3.4.slicing_data.ipynb

{
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  {
   "cell_type": "markdown",
   "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=130>\n",
    "        </td>\n",
    "        <td>\n",
    "            <img src='./images/pandas-logo.png'width=150>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>\n",
    "\n",
    "<h1 style='text-align: center;'>Slicing DataFrame</h1>\n",
    "<h2 style='text-align: center;'>Shouke Wei, Ph.D. Professor</h2>\n",
    "<h3 style='text-align: center;'>Email: shouke.wei@gmail.com</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "- learn how to slice dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</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",
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      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "0  Guangdong  First  2000  1.074125  8.650  0.314513   1.408147  0.108032   \n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "3  Guangdong  First  2003  1.584464  8.963  0.481320   2.346735  0.169563   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "\n",
       "       uinc  \n",
       "0  0.976157  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "3  1.238043  \n",
       "4  1.362765  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the required packages\n",
    "import pandas as pd\n",
    "\n",
    "# Read the data\n",
    "df = pd.read_csv('./data/gdp_china_renamed.csv')\n",
    "\n",
    "# diplay the first 5 rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Selecting columns\n",
    "### (1) One column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 1: Use column name to subset the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.074125\n",
       "1    1.203925\n",
       "2    1.350242\n",
       "3    1.584464\n",
       "4    1.886462\n",
       "Name: gdp, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = df['gdp']\n",
    "gdp.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: Use the column name as an attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.074125\n",
       "1    1.203925\n",
       "2    1.350242\n",
       "3    1.584464\n",
       "4    1.886462\n",
       "Name: gdp, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp = df.gdp\n",
    "gdp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'function' object has no attribute 'head'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12936/1067129407.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mpop\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'function' object has no attribute 'head'"
     ]
    }
   ],
   "source": [
    "pop = df.pop\n",
    "pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reason for this error is that it conflicts with a built-in function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (2) Select muliple columns\n",
    "#### Method 1: using a name list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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",
       "    </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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        prov  year       gdp\n",
       "0  Guangdong  2000  1.074125\n",
       "1  Guangdong  2001  1.203925\n",
       "2  Guangdong  2002  1.350242\n",
       "3  Guangdong  2003  1.584464\n",
       "4  Guangdong  2004  1.886462"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df[['prov','year','gdp']]\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Method 2: using drop() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</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",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr       gdp    pop      finv      trade    fexpen      uinc\n",
       "0  Guangdong  First  1.074125  8.650  0.314513   1.408147  0.108032  0.976157\n",
       "1  Guangdong  First  1.203925  8.733  0.348443  14.609701  0.132133  1.041519\n",
       "2  Guangdong  First  1.350242  8.842  0.385078   1.830169  0.152108  1.113720\n",
       "3  Guangdong  First  1.584464  8.963  0.481320   2.346735  0.169563  1.238043\n",
       "4  Guangdong  First  1.886462    NaN  0.587002   2.955899  0.185295  1.362765"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.drop('year',axis=1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</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>1.074125</td>\n",
       "      <td>8.650</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>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>1.350242</td>\n",
       "      <td>8.842</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>1.584464</td>\n",
       "      <td>8.963</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>1.886462</td>\n",
       "      <td>NaN</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",
       "</div>"
      ],
      "text/plain": [
       "        prov       gdp    pop      finv      trade    fexpen      uinc\n",
       "0  Guangdong  1.074125  8.650  0.314513   1.408147  0.108032  0.976157\n",
       "1  Guangdong  1.203925  8.733  0.348443  14.609701  0.132133  1.041519\n",
       "2  Guangdong  1.350242  8.842  0.385078   1.830169  0.152108  1.113720\n",
       "3  Guangdong  1.584464  8.963  0.481320   2.346735  0.169563  1.238043\n",
       "4  Guangdong  1.886462    NaN  0.587002   2.955899  0.185295  1.362765"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.drop(['year','gdpr'],axis=1)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Select rows\n",
    "#### (1) select first N rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "      <td>9.194</td>\n",
       "      <td>0.697793</td>\n",
       "      <td>3.505761</td>\n",
       "      <td>0.228907</td>\n",
       "      <td>1.476994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "0  Guangdong  First  2000  1.074125  8.650  0.314513   1.408147  0.108032   \n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "3  Guangdong  First  2003  1.584464  8.963  0.481320   2.346735  0.169563   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "5  Guangdong  First  2005  2.255737  9.194  0.697793   3.505761  0.228907   \n",
       "\n",
       "       uinc  \n",
       "0  0.976157  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "3  1.238043  \n",
       "4  1.362765  \n",
       "5  1.476994  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "#### (2) select last N rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>prov</th>\n",
       "      <th>gdpr</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>89</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2013</td>\n",
       "      <td>3.219130</td>\n",
       "      <td>9.413</td>\n",
       "      <td>2.608746</td>\n",
       "      <td>0.371325</td>\n",
       "      <td>0.558231</td>\n",
       "      <td>2.174067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360</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",
       "</div>"
      ],
      "text/plain": [
       "     prov   gdpr  year       gdp    pop      finv     trade    fexpen  \\\n",
       "89  Henan  Fifth  2013  3.219130  9.413  2.608746  0.371325  0.558231   \n",
       "90  Henan  Fifth  2014  3.493824  9.436  3.078217  0.399111  0.602869   \n",
       "91  Henan  Fifth  2015  3.700216  9.480  3.566035  0.459535  0.679935   \n",
       "92  Henan  Fifth  2016  4.047179  9.532  4.041509  0.471385  0.745374   \n",
       "93  Henan  Fifth  2017  4.455283  9.392  4.449690  0.000523  0.821552   \n",
       "94  Henan  Fifth  2018  4.805586  9.360  4.450960  0.551170  0.921773   \n",
       "\n",
       "        uinc  \n",
       "89  2.174067  \n",
       "90  2.367206  \n",
       "91  2.557561  \n",
       "92  2.723292  \n",
       "93  2.955790  \n",
       "94  3.187420  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (3) randomly select N rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        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>prov</th>\n",
       "      <th>gdpr</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>21</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>Second</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.060685</td>\n",
       "      <td>7.406</td>\n",
       "      <td>0.345012</td>\n",
       "      <td>0.581827</td>\n",
       "      <td>0.086025</td>\n",
       "      <td>0.817764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Jiangsu</td>\n",
       "      <td>Second</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.244287</td>\n",
       "      <td>7.458</td>\n",
       "      <td>0.523300</td>\n",
       "      <td>0.940411</td>\n",
       "      <td>0.104768</td>\n",
       "      <td>0.926246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Shandong</td>\n",
       "      <td>Third</td>\n",
       "      <td>2013</td>\n",
       "      <td>5.523032</td>\n",
       "      <td>9.733</td>\n",
       "      <td>3.678907</td>\n",
       "      <td>1.650683</td>\n",
       "      <td>0.668880</td>\n",
       "      <td>2.688239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Fourth</td>\n",
       "      <td>2008</td>\n",
       "      <td>2.146269</td>\n",
       "      <td>5.212</td>\n",
       "      <td>0.932300</td>\n",
       "      <td>1.466345</td>\n",
       "      <td>0.220858</td>\n",
       "      <td>2.272666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2007</td>\n",
       "      <td>3.177701</td>\n",
       "      <td>9.660</td>\n",
       "      <td>0.929426</td>\n",
       "      <td>4.822350</td>\n",
       "      <td>0.315957</td>\n",
       "      <td>1.769930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Zhejiang</td>\n",
       "      <td>Fourth</td>\n",
       "      <td>2011</td>\n",
       "      <td>3.231885</td>\n",
       "      <td>5.463</td>\n",
       "      <td>1.418528</td>\n",
       "      <td>1.998209</td>\n",
       "      <td>0.384259</td>\n",
       "      <td>3.097068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2006</td>\n",
       "      <td>1.236279</td>\n",
       "      <td>9.392</td>\n",
       "      <td>0.590471</td>\n",
       "      <td>0.078080</td>\n",
       "      <td>0.144009</td>\n",
       "      <td>0.981026</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         prov    gdpr  year       gdp    pop      finv     trade    fexpen  \\\n",
       "21    Jiangsu  Second  2002  1.060685  7.406  0.345012  0.581827  0.086025   \n",
       "22    Jiangsu  Second  2003  1.244287  7.458  0.523300  0.940411  0.104768   \n",
       "51   Shandong   Third  2013  5.523032  9.733  3.678907  1.650683  0.668880   \n",
       "65   Zhejiang  Fourth  2008  2.146269  5.212  0.932300  1.466345  0.220858   \n",
       "7   Guangdong   First  2007  3.177701  9.660  0.929426  4.822350  0.315957   \n",
       "68   Zhejiang  Fourth  2011  3.231885  5.463  1.418528  1.998209  0.384259   \n",
       "82      Henan   Fifth  2006  1.236279  9.392  0.590471  0.078080  0.144009   \n",
       "\n",
       "        uinc  \n",
       "21  0.817764  \n",
       "22  0.926246  \n",
       "51  2.688239  \n",
       "65  2.272666  \n",
       "7   1.769930  \n",
       "68  3.097068  \n",
       "82  0.981026  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (4) using row index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</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",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "0  Guangdong  First  2000  1.074125  8.650  0.314513   1.408147  0.108032   \n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "3  Guangdong  First  2003  1.584464  8.963  0.481320   2.346735  0.169563   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "\n",
       "       uinc  \n",
       "0  0.976157  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "3  1.238043  \n",
       "4  1.362765  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:5]\n",
    "# equal to df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "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>prov</th>\n",
       "      <th>gdpr</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>90</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360</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",
       "</div>"
      ],
      "text/plain": [
       "     prov   gdpr  year       gdp    pop      finv     trade    fexpen  \\\n",
       "90  Henan  Fifth  2014  3.493824  9.436  3.078217  0.399111  0.602869   \n",
       "91  Henan  Fifth  2015  3.700216  9.480  3.566035  0.459535  0.679935   \n",
       "92  Henan  Fifth  2016  4.047179  9.532  4.041509  0.471385  0.745374   \n",
       "93  Henan  Fifth  2017  4.455283  9.392  4.449690  0.000523  0.821552   \n",
       "94  Henan  Fifth  2018  4.805586  9.360  4.450960  0.551170  0.921773   \n",
       "\n",
       "        uinc  \n",
       "90  2.367206  \n",
       "91  2.557561  \n",
       "92  2.723292  \n",
       "93  2.955790  \n",
       "94  3.187420  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[-5:]\n",
    "#equal to df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (5) using drop() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "      <td>9.194</td>\n",
       "      <td>0.697793</td>\n",
       "      <td>3.505761</td>\n",
       "      <td>0.228907</td>\n",
       "      <td>1.476994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2006</td>\n",
       "      <td>2.658776</td>\n",
       "      <td>9.442</td>\n",
       "      <td>0.797337</td>\n",
       "      <td>4.202726</td>\n",
       "      <td>0.255334</td>\n",
       "      <td>1.601558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "5  Guangdong  First  2005  2.255737  9.194  0.697793   3.505761  0.228907   \n",
       "6  Guangdong  First  2006  2.658776  9.442  0.797337   4.202726  0.255334   \n",
       "\n",
       "       uinc  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "4  1.362765  \n",
       "5  1.476994  \n",
       "6  1.601558  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df.drop([0,3],axis=0)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (6) using `.loc` indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "      <td>9.194</td>\n",
       "      <td>0.697793</td>\n",
       "      <td>3.505761</td>\n",
       "      <td>0.228907</td>\n",
       "      <td>1.476994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2006</td>\n",
       "      <td>2.658776</td>\n",
       "      <td>9.442</td>\n",
       "      <td>0.797337</td>\n",
       "      <td>4.202726</td>\n",
       "      <td>0.255334</td>\n",
       "      <td>1.601558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "0  Guangdong  First  2000  1.074125  8.650  0.314513   1.408147  0.108032   \n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "3  Guangdong  First  2003  1.584464  8.963  0.481320   2.346735  0.169563   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "5  Guangdong  First  2005  2.255737  9.194  0.697793   3.505761  0.228907   \n",
       "6  Guangdong  First  2006  2.658776  9.442  0.797337   4.202726  0.255334   \n",
       "\n",
       "       uinc  \n",
       "0  0.976157  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "3  1.238043  \n",
       "4  1.362765  \n",
       "5  1.476994  \n",
       "6  1.601558  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[0:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (7) using `.iloc` indexer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "      <td>9.194</td>\n",
       "      <td>0.697793</td>\n",
       "      <td>3.505761</td>\n",
       "      <td>0.228907</td>\n",
       "      <td>1.476994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp    pop      finv      trade    fexpen  \\\n",
       "0  Guangdong  First  2000  1.074125  8.650  0.314513   1.408147  0.108032   \n",
       "1  Guangdong  First  2001  1.203925  8.733  0.348443  14.609701  0.132133   \n",
       "2  Guangdong  First  2002  1.350242  8.842  0.385078   1.830169  0.152108   \n",
       "3  Guangdong  First  2003  1.584464  8.963  0.481320   2.346735  0.169563   \n",
       "4  Guangdong  First  2004  1.886462    NaN  0.587002   2.955899  0.185295   \n",
       "5  Guangdong  First  2005  2.255737  9.194  0.697793   3.505761  0.228907   \n",
       "\n",
       "       uinc  \n",
       "0  0.976157  \n",
       "1  1.041519  \n",
       "2  1.113720  \n",
       "3  1.238043  \n",
       "4  1.362765  \n",
       "5  1.476994  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Slicing rows and columns\n",
    "#### (1) using `.loc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2006</td>\n",
       "      <td>2.658776</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov  year       gdp\n",
       "0  Guangdong  2000  1.074125\n",
       "1  Guangdong  2001  1.203925\n",
       "2  Guangdong  2002  1.350242\n",
       "3  Guangdong  2003  1.584464\n",
       "4  Guangdong  2004  1.886462\n",
       "5  Guangdong  2005  2.255737\n",
       "6  Guangdong  2006  2.658776"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using column names\n",
    "df.loc[0:6,['prov','year','gdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2015</td>\n",
       "      <td>3.700216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.047179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2017</td>\n",
       "      <td>4.455283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Henan</td>\n",
       "      <td>2018</td>\n",
       "      <td>4.805586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>95 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         prov  year       gdp\n",
       "0   Guangdong  2000  1.074125\n",
       "1   Guangdong  2001  1.203925\n",
       "2   Guangdong  2002  1.350242\n",
       "3   Guangdong  2003  1.584464\n",
       "4   Guangdong  2004  1.886462\n",
       "..        ...   ...       ...\n",
       "90      Henan  2014  3.493824\n",
       "91      Henan  2015  3.700216\n",
       "92      Henan  2016  4.047179\n",
       "93      Henan  2017  4.455283\n",
       "94      Henan  2018  4.805586\n",
       "\n",
       "[95 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all rows\n",
    "df.loc[:,['prov','year','gdp']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prov</th>\n",
       "      <th>gdpr</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>First</td>\n",
       "      <td>1.074125</td>\n",
       "      <td>8.650</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>First</td>\n",
       "      <td>1.203925</td>\n",
       "      <td>8.733</td>\n",
       "      <td>0.348443</td>\n",
       "      <td>14.609701</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>First</td>\n",
       "      <td>1.350242</td>\n",
       "      <td>8.842</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>First</td>\n",
       "      <td>1.584464</td>\n",
       "      <td>8.963</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>First</td>\n",
       "      <td>1.886462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.587002</td>\n",
       "      <td>2.955899</td>\n",
       "      <td>0.185295</td>\n",
       "      <td>1.362765</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>3.493824</td>\n",
       "      <td>9.436</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>3.700216</td>\n",
       "      <td>9.480</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>4.047179</td>\n",
       "      <td>9.532</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>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>4.455283</td>\n",
       "      <td>9.392</td>\n",
       "      <td>4.449690</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.821552</td>\n",
       "      <td>2.955790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>Henan</td>\n",
       "      <td>Fifth</td>\n",
       "      <td>4.805586</td>\n",
       "      <td>9.360</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   gdpr       gdp    pop      finv      trade    fexpen      uinc\n",
       "0   Guangdong  First  1.074125  8.650  0.314513   1.408147  0.108032  0.976157\n",
       "1   Guangdong  First  1.203925  8.733  0.348443  14.609701  0.132133  1.041519\n",
       "2   Guangdong  First  1.350242  8.842  0.385078   1.830169  0.152108  1.113720\n",
       "3   Guangdong  First  1.584464  8.963  0.481320   2.346735  0.169563  1.238043\n",
       "4   Guangdong  First  1.886462    NaN  0.587002   2.955899  0.185295  1.362765\n",
       "..        ...    ...       ...    ...       ...        ...       ...       ...\n",
       "90      Henan  Fifth  3.493824  9.436  3.078217   0.399111  0.602869  2.367206\n",
       "91      Henan  Fifth  3.700216  9.480  3.566035   0.459535  0.679935  2.557561\n",
       "92      Henan  Fifth  4.047179  9.532  4.041509   0.471385  0.745374  2.723292\n",
       "93      Henan  Fifth  4.455283  9.392  4.449690   0.000523  0.821552  2.955790\n",
       "94      Henan  Fifth  4.805586  9.360  4.450960   0.551170  0.921773  3.187420\n",
       "\n",
       "[95 rows x 8 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exclude one column\n",
    "df.loc[:,df.columns!='year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### (2) using `.iloc`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>prov</th>\n",
       "      <th>gdpr</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.074125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2001</td>\n",
       "      <td>1.203925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2002</td>\n",
       "      <td>1.350242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2003</td>\n",
       "      <td>1.584464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2004</td>\n",
       "      <td>1.886462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>First</td>\n",
       "      <td>2005</td>\n",
       "      <td>2.255737</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov   gdpr  year       gdp\n",
       "0  Guangdong  First  2000  1.074125\n",
       "1  Guangdong  First  2001  1.203925\n",
       "2  Guangdong  First  2002  1.350242\n",
       "3  Guangdong  First  2003  1.584464\n",
       "4  Guangdong  First  2004  1.886462\n",
       "5  Guangdong  First  2005  2.255737"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# continous columns\n",
    "df.iloc[0:6,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>prov</th>\n",
       "      <th>year</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2000</td>\n",
       "      <td>8.650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2001</td>\n",
       "      <td>8.733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2002</td>\n",
       "      <td>8.842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2003</td>\n",
       "      <td>8.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Guangdong</td>\n",
       "      <td>2005</td>\n",
       "      <td>9.194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        prov  year    pop\n",
       "0  Guangdong  2000  8.650\n",
       "1  Guangdong  2001  8.733\n",
       "2  Guangdong  2002  8.842\n",
       "3  Guangdong  2003  8.963\n",
       "4  Guangdong  2004    NaN\n",
       "5  Guangdong  2005  9.194"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# discontinuous columns\n",
    "df.iloc[0:6,[0,2,4]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. `loc` vs `iloc`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- `loc`: label-based, which specify rows and columns based on their row and column labels.\n",
    "\n",
    "- `iloc`: integer position-based, which specify rows and columns by their integer position values (0-based integer position)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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