4.6.encode_categorical.ipynb

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   "metadata": {},
   "source": [
    "<img src=\"./images/shouke_logo.png\"\n",
    "     style=\"float: right\"\n",
    "     width=100\n",
    "     style=\"padding-bottom:100px;\"/>\n",
    "<br>\n",
    "<br>\n",
    "\n",
    "<table style=\"float:center;\">\n",
    "    <tr>\n",
    "        <td>\n",
    "            <img src='./images/python-logo.png'width=120>\n",
    "        </td>\n",
    "        <td>\n",
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    "            <img src='./images/scikit_learn_logo.png'width=150>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<h1 style='text-align: center;'>Encoding Categorical Data</h1>\n",
    "<h3 style='text-align: center;'>Shouke Wei, Ph.D. Professor</h3>\n",
    "<h4 style='text-align: center;'>Email: shouke.wei@gmail.com</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4085b89f",
   "metadata": {},
   "source": [
    "## Objective\n",
    "- learn how to encode categorical data, i.e. transfer categorical data into numerical values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0bf8adca",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</th>\n",
       "      <th>pop</th>\n",
       "      <th>finv</th>\n",
       "      <th>trade</th>\n",
       "      <th>fexpen</th>\n",
       "      <th>uinc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650000</td>\n",
       "      <td>0.314513</td>\n",
       "      <td>1.408147</td>\n",
       "      <td>0.108032</td>\n",
       "      <td>0.976157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733000</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>1.501391</td>\n",
       "      <td>0.132133</td>\n",
       "      <td>1.041519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842000</td>\n",
       "      <td>0.385078</td>\n",
       "      <td>1.830169</td>\n",
       "      <td>0.152108</td>\n",
       "      <td>1.113720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963000</td>\n",
       "      <td>0.481320</td>\n",
       "      <td>2.346735</td>\n",
       "      <td>0.169563</td>\n",
       "      <td>1.238043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>9.052298</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        prov  year       gdp       pop      finv     trade    fexpen      uinc\n",
       "0  Guangdong  2000  1.074125  8.650000  0.314513  1.408147  0.108032  0.976157\n",
       "1  Guangdong  2001  1.203925  8.733000  0.348443  1.501391  0.132133  1.041519\n",
       "2  Guangdong  2002  1.350242  8.842000  0.385078  1.830169  0.152108  1.113720\n",
       "3  Guangdong  2003  1.584464  8.963000  0.481320  2.346735  0.169563  1.238043\n",
       "4  Guangdong  2004  1.886462  9.052298  0.587002  2.955899  0.185295  1.362765"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import required packages\n",
    "import pandas as pd\n",
    "\n",
    "# read data\n",
    "df = pd.read_csv('./data/gdp_china_variables_seleted.csv')\n",
    "# display the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc27f86",
   "metadata": {},
   "source": [
    "## 1. Factorize method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "180b06ea",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</th>\n",
       "      <th>pop</th>\n",
       "      <th>finv</th>\n",
       "      <th>trade</th>\n",
       "      <th>fexpen</th>\n",
       "      <th>uinc</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650000</td>\n",
       "      <td>0.314513</td>\n",
       "      <td>1.408147</td>\n",
       "      <td>0.108032</td>\n",
       "      <td>0.976157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733000</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>1.501391</td>\n",
       "      <td>0.132133</td>\n",
       "      <td>1.041519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842000</td>\n",
       "      <td>0.385078</td>\n",
       "      <td>1.830169</td>\n",
       "      <td>0.152108</td>\n",
       "      <td>1.113720</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963000</td>\n",
       "      <td>0.481320</td>\n",
       "      <td>2.346735</td>\n",
       "      <td>0.169563</td>\n",
       "      <td>1.238043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>9.052298</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>4</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436000</td>\n",
       "      <td>3.078217</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>0.602869</td>\n",
       "      <td>2.367206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>4</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480000</td>\n",
       "      <td>3.566035</td>\n",
       "      <td>0.459535</td>\n",
       "      <td>0.679935</td>\n",
       "      <td>2.557561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>4</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532000</td>\n",
       "      <td>4.041509</td>\n",
       "      <td>0.471385</td>\n",
       "      <td>0.745374</td>\n",
       "      <td>2.723292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392000</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.474870</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>4</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>4.450960</td>\n",
       "      <td>0.551170</td>\n",
       "      <td>0.921773</td>\n",
       "      <td>3.187420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    prov  year       gdp       pop      finv     trade    fexpen      uinc\n",
       "0      0  2000  1.074125  8.650000  0.314513  1.408147  0.108032  0.976157\n",
       "1      0  2001  1.203925  8.733000  0.348443  1.501391  0.132133  1.041519\n",
       "2      0  2002  1.350242  8.842000  0.385078  1.830169  0.152108  1.113720\n",
       "3      0  2003  1.584464  8.963000  0.481320  2.346735  0.169563  1.238043\n",
       "4      0  2004  1.886462  9.052298  0.587002  2.955899  0.185295  1.362765\n",
       "..   ...   ...       ...       ...       ...       ...       ...       ...\n",
       "90     4  2014  3.493824  9.436000  3.078217  0.399111  0.602869  2.367206\n",
       "91     4  2015  3.700216  9.480000  3.566035  0.459535  0.679935  2.557561\n",
       "92     4  2016  4.047179  9.532000  4.041509  0.471385  0.745374  2.723292\n",
       "93     4  2017  4.455283  9.392000  4.449690  0.474870  0.821552  2.955790\n",
       "94     4  2018  4.805586  9.360000  4.450960  0.551170  0.921773  3.187420\n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_copy = df.copy()\n",
    "df_copy['prov'] = pd.factorize(df_copy['prov'])[0]\n",
    "df_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7311bd5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n",
       "       3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,\n",
       "       4, 4, 4, 4, 4, 4, 4], dtype=int64)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.factorize(df_copy['prov'])[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "280a2047",
   "metadata": {},
   "source": [
    "## 2. get_dummies method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f312c575",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov_Henan</th>\n",
       "      <th>prov_Jiangsu</th>\n",
       "      <th>prov_Shandong</th>\n",
       "      <th>prov_Zhejiang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <th>90</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    prov_Henan  prov_Jiangsu  prov_Shandong  prov_Zhejiang\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",
       "90           1             0              0              0\n",
       "91           1             0              0              0\n",
       "92           1             0              0              0\n",
       "93           1             0              0              0\n",
       "94           1             0              0              0\n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dums = pd.get_dummies(df['prov'],prefix='prov',drop_first=True)\n",
    "dums                      "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3a82b52",
   "metadata": {},
   "source": [
    "A categorical variable of K categories, or levels, usually enters a regression as a sequence of K-1 dummy variables. Otherwise, it might cause **Dummy Variable Trap**, where two or more dummy variables created by one-hot encoding are highly correlated (i.e. multi-collinear)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0117abe4",
   "metadata": {
    "scrolled": false
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</th>\n",
       "      <th>pop</th>\n",
       "      <th>finv</th>\n",
       "      <th>trade</th>\n",
       "      <th>fexpen</th>\n",
       "      <th>uinc</th>\n",
       "      <th>prov_Henan</th>\n",
       "      <th>prov_Jiangsu</th>\n",
       "      <th>prov_Shandong</th>\n",
       "      <th>prov_Zhejiang</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650000</td>\n",
       "      <td>0.314513</td>\n",
       "      <td>1.408147</td>\n",
       "      <td>0.108032</td>\n",
       "      <td>0.976157</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>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733000</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>1.501391</td>\n",
       "      <td>0.132133</td>\n",
       "      <td>1.041519</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>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842000</td>\n",
       "      <td>0.385078</td>\n",
       "      <td>1.830169</td>\n",
       "      <td>0.152108</td>\n",
       "      <td>1.113720</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963000</td>\n",
       "      <td>0.481320</td>\n",
       "      <td>2.346735</td>\n",
       "      <td>0.169563</td>\n",
       "      <td>1.238043</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>9.052298</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436000</td>\n",
       "      <td>3.078217</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>0.602869</td>\n",
       "      <td>2.367206</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480000</td>\n",
       "      <td>3.566035</td>\n",
       "      <td>0.459535</td>\n",
       "      <td>0.679935</td>\n",
       "      <td>2.557561</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532000</td>\n",
       "      <td>4.041509</td>\n",
       "      <td>0.471385</td>\n",
       "      <td>0.745374</td>\n",
       "      <td>2.723292</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392000</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.474870</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>4.450960</td>\n",
       "      <td>0.551170</td>\n",
       "      <td>0.921773</td>\n",
       "      <td>3.187420</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         prov  year       gdp       pop      finv     trade    fexpen  \\\n",
       "0   Guangdong  2000  1.074125  8.650000  0.314513  1.408147  0.108032   \n",
       "1   Guangdong  2001  1.203925  8.733000  0.348443  1.501391  0.132133   \n",
       "2   Guangdong  2002  1.350242  8.842000  0.385078  1.830169  0.152108   \n",
       "3   Guangdong  2003  1.584464  8.963000  0.481320  2.346735  0.169563   \n",
       "4   Guangdong  2004  1.886462  9.052298  0.587002  2.955899  0.185295   \n",
       "..        ...   ...       ...       ...       ...       ...       ...   \n",
       "90      Henan  2014  3.493824  9.436000  3.078217  0.399111  0.602869   \n",
       "91      Henan  2015  3.700216  9.480000  3.566035  0.459535  0.679935   \n",
       "92      Henan  2016  4.047179  9.532000  4.041509  0.471385  0.745374   \n",
       "93      Henan  2017  4.455283  9.392000  4.449690  0.474870  0.821552   \n",
       "94      Henan  2018  4.805586  9.360000  4.450960  0.551170  0.921773   \n",
       "\n",
       "        uinc  prov_Henan  prov_Jiangsu  prov_Shandong  prov_Zhejiang  \n",
       "0   0.976157           0             0              0              0  \n",
       "1   1.041519           0             0              0              0  \n",
       "2   1.113720           0             0              0              0  \n",
       "3   1.238043           0             0              0              0  \n",
       "4   1.362765           0             0              0              0  \n",
       "..       ...         ...           ...            ...            ...  \n",
       "90  2.367206           1             0              0              0  \n",
       "91  2.557561           1             0              0              0  \n",
       "92  2.723292           1             0              0              0  \n",
       "93  2.955790           1             0              0              0  \n",
       "94  3.187420           1             0              0              0  \n",
       "\n",
       "[95 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add dummies to Pandas DataFrame\n",
    "df_coded = df.join(dums)\n",
    "df_coded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09771375",
   "metadata": {},
   "source": [
    "## 3. OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c57f9943",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "       [1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0.],\n",
       "       [1., 0., 0., 0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "enc = OneHotEncoder(sparse=False,drop='first')\n",
    "# sparse parameter (=True or False): will return a sparse matrix or dense array\n",
    "trans = enc.fit_transform(df[['prov']])\n",
    "trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5b24899e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('prov_hn', 'prov_js', 'prov_sd', 'prov_zj')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# feature_names = enc.get_feature_names_out(['prov'])\n",
    "feature_names = ['prov_hn', 'prov_js', 'prov_sd', 'prov_zj']\n",
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "eeec1cab",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov_hn</th>\n",
       "      <th>prov_js</th>\n",
       "      <th>prov_sd</th>\n",
       "      <th>prov_zj</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>93</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    prov_hn  prov_js  prov_sd  prov_zj\n",
       "0       0.0      0.0      0.0      0.0\n",
       "1       0.0      0.0      0.0      0.0\n",
       "2       0.0      0.0      0.0      0.0\n",
       "3       0.0      0.0      0.0      0.0\n",
       "4       0.0      0.0      0.0      0.0\n",
       "..      ...      ...      ...      ...\n",
       "90      1.0      0.0      0.0      0.0\n",
       "91      1.0      0.0      0.0      0.0\n",
       "92      1.0      0.0      0.0      0.0\n",
       "93      1.0      0.0      0.0      0.0\n",
       "94      1.0      0.0      0.0      0.0\n",
       "\n",
       "[95 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_coded = pd.DataFrame(trans, columns=feature_names)\n",
    "feature_coded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05359a79",
   "metadata": {},
   "outputs": [
    {
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       "      <th>pop</th>\n",
       "      <th>finv</th>\n",
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       "      <td>1.501391</td>\n",
       "      <td>0.132133</td>\n",
       "      <td>1.041519</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>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842000</td>\n",
       "      <td>0.385078</td>\n",
       "      <td>1.830169</td>\n",
       "      <td>0.152108</td>\n",
       "      <td>1.113720</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>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963000</td>\n",
       "      <td>0.481320</td>\n",
       "      <td>2.346735</td>\n",
       "      <td>0.169563</td>\n",
       "      <td>1.238043</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>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>9.052298</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2014</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436000</td>\n",
       "      <td>3.078217</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>0.602869</td>\n",
       "      <td>2.367206</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480000</td>\n",
       "      <td>3.566035</td>\n",
       "      <td>0.459535</td>\n",
       "      <td>0.679935</td>\n",
       "      <td>2.557561</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532000</td>\n",
       "      <td>4.041509</td>\n",
       "      <td>0.471385</td>\n",
       "      <td>0.745374</td>\n",
       "      <td>2.723292</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392000</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.474870</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360000</td>\n",
       "      <td>4.450960</td>\n",
       "      <td>0.551170</td>\n",
       "      <td>0.921773</td>\n",
       "      <td>3.187420</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year       gdp       pop      finv     trade    fexpen      uinc  prov_hn  \\\n",
       "0   2000  1.074125  8.650000  0.314513  1.408147  0.108032  0.976157      0.0   \n",
       "1   2001  1.203925  8.733000  0.348443  1.501391  0.132133  1.041519      0.0   \n",
       "2   2002  1.350242  8.842000  0.385078  1.830169  0.152108  1.113720      0.0   \n",
       "3   2003  1.584464  8.963000  0.481320  2.346735  0.169563  1.238043      0.0   \n",
       "4   2004  1.886462  9.052298  0.587002  2.955899  0.185295  1.362765      0.0   \n",
       "..   ...       ...       ...       ...       ...       ...       ...      ...   \n",
       "90  2014  3.493824  9.436000  3.078217  0.399111  0.602869  2.367206      1.0   \n",
       "91  2015  3.700216  9.480000  3.566035  0.459535  0.679935  2.557561      1.0   \n",
       "92  2016  4.047179  9.532000  4.041509  0.471385  0.745374  2.723292      1.0   \n",
       "93  2017  4.455283  9.392000  4.449690  0.474870  0.821552  2.955790      1.0   \n",
       "94  2018  4.805586  9.360000  4.450960  0.551170  0.921773  3.187420      1.0   \n",
       "\n",
       "    prov_js  prov_sd  prov_zj  \n",
       "0       0.0      0.0      0.0  \n",
       "1       0.0      0.0      0.0  \n",
       "2       0.0      0.0      0.0  \n",
       "3       0.0      0.0      0.0  \n",
       "4       0.0      0.0      0.0  \n",
       "..      ...      ...      ...  \n",
       "90      0.0      0.0      0.0  \n",
       "91      0.0      0.0      0.0  \n",
       "92      0.0      0.0      0.0  \n",
       "93      0.0      0.0      0.0  \n",
       "94      0.0      0.0      0.0  \n",
       "\n",
       "[95 rows x 11 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coded = pd.concat([df,feature_coded],axis=1)\n",
    "df_coded.drop(['prov'],axis=1, inplace=True)\n",
    "df_coded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7efa3e25",
   "metadata": {},
   "source": [
    "## 4. Save the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c45db3a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_coded.to_csv('./data/gdp_china_encoded.csv', index=False)"
   ]
  }
 ],
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