Day_006_HW.ipynb

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   "source": [
    "# [作業目標]\n",
    "- 仿造範例的 One Hot Encoding, 將指定的資料進行編碼"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [作業重點]\n",
    "- 將 sub_train 進行 One Hot Encoding 編碼 (In[4], Out[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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    "id": "lh9gyloHVblR"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    "colab_type": "code",
    "id": "z4iLB9cKVblV"
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   "outputs": [],
   "source": [
    "# 設定 data_path, 並讀取 app_train\n",
    "dir_data = './data/'\n",
    "f_app_train = os.path.join(dir_data, 'application_train.csv')\n",
    "app_train = pd.read_csv(f_app_train)"
   ]
  },
  {
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   "source": [
    "## 作業\n",
    "將下列部分資料片段 sub_train 使用 One Hot encoding, 並觀察轉換前後的欄位數量 (使用 shape) 與欄位名稱 (使用 head) 變化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(307511, 1)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WEDNESDAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MONDAY</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MONDAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WEDNESDAY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>THURSDAY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  WEEKDAY_APPR_PROCESS_START\n",
       "0                  WEDNESDAY\n",
       "1                     MONDAY\n",
       "2                     MONDAY\n",
       "3                  WEDNESDAY\n",
       "4                   THURSDAY"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_train = pd.DataFrame(app_train['WEEKDAY_APPR_PROCESS_START'])\n",
    "print(sub_train.shape)\n",
    "sub_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>WEEKDAY_APPR_PROCESS_START</th>\n",
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      "text/plain": [
       "   WEEKDAY_APPR_PROCESS_START\n",
       "0                           6\n",
       "1                           1\n",
       "2                           1\n",
       "3                           6\n",
       "4                           4"
      ]
     },
     "execution_count": 8,
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     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Your Code Here\n",
    "\"\"\"\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "labelencoder = LabelEncoder()\n",
    "subtrain_le=pd.DataFrame(sub_train)\n",
    "subtrain_le['WEEKDAY_APPR_PROCESS_START'] = labelencoder.fit_transform(subtrain_le['WEEKDAY_APPR_PROCESS_START'])\n",
    "subtrain_le.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "     0    1    2    3    4    5    6\n",
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      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "onehotencoder = OneHotEncoder(categorical_features = [0])\n",
    "subtrain_str_ohe=onehotencoder.fit_transform(subtrain_le).toarray()\n",
    "pd.DataFrame(subtrain_str_ohe).head()"
   ]
  }
 ],
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