encoding-categorical-values-in-python.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>?</th>\n",
       "      <th>alfa-romero</th>\n",
       "      <th>gas</th>\n",
       "      <th>std</th>\n",
       "      <th>two</th>\n",
       "      <th>convertible</th>\n",
       "      <th>rwd</th>\n",
       "      <th>front</th>\n",
       "      <th>88.60</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>mpfi</th>\n",
       "      <th>3.47</th>\n",
       "      <th>2.68</th>\n",
       "      <th>9.00</th>\n",
       "      <th>111</th>\n",
       "      <th>5000</th>\n",
       "      <th>21</th>\n",
       "      <th>27</th>\n",
       "      <th>13495</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
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       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.60  ...    \\\n",
       "0  3    ?  alfa-romero  gas  std   two  convertible  rwd  front   88.6  ...     \n",
       "1  1    ?  alfa-romero  gas  std   two    hatchback  rwd  front   94.5  ...     \n",
       "2  2  164         audi  gas  std  four        sedan  fwd  front   99.8  ...     \n",
       "3  2  164         audi  gas  std  four        sedan  4wd  front   99.4  ...     \n",
       "4  2    ?         audi  gas  std   two        sedan  fwd  front   99.8  ...     \n",
       "\n",
       "   130  mpfi  3.47  2.68  9.00  111  5000  21  27  13495  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "1  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "2  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "3  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n",
       "4  136  mpfi  3.19  3.40   8.5  110  5500  19  25  15250  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# 原始資料中的各欄沒有標題，且有些欄位為問號 '?'\n",
    "df = pd.read_csv('http://mlr.cs.umass.edu/ml/machine-learning-databases/autos/imports-85.data')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized_losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel_type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num_doors</th>\n",
       "      <th>body_style</th>\n",
       "      <th>drive_wheels</th>\n",
       "      <th>engine_location</th>\n",
       "      <th>wheel_base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine_size</th>\n",
       "      <th>fuel_system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression_ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak_rpm</th>\n",
       "      <th>city_mpg</th>\n",
       "      <th>highway_mpg</th>\n",
       "      <th>price</th>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164.0</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized_losses         make fuel_type aspiration num_doors  \\\n",
       "0          3                NaN  alfa-romero       gas        std       two   \n",
       "1          3                NaN  alfa-romero       gas        std       two   \n",
       "2          1                NaN  alfa-romero       gas        std       two   \n",
       "3          2              164.0         audi       gas        std      four   \n",
       "4          2              164.0         audi       gas        std      four   \n",
       "\n",
       "    body_style drive_wheels engine_location  wheel_base   ...     engine_size  \\\n",
       "0  convertible          rwd           front        88.6   ...             130   \n",
       "1  convertible          rwd           front        88.6   ...             130   \n",
       "2    hatchback          rwd           front        94.5   ...             152   \n",
       "3        sedan          fwd           front        99.8   ...             109   \n",
       "4        sedan          4wd           front        99.4   ...             136   \n",
       "\n",
       "   fuel_system  bore  stroke compression_ratio horsepower  peak_rpm city_mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "1         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "2         mpfi  2.68    3.47               9.0      154.0    5000.0       19   \n",
       "3         mpfi  3.19    3.40              10.0      102.0    5500.0       24   \n",
       "4         mpfi  3.19    3.40               8.0      115.0    5500.0       18   \n",
       "\n",
       "   highway_mpg    price  \n",
       "0           27  13495.0  \n",
       "1           27  16500.0  \n",
       "2           26  16500.0  \n",
       "3           30  13950.0  \n",
       "4           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 原始資料並沒有標題，因此自訂 headers\n",
    "headers = ['symboling', 'normalized_losses', 'make', 'fuel_type', 'aspiration',\n",
    "           'num_doors', 'body_style', 'drive_wheels', 'engine_location',\n",
    "           'wheel_base', 'length', 'width', 'height', 'curb_weight',\n",
    "           'engine_type', 'num_cylinders', 'engine_size', 'fuel_system',\n",
    "           'bore', 'stroke', 'compression_ratio', 'horsepower', 'peak_rpm',\n",
    "           'city_mpg', 'highway_mpg', 'price']\n",
    "\n",
    "# 讀檔案的過程中，除了給予自訂的 header，也同時將問號('?')轉換成 Nan\n",
    "df = pd.read_csv('http://mlr.cs.umass.edu/ml/machine-learning-databases/autos/imports-85.data',\n",
    "                  header=None, names=headers, na_values='?')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_doors</th>\n",
       "      <th>body_style</th>\n",
       "      <th>drive_wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "  num_doors   body_style drive_wheels\n",
       "0       two  convertible          rwd\n",
       "1       two  convertible          rwd\n",
       "2       two    hatchback          rwd\n",
       "3      four        sedan          fwd\n",
       "4      four        sedan          4wd"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 只取需要的 columns\n",
    "obj_df = df.select_dtypes(include=['object']).copy()\n",
    "obj_df = obj_df[['num_doors', 'body_style', 'drive_wheels']]\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_doors       object\n",
      "body_style      object\n",
      "drive_wheels    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# 查看資料型態\n",
    "print(obj_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>NaN</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_doors body_style drive_wheels\n",
       "27       NaN      sedan          fwd\n",
       "63       NaN      sedan          fwd"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# .any() 只有要有一個 True \n",
    "# .all() 全部都為 True\n",
    "# axis=1: row direction; axis=0: column direction\n",
    "# 查看是否有欄位是 NaN\n",
    "\n",
    "obj_df[obj_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num_doors, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# value_counts(): 顯示非 NaN 的數量\n",
    "obj_df['num_doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df['num_doors'].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "203"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df['num_doors'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_df = obj_df.fillna(value={'num_doors': 'four'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    116\n",
       "two      89\n",
       "Name: num_doors, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df['num_doors'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find and Replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>num_doors</th>\n",
       "      <th>body_style</th>\n",
       "      <th>drive_wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_doors   body_style drive_wheels\n",
       "0          2  convertible          rwd\n",
       "1          2  convertible          rwd\n",
       "2          2    hatchback          rwd\n",
       "3          4        sedan          fwd\n",
       "4          4        sedan          4wd"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_replace = {'num_doors': {'four': 4, 'two': 2},\n",
    "              'num_cylinders': {\n",
    "                                'four': 4, 'six': 6, 'five': 5, 'eight': 8,\n",
    "                                'two': 2, 'twelve': 12, 'three':3\n",
    "                               }\n",
    "             }\n",
    "obj_df.replace(to_replace=to_replace, inplace=True)\n",
    "obj_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "num_doors        int64\n",
       "body_style      object\n",
       "drive_wheels    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obj_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Label Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pandas Categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ori data type(a1): object\n",
      "new data type(a2): category\n"
     ]
    }
   ],
   "source": [
    "a1 = obj_df['body_style'] # a1: pandas.Series\n",
    "print('ori data type(a1):', a1.dtypes)\n",
    "a2 = obj_df['body_style'].astype('category') # a2: pandas.Series\n",
    "print('new data type(a2):', a2.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pd_cat: [0, 0, 2, 3, 3]\n"
     ]
    }
   ],
   "source": [
    "# a1 為 object; a1.cat.codes 不可行\n",
    "# a2 為 category; \n",
    "pd_cat = a2.cat.codes # convert to int from string\n",
    "print('pd_cat:', pd_cat.tolist()[:5]) # transform the to list from Seires and shows the first 5 values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['convertible', 'hardtop', 'hatchback', 'sedan', 'wagon'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# pd_cat 所相對應的類別\n",
    "print(a2.cat.categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'convertible', 1: 'hardtop', 2: 'hatchback', 3: 'sedan', 4: 'wagon'}\n"
     ]
    }
   ],
   "source": [
    "# 將 pd_cat 所相對應的類別轉換成 dictionary，方便閱讀\n",
    "a2_classes = dict(enumerate(a2.cat.categories))\n",
    "print(a2_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn.preprocessing.LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sk_cat: [0, 0, 2, 3, 3]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "le = LabelEncoder()\n",
    "sk_cat = le.fit_transform(a1)\n",
    "print('sk_cat:', sk_cat.tolist()[0:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas : [0, 0, 2, 3, 3, 3, 3, 4, 3, 2]\n",
      "sklearn: [0, 0, 2, 3, 3, 3, 3, 4, 3, 2]\n"
     ]
    }
   ],
   "source": [
    "print('pandas :', pd_cat.tolist()[0:10])\n",
    "print('sklearn:', sk_cat.tolist()[0:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## One Hot Encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### pandas get_dummies()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>body_style</th>\n",
       "      <th>drive_wheels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    body_style drive_wheels\n",
       "0  convertible          rwd\n",
       "1  convertible          rwd\n",
       "2    hatchback          rwd\n",
       "3        sedan          fwd\n",
       "4        sedan          4wd"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = obj_df[['body_style', 'drive_wheels']]\n",
    "b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "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>body_style</th>\n",
       "      <th>drive_wheels_4wd</th>\n",
       "      <th>drive_wheels_fwd</th>\n",
       "      <th>drive_wheels_rwd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>convertible</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>convertible</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hatchback</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>sedan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>sedan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    body_style  drive_wheels_4wd  drive_wheels_fwd  drive_wheels_rwd\n",
       "0  convertible                 0                 0                 1\n",
       "1  convertible                 0                 0                 1\n",
       "2    hatchback                 0                 0                 1\n",
       "3        sedan                 0                 1                 0\n",
       "4        sedan                 1                 0                 0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data=b, columns=['drive_wheels']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The new data frame contains three new columns\n",
    "* drive_wheels_4wd\n",
    "* drive_wheels_rwd\n",
    "* drive_wheels_fwd\n",
    "\n",
    "#### You can label the new columns by `prefix`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>body_convertible</th>\n",
       "      <th>body_hardtop</th>\n",
       "      <th>body_hatchback</th>\n",
       "      <th>body_sedan</th>\n",
       "      <th>body_wagon</th>\n",
       "      <th>drive_4wd</th>\n",
       "      <th>drive_fwd</th>\n",
       "      <th>drive_rwd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   body_convertible  body_hardtop  body_hatchback  body_sedan  body_wagon  \\\n",
       "0                 1             0               0           0           0   \n",
       "1                 1             0               0           0           0   \n",
       "2                 0             0               1           0           0   \n",
       "3                 0             0               0           1           0   \n",
       "4                 0             0               0           1           0   \n",
       "\n",
       "   drive_4wd  drive_fwd  drive_rwd  \n",
       "0          0          0          1  \n",
       "1          0          0          1  \n",
       "2          0          0          1  \n",
       "3          0          1          0  \n",
       "4          1          0          0  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.get_dummies(data=b, columns=['body_style', 'drive_wheels'], prefix=['body', 'drive']).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### sklearn.preprocessing.LabelBinarizer\n",
    "#### 可直接將 string value 轉換成 integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 0, 1, 0, 0],\n",
       "       ..., \n",
       "       [0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 1, 0],\n",
       "       [0, 0, 0, 1, 0]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "lb_style = LabelBinarizer()\n",
    "lb_results = lb_style.fit_transform(b['body_style'])\n",
    "lb_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['convertible', 'hardtop', 'hatchback', 'sedan', 'wagon'],\n",
       "      dtype='<U11')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lb_style.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>convertible</th>\n",
       "      <th>hardtop</th>\n",
       "      <th>hatchback</th>\n",
       "      <th>sedan</th>\n",
       "      <th>wagon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   convertible  hardtop  hatchback  sedan  wagon\n",
       "0            1        0          0      0      0\n",
       "1            1        0          0      0      0\n",
       "2            0        0          1      0      0\n",
       "3            0        0          0      1      0\n",
       "4            0        0          0      1      0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(lb_results, columns=lb_style.classes_).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Reference\n",
    "[Guide to Encoding Categorical Values in Python](http://pbpython.com/categorical-encoding.html)"
   ]
  }
 ],
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