5evQoDzzwAND9kK3Wc7oRrR9/drudiooK5s2bB4CPjw+BgYGaz+uaEydOMHbsWEaNGqW5nLRzDj8EWK1Wzp49S1RUFK2trerDmqGhoVy+fNnL0fWd2+1mzZo1XLx4kbS0NMaMGUNAQACG/82i+ucpdbSiqKiIp59+GofDAUBbW5vmcwLYtGkTAA8++CCpqamaP/6sVisjRozgvffe49y5c5jNZhYtWqT5vK4pKytj1qxZAJrLSYrKIOns7CQ/P59FixYREBDg7XA8Qq/Xk5ubS0dHB3l5edTV1Xk7pH/kyJEjBAcHYzabKS8v93Y4HrNhwwbCwsJobW1l48aN/Zv77l/G5XJx9uxZFi9eTHR0NB9//DG7d+/2dlge4XQ6OXLkCAsXLvR2KP0iRWUQOJ1O8vPzSU5OZvr06QAEBwdz6dIlQkNDuXTpEiNGjPBylP0XGBhIbGwslZWV2O12XC4XBoNBnVJHK06fPs3hw4c5duwYXV1dOBwOioqKNJ0ToMYbHBzMtGnTqKqq0vzxZzQaMRqN6mSzM2bMYPfu3ZrPC+DYsWNMmDCBkJDu6TG1lpNcUxlgiqJQWFhIeHg48+fPV1+Pj4+npKQEgJKSEqZN09bqkJcvX6ajo3vq7a6uLk6cOEF4eDhxcXEcPHgQgL1792pq6pyFCxdSWFhIQUEBr7zyCnfffTcrV67UdE6dnZ3qUF5nZye//fYbkZGRmj/+QkJCMBqNXLhwAei+BhEREaH5vKDn0Bdo77tCnqgfYKdOneKtt94iMjJSnUX5qaeeIjo6GovFQlNTEyNHjuTVV1/9d98m+Bfnzp2joKAAt9uNoigkJiayYMECGhoarrv9dtiwYd4Ot8/Ky8vZs2cPWVlZms6poaGBvLw8oHvIKCkpiccff5y2tjZNH38A1dXVFBYW4nQ6GT16NMuXL0dRFE3ndeXKFZYtW8bWrVvVYXKt/a+kqAghhPAYGf4SQgjhMVJUhBBCeIwUFSGEEB4jRUUIIYTHSFERQgjhMVJUhBhkP/74I0VFRd4Oo4e8vDyOHz/u7TDEECBFRYj/Wb9+Pc899xxXr14dsD6cTifffPMNjz766ID10R+PPfYYn3/+ubfDEEOAFBUh6J6gsKKiAoDDhw8PWD+HDh3CZDIN+lQvLpfrb/8eFRWFw+HgzJkzgxSRGKpk7i8hgH379hETE0NUVBQlJSUkJiaqf2tra6OgoICKigpMJhP33Xcf5eXlbNiwAYC6ujo++ugj/vjjD0aMGMETTzzBzJkzb9jPsWPHiI2NVbezs7O5//77efjhh9XXVq9eTUZGBgkJCX/b9tGjR/niiy9oaGggICCAuXPnkpGRAXQXyRUrVvDiiy+yc+dORo8ezbp16ygsLOT48eO43W7uuOMO1qxZo84xFRsby9GjR5k4caJnP1xxW5GiIgTdcyrNnz+f6Oho1q1bR0tLi/pl++GHH+Lv788HH3xAY2MjmzZtYuTIkUD3fFobN24kIyOD119/nXPnzrFp0ybGjRvHuHHjruuntraWyZMnq9spKSl8++23alGprq7GZrMxZcqUW7bt5+fHihUriIiIoLa2lo0bN3LnnXeSkJCgtn/y5EksFgt6vZ6SkhLsdjvvv/8+w4YNo7q6Gl9fX3XfiIgITp06NSCfr7h9yPCXuO2dOnWKpqYmEhMTMZvNjBkzhtLSUqB7zZhffvmFjIwM/Pz8iIiIUBdMgu6zhVGjRjF37lwMBgNms5np06erE1D+VUdHB8OHD1e3p02bRn19PfX19UD3GdPMmTPx8fG5ZdtxcXFERkai1+sZP348s2bN4uTJkz36S09Px9/fH19fXwwGA+3t7Vy8eBG9Xo/ZbO6xDIO/v786SagQ/SVnKuK2t3fvXu699151SvGkpCT1zOXy5cu4XC6MRqO6/59/b2xspLKykkWLFqmvuVwuZs+efcO+AgMD1VmDAYYNG0ZiYiL79+9nwYIFlJWV8dprr/Wq7crKSnbs2EFNTQ1OpxOn06muUHmjWGfPnk1zczObN2/GbreTnJzMk08+iY9P99dAZ2cngYGBffnohLiOFBVxW+vq6uLnn3/G7XbzwgsvAN13aHV0dFBdXU1kZCQGg4Hm5mZ1cavm5mb1/UajkdjYWN58881e9Td+/Hj1rOSaOXPmsGXLFu666y78/PyIiYnpVdvvvvsuaWlprF27Fl9fX4qKiq5bFfDazNjQveRueno66enpWK1WsrOzMZlM6pK858+fZ/z48b3KQ4ibkeEvcVv79ddf0ev1WCwWcnNzyc3NxWKxMGnSJPbt24derychIYGdO3dy5coV6urq1LUtAKZOnUp9fT379u1Tzxaqqqo4f/78DfubPHnydUNUMTEx6PV6Pvnkkx5nOLdq2+FwEBQUhK+vL1VVVeqQ3c38/vvv1NTU4Ha7CQgIwMfHB73+/18BFRUVPa73CNEfUlTEba2kpIS5c+cycuRIQkJC1J+0tDT279+Py+Xi+eefx263k5mZydatW5k1a5a6nsrw4cN54403KCsrY+nSpWRmZvLZZ5/hdDpv2N/UqVOpq6u7bp372bNnU1NTQ3JysvrardpesmQJX331Fc888wxff/11jzvWbqSlpYX8/HyeffZZVq1axaRJk9T+qqqq8PPzIyoqqt+fpRAg66kI0WeffvopLS0trFixol/vLy4u5vz58z2ulZSUlFBcXKzepjzY8vLymDdvHlOmTPFK/2LokGsqQtxCXV0dTqeTyMhIzpw5w08//cTSpUv73V5qamqP7StXrvDDDz/w0EMP/dNQ+2316tVe61sMLVJUhLgFh8PBO++8w6VLlwgODmb+/PkeWyf8+PHj5Ofnc88995CUlOSRNoXwJhn+EkII4TFyoV4IIYTHSFERQgjhMVJUhBBCeIwUFSGEEB4jRUUIIYTH/BfbiyrQQ3X2IwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 完整分布圖 (distplot) : 將 bar 與 Kde 同時呈現\n",
    "sns.distplot(app_train.loc[app_train['TARGET'] == 1, 'DAYS_BIRTH'] / 365, label = 'target == 1')\n",
    "plt.legend()\n",
    "plt.xlabel('Age (years)'); plt.ylabel('Density'); plt.title('Distribution of Ages');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# It's your turn\n",
    "## 除了直接觀察變項以外，我們也可以分群比較\n",
    "1. 自 20 到 70 歲，切 11 個點，進行分群比較 (KDE plot)\n",
    "2. 以年齡區間為 x, target 為 y 繪製 barplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# To do: 變項的分群比較\n",
    "1. 自 20 到 70 歲，切 11 個點，進行分群比較 (KDE plot)\n",
    "2. 以年齡區間為 x, target 為 y 繪製 barplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業目標]\n",
    "- 試著調整資料, 並利用提供的程式繪製分布圖"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 如何將資料依照歲數, 將 20 到 70 歲切成11個區間? (In[4], Hint : 使用 numpy.linspace),  \n",
    "  送入繪圖前的除了排序外, 還要注意什麼? (In[5])\n",
    "- 如何調整對應資料, 以繪製長條圖(bar chart)? (In[7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入需要的套件\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # 另一個繪圖-樣式套件\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# 忽略警告訊息\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 設定 data_path\n",
    "dir_data = './data/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Path of read in data: ./data/application_train.csv\n"
     ]
    },
    {
     "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>SK_ID_CURR</th>\n",
       "      <th>TARGET</th>\n",
       "      <th>NAME_CONTRACT_TYPE</th>\n",
       "      <th>CODE_GENDER</th>\n",
       "      <th>FLAG_OWN_CAR</th>\n",
       "      <th>FLAG_OWN_REALTY</th>\n",
       "      <th>CNT_CHILDREN</th>\n",
       "      <th>AMT_INCOME_TOTAL</th>\n",
       "      <th>AMT_CREDIT</th>\n",
       "      <th>AMT_ANNUITY</th>\n",
       "      <th>...</th>\n",
       "      <th>FLAG_DOCUMENT_18</th>\n",
       "      <th>FLAG_DOCUMENT_19</th>\n",
       "      <th>FLAG_DOCUMENT_20</th>\n",
       "      <th>FLAG_DOCUMENT_21</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_HOUR</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_DAY</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_WEEK</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_MON</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_QRT</th>\n",
       "      <th>AMT_REQ_CREDIT_BUREAU_YEAR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100002</td>\n",
       "      <td>1</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>202500.0</td>\n",
       "      <td>406597.5</td>\n",
       "      <td>24700.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100003</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>270000.0</td>\n",
       "      <td>1293502.5</td>\n",
       "      <td>35698.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100004</td>\n",
       "      <td>0</td>\n",
       "      <td>Revolving loans</td>\n",
       "      <td>M</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>67500.0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>6750.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>100006</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>F</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>135000.0</td>\n",
       "      <td>312682.5</td>\n",
       "      <td>29686.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>100007</td>\n",
       "      <td>0</td>\n",
       "      <td>Cash loans</td>\n",
       "      <td>M</td>\n",
       "      <td>N</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>121500.0</td>\n",
       "      <td>513000.0</td>\n",
       "      <td>21865.5</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SK_ID_CURR  TARGET NAME_CONTRACT_TYPE CODE_GENDER FLAG_OWN_CAR  \\\n",
       "0      100002       1         Cash loans           M            N   \n",
       "1      100003       0         Cash loans           F            N   \n",
       "2      100004       0    Revolving loans           M            Y   \n",
       "3      100006       0         Cash loans           F            N   \n",
       "4      100007       0         Cash loans           M            N   \n",
       "\n",
       "  FLAG_OWN_REALTY  CNT_CHILDREN  AMT_INCOME_TOTAL  AMT_CREDIT  AMT_ANNUITY  \\\n",
       "0               Y             0          202500.0    406597.5      24700.5   \n",
       "1               N             0          270000.0   1293502.5      35698.5   \n",
       "2               Y             0           67500.0    135000.0       6750.0   \n",
       "3               Y             0          135000.0    312682.5      29686.5   \n",
       "4               Y             0          121500.0    513000.0      21865.5   \n",
       "\n",
       "   ...  FLAG_DOCUMENT_18 FLAG_DOCUMENT_19 FLAG_DOCUMENT_20 FLAG_DOCUMENT_21  \\\n",
       "0  ...                 0                0                0                0   \n",
       "1  ...                 0                0                0                0   \n",
       "2  ...                 0                0                0                0   \n",
       "3  ...                 0                0                0                0   \n",
       "4  ...                 0                0                0                0   \n",
       "\n",
       "  AMT_REQ_CREDIT_BUREAU_HOUR AMT_REQ_CREDIT_BUREAU_DAY  \\\n",
       "0                        0.0                       0.0   \n",
       "1                        0.0                       0.0   \n",
       "2                        0.0                       0.0   \n",
       "3                        NaN                       NaN   \n",
       "4                        0.0                       0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_WEEK  AMT_REQ_CREDIT_BUREAU_MON  \\\n",
       "0                         0.0                        0.0   \n",
       "1                         0.0                        0.0   \n",
       "2                         0.0                        0.0   \n",
       "3                         NaN                        NaN   \n",
       "4                         0.0                        0.0   \n",
       "\n",
       "   AMT_REQ_CREDIT_BUREAU_QRT  AMT_REQ_CREDIT_BUREAU_YEAR  \n",
       "0                        0.0                         1.0  \n",
       "1                        0.0                         0.0  \n",
       "2                        0.0                         0.0  \n",
       "3                        NaN                         NaN  \n",
       "4                        0.0                         0.0  \n",
       "\n",
       "[5 rows x 122 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 讀取檔案\n",
    "f_app = os.path.join(dir_data, 'application_train.csv')\n",
    "print('Path of read in data: %s' % (f_app))\n",
    "app_train = pd.read_csv(f_app)\n",
    "app_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 資料整理 ( 'DAYS_BIRTH'全部取絕對值 )\n",
    "app_train['DAYS_BIRTH'] = abs(app_train['DAYS_BIRTH'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35.0, 40.0]    42868\n",
      "(40.0, 45.0]    41406\n",
      "(30.0, 35.0]    39440\n",
      "(45.0, 50.0]    35135\n",
      "(50.0, 55.0]    34942\n",
      "(55.0, 60.0]    33120\n",
      "(25.0, 30.0]    32862\n",
      "(60.0, 65.0]    27476\n",
      "(20.0, 25.0]    12159\n",
      "(65.0, 70.0]     8103\n",
      "Name: YEARS_BINNED, dtype: int64\n"
     ]
    },
    {
     "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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "      <th>YEARS_BINNED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>9461</td>\n",
       "      <td>25.920548</td>\n",
       "      <td>(25.0, 30.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16765</td>\n",
       "      <td>45.931507</td>\n",
       "      <td>(45.0, 50.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19046</td>\n",
       "      <td>52.180822</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>19005</td>\n",
       "      <td>52.068493</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>19932</td>\n",
       "      <td>54.608219</td>\n",
       "      <td>(50.0, 55.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TARGET  DAYS_BIRTH  YEARS_BIRTH  YEARS_BINNED\n",
       "0       1        9461    25.920548  (25.0, 30.0]\n",
       "1       0       16765    45.931507  (45.0, 50.0]\n",
       "2       0       19046    52.180822  (50.0, 55.0]\n",
       "3       0       19005    52.068493  (50.0, 55.0]\n",
       "4       0       19932    54.608219  (50.0, 55.0]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 根據年齡分成不同組別 (年齡區間 - 還款與否)\n",
    "age_data = app_train[['TARGET', 'DAYS_BIRTH']] # subset\n",
    "age_data['YEARS_BIRTH'] = age_data['DAYS_BIRTH'] / 365 # day-age to year-age\n",
    "\n",
    "#自 20 到 70 歲，切 11 個點 (得到 10 組)\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "bin_cut =  np.linspace(20, 70, num=11) \n",
    "age_data['YEARS_BINNED'] = pd.cut(age_data['YEARS_BIRTH'], bins = bin_cut) \n",
    "\n",
    "# 顯示不同組的數量\n",
    "print(age_data['YEARS_BINNED'].value_counts())\n",
    "age_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 繪圖前先排序 / 分組\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "year_group_sorted = age_data['YEARS_BINNED'].unique()\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "for i in range(len(year_group_sorted)):\n",
    "    sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \\\n",
    "                              (age_data['TARGET'] == 0), 'YEARS_BIRTH'], label = str(year_group_sorted[i]))\n",
    "    \n",
    "    sns.distplot(age_data.loc[(age_data['YEARS_BINNED'] == year_group_sorted[i]) & \\\n",
    "                              (age_data['TARGET'] == 1), 'YEARS_BIRTH'], label = str(year_group_sorted[i]))\n",
    "plt.title('KDE with Age groups')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>TARGET</th>\n",
       "      <th>DAYS_BIRTH</th>\n",
       "      <th>YEARS_BIRTH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YEARS_BINNED</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(20.0, 25.0]</th>\n",
       "      <td>0.123036</td>\n",
       "      <td>8532.795625</td>\n",
       "      <td>23.377522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(25.0, 30.0]</th>\n",
       "      <td>0.111436</td>\n",
       "      <td>10155.219250</td>\n",
       "      <td>27.822518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30.0, 35.0]</th>\n",
       "      <td>0.102814</td>\n",
       "      <td>11854.848377</td>\n",
       "      <td>32.479037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(35.0, 40.0]</th>\n",
       "      <td>0.089414</td>\n",
       "      <td>13707.908253</td>\n",
       "      <td>37.555913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(40.0, 45.0]</th>\n",
       "      <td>0.078491</td>\n",
       "      <td>15497.661233</td>\n",
       "      <td>42.459346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(45.0, 50.0]</th>\n",
       "      <td>0.074171</td>\n",
       "      <td>17323.900441</td>\n",
       "      <td>47.462741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50.0, 55.0]</th>\n",
       "      <td>0.066968</td>\n",
       "      <td>19196.494791</td>\n",
       "      <td>52.593136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(55.0, 60.0]</th>\n",
       "      <td>0.055314</td>\n",
       "      <td>20984.262742</td>\n",
       "      <td>57.491131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60.0, 65.0]</th>\n",
       "      <td>0.052737</td>\n",
       "      <td>22780.547460</td>\n",
       "      <td>62.412459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(65.0, 70.0]</th>\n",
       "      <td>0.037270</td>\n",
       "      <td>24292.614340</td>\n",
       "      <td>66.555108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TARGET    DAYS_BIRTH  YEARS_BIRTH\n",
       "YEARS_BINNED                                     \n",
       "(20.0, 25.0]  0.123036   8532.795625    23.377522\n",
       "(25.0, 30.0]  0.111436  10155.219250    27.822518\n",
       "(30.0, 35.0]  0.102814  11854.848377    32.479037\n",
       "(35.0, 40.0]  0.089414  13707.908253    37.555913\n",
       "(40.0, 45.0]  0.078491  15497.661233    42.459346\n",
       "(45.0, 50.0]  0.074171  17323.900441    47.462741\n",
       "(50.0, 55.0]  0.066968  19196.494791    52.593136\n",
       "(55.0, 60.0]  0.055314  20984.262742    57.491131\n",
       "(60.0, 65.0]  0.052737  22780.547460    62.412459\n",
       "(65.0, 70.0]  0.037270  24292.614340    66.555108"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 計算每個年齡區間的 Target、DAYS_BIRTH與 YEARS_BIRTH 的平均值\n",
    "age_groups  = age_data.groupby('YEARS_BINNED').mean()\n",
    "age_groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 8))\n",
    "\n",
    "# 以年齡區間為 x, target 為 y 繪製 barplot\n",
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "px = age_data['YEARS_BINNED']\n",
    "py = age_data['TARGET']\n",
    "sns.barplot(px, py)\n",
    "\n",
    "# Plot labeling\n",
    "plt.xticks(rotation = 75); plt.xlabel('Age Group (years)'); plt.ylabel('Failure to Repay (%)')\n",
    "plt.title('Failure to Repay by Age Group');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}