Homework/Day_11_HW.ipynb

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    "作業目標:<br>\n",
    "1. 運用編碼處理類別資料<br>\n",
    "2. 補缺失值"
   ]
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    "作業重點: <br>\n",
    "1. 類別編碼有多種方法，需分辨使用方法與時機<br>\n",
    "2. 補缺失值須因應情境決定如何補值"
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    "題目 : \n",
    "將以下問卷資料的職業(Profession)欄位缺失值填入字串'others'，更進一步將字串做編碼。\n",
    "此時用什麼方式做編碼比較適合?為什麼?"
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     "text": [
      "1.0.5\n"
     ]
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   "source": [
    "import pandas as pd\n",
    "print( pd.__version__ ) "
   ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Profession</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>engineer</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "      Sex Profession\n",
       "0    male    teacher\n",
       "1    male   engineer\n",
       "2  female       None\n",
       "3  female   engineer"
      ]
     },
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   "source": [
    "q_df = pd.DataFrame( [['male', 'teacher'], \n",
    "                      ['male', 'engineer'],\n",
    "                      ['female', None],\n",
    "                      ['female', 'engineer']], columns=['Sex','Profession'] )\n",
    "q_df"
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Profession</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>others</td>\n",
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       "      <th>3</th>\n",
       "      <td>female</td>\n",
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       "      Sex Profession\n",
       "0    male    teacher\n",
       "1    male   engineer\n",
       "2  female     others\n",
       "3  female   engineer"
      ]
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   "source": [
    "#缺失值填入字串'others'\n",
    "\n",
    "q_df.fillna( value='others', inplace=True )\n",
    "q_df"
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Profession</th>\n",
       "      <th>Profession_engineer</th>\n",
       "      <th>Profession_others</th>\n",
       "      <th>Profession_teacher</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>teacher</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>engineer</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>others</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>female</td>\n",
       "      <td>engineer</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "      Sex Profession  Profession_engineer  Profession_others  \\\n",
       "0    male    teacher                    0                  0   \n",
       "1    male   engineer                    1                  0   \n",
       "2  female     others                    0                  1   \n",
       "3  female   engineer                    1                  0   \n",
       "\n",
       "   Profession_teacher  \n",
       "0                   1  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  "
      ]
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   "source": [
    "#更進一步將字串做編碼。此時用什麼方式做編碼比較適合?為什麼?\n",
    "# Ans: One-Hot encoding；因職業之間並沒有順序及大小之分。\n",
    "\n",
    "OneHot = pd.get_dummies( q_df.Profession, prefix='Profession' )\n",
    "q_df = pd.concat( [q_df,OneHot], axis=1 )\n",
    "q_df"
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