Introduction to Data Science.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# วิทยาศาสตร์ข้อมูลเบื้องต้น\n",
    "\n",
    " วัชรพัฐ เมตตานันท\n",
    " ***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## บทนำ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เทคโนโลยีในปัจจุบันทำให้เราสามารถเก็บข้อมูลต่าง ๆ ได้เป็นจำนวนมาก คนจึงเริ่มมีความต้องการที่จะนำข้อมูลเหล่านี้มาใช้ให้เกิดประโยชน์สูงสุด ด้วยสาเหตุนี้จึงทำให้เกิดศาสตร์แขนงใหม่ที่เรียกว่า _วิทยาศาสตร์ข้อมูล_ (data science) เกิดขึ้น\n",
    "\n",
    "วิทยาศาสตร์ข้อมูล เป็นศาสตร์ที่ศึกษาเรื่องการนำข้อมูลมาใช้ประโยชน์ ซึ่งครอบคลุมประเด็นหลัก ๆ ได้แก่\n",
    "\n",
    "1. การจัดการข้อมูล (data management)\n",
    "1. การวิเคราะห์ข้อมูล (data analysis)\n",
    "1. การนำเสนอข้อมูลด้วยภาพ (data visualization)\n",
    "1. การเรียนรู้ของเครื่อง (machine learning)\n",
    "\n",
    "จะเห็นได้ว่าคนที่เหมาะสมสำหรับงานด้านวิทยาศาสตร์ข้อมูลนี้ คือคนที่มีความรู้ด้านสถิติและคณิตศาสตร์สำหรับใช้ในการวิเคราะห์ข้อมูล และเนื่องจากข้อมูลที่เรามักจะต้องเจอนั้นมีปริมาณมาก จึงจำเป็นต้องใช้ความรู้ด้านการเขียนโปรแกรมเพื่อให้คอมพิวเตอร์ทำการคำนวณต่าง ๆ ให้เรา นักวิทยาศาสตร์ข้อมูลในปัจจุบันจึงมักมาจากกลุ่มคนในด้านวิทยาการคอมพิวเตอร์และนักคณิตศาสตร์\n",
    "\n",
    "ในปฏิบัติการครั้งนี้ เราจะมาดูภาพรวมของการทำงานด้านวิทยาศาสตร์ข้อมูล โดยใช้ _ภาษาไพธอน_ (Python) ซึ่งได้รับความนิยมมาก เนื่องจากมีเครื่องมือสำหรับงานด้านวิทยาศาสตร์ข้อมูลครบถ้วน"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ภาษาไพธอนสำหรับวิทยาศาสตร์ข้อมูล"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ถึงแม้ภาษาไพธอนจะเป็นภาษาที่เรียนรู้ได้ง่าย \n",
    "แต่การใช้แค่เครื่องมือพื้นฐานของตัวภาษาในการเขียนโปรแกรมที่ต้องทำการประมวลผลข้อมูลจำนวนมากอาจไม่มีประสิทธิภาพดี \n",
    "เนื่องจากตัวภาษามีความยืดหยุ่นสูงจึงมีกระบวนการทำงานเบื้องหลังจำนวนมากตามไปด้วย อย่างไรก็ดี ในปัจจุบันเรามีเครื่องมือเพิ่มเติมในภาษาไพธอน\n",
    "ที่ลดข้อเสียเหล่านี้ลงไป ทำให้เราสามารถนำภาษาไพธอนมาใช้ในการประมวลผลและวิเคราะห์ข้อมูลต่าง ๆ ได้อย่างมีประสิทธิภาพและง่าย!!!\n",
    "\n",
    "ตลอดการแสดงตัวอย่างการวิเคราะห์ข้อมูลในปฏิบัติการนี้ เราจะทำความรู้จักเครื่องมือสำคัญของภาษาไพธอนสำหรับนักวิทยาศาสตร์ข้อมูลได้แก่\n",
    "\n",
    "1. Numpy (`numpy`): module สำหรับประมวลผลเชิงตัวเลขอย่างมีประสิทธิภาพ\n",
    "1. Pandas (`pandas`): modle สำหรับจัดการข้อมูลที่มีความซับซ้อนสูง\n",
    "1. Matplotlib (`matplotlib`): module สำหรับทำการแสดงผลข้อมูลด้วยแผนภาพหรือกราฟในลักษณะต่าง ๆ\n",
    "1. Scikit-Learn (`sklearn`): module สำหรับสร้างแบบจำลองการเรียนรู้ของเครื่อง \n",
    "เพื่อใช้ในการวิเคราะห์ข้อมูล และนำไปสู่การตัดสินใจหรือทำนายข้อมูลต่าง ๆ \n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## รายได้เฉลี่ยต่อครัวเรือนของประชากรไทย\n",
    "\n",
    "ในปฏิบัติการครั้งนี้ เราจะมาวิเคราะห์ข้อมูลเกี่ยวกับรายได้เฉลี่ยต่อครัวเรือนของประชากรไทย โดยใช้ข้อมูลจาก _ศูนย์กลางข้อมูลภาครัฐ_ (https://data.go.th/) สำนักงานรัฐบาลอิเล็กทรอนิกส์ (องค์การมหาชน) \n",
    "\n",
    "ไฟล์ข้อมูลที่ใช้เป็นไฟล์จากแหล่งข้อมูลจริง โดยไม่มีการปรับแก้ใด ๆ \n",
    "เพื่อให้เราได้เห็นปัญหาและฝึกแก้ไขปัญหาในการจัดการข้อมูลจากแหล่งข้อมูลจริง\n",
    "\n",
    "ข้อมูลรายได้เฉลี่ยต่อเดือนต่อครัวเรือน จำแนกตามภาค และจังหวัด พ.ศ. 2541 - 2558 อยู่ในไฟล์ชื่อ `income.csv`\n",
    "เราสามารถเปิดข้อมูลในไฟล์ประเภทดังกล่าวมาเก็บใส่ตัวแปรได้อย่างง่ายโดยใช้ฟังก์ชัน `read_csv` ของ Pandas ซึ่งทำได้โดยใช้คำสั่งต่อไปนี้ (สามารถทดลองรันคำสั่งได้โดยเลือกที่เซลคำสั่งโปรแกรมภาษาไพธอนและกด Shift+Enter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "income = pd.read_csv('data/income.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราจะได้ข้อมูลในตัวแปร `income` นั้นมีลักษณะเป็นตารางข้อมูลที่มีการระบุหัวตาราง และหัวแถว เราเรียกชนิดข้อมูลเช่นนี้ว่า `DataFrame` ซึ่งเป็นชนิดข้อมูลหลักของ Pandas ในการจัดการข้อมูล เราสามารถเรียกดูเฉพาะส่วนบนของตารางได้โดยใช้คำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "  รายได้เฉลี่ยต่อเดือนต่อครัวเรือน จำแนกตามภาค และจังหวัด พ.ศ. 2541 - 2558  \\\n",
       "0  Average Monthly Income Per Household: 1998 - 2015                         \n",
       "1                                                NaN                         \n",
       "2                                     ภาค และจังหวัด                         \n",
       "3                                                NaN                         \n",
       "4                                                NaN                         \n",
       "\n",
       "  Unnamed: 1  Unnamed: 2  Unnamed: 3  Unnamed: 4  Unnamed: 5  Unnamed: 6  \\\n",
       "0        NaN         NaN         NaN         NaN         NaN         NaN   \n",
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       "\n",
       "[5 rows x 67 columns]"
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    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราเรียกหัวตารางของแต่ละหลักว่า `column` และเรียกหัวแถวของแต่ละแถวว่า `index` อย่างไรก็ดี จะเห็นว่าหัวตารางนั้นประกอบด้วยข้อความ `Unnamed` จำนวนมาก ซึ่งไม่ใช่สิ่งที่เราต้องการ สาเหตุเกิดจากในไฟล์ `income.csv` นั้นไม่ได้ระบุหัวตารางไว้ที่บรรทัดแรก เราสามารถอ่านไฟล์ใหม่โดยข้ามสามบรรทัดแรกไปดังนี้\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>-1998.0</td>\n",
       "      <td>-2000.0</td>\n",
       "      <td>-2002.0</td>\n",
       "      <td>-2004.0</td>\n",
       "      <td>-2006.0</td>\n",
       "      <td>-2007.0</td>\n",
       "      <td>-2009.0</td>\n",
       "      <td>-2011.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ทั่วราชอาณาจักร</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ตอนพิเศษ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ภาค และจังหวัด Unnamed: 1     2541     2543     2545     2547  \\\n",
       "0                  NaN        NaN  -1998.0  -2000.0  -2002.0  -2004.0   \n",
       "1                  NaN        NaN      NaN      NaN      NaN      NaN   \n",
       "2     ทั่วราชอาณาจักร         NaN  12492.0  12150.0  13736.0  14963.0   \n",
       "3            ตอนพิเศษ         NaN  24929.0  25242.0  28239.0  28135.0   \n",
       "4       กรุงเทพมหานคร         NaN  26054.0  26909.0  29589.0  29843.0   \n",
       "\n",
       "      2549     2550     2552     2554     ...       Unnamed: 57  Unnamed: 58  \\\n",
       "0  -2006.0  -2007.0  -2009.0  -2011.0     ...               NaN          NaN   \n",
       "1      NaN      NaN      NaN      NaN     ...               NaN          NaN   \n",
       "2  17787.0  18660.0  20904.0  23236.0     ...               NaN          NaN   \n",
       "3  33088.0  35007.0  37732.0  41631.0     ...               NaN          NaN   \n",
       "4  36658.0  39020.0  42380.0  48951.0     ...               NaN          NaN   \n",
       "\n",
       "  Unnamed: 59  Unnamed: 60  Unnamed: 61  Unnamed: 62  Unnamed: 63  \\\n",
       "0         NaN          NaN          NaN          NaN          NaN   \n",
       "1         NaN          NaN          NaN          NaN          NaN   \n",
       "2         NaN          NaN          NaN          NaN          NaN   \n",
       "3         NaN          NaN          NaN          NaN          NaN   \n",
       "4         NaN          NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 64  Unnamed: 65  Unnamed: 66  \n",
       "0          NaN          NaN          NaN  \n",
       "1          NaN          NaN          NaN  \n",
       "2          NaN          NaN          NaN  \n",
       "3          NaN          NaN          NaN  \n",
       "4          NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = pd.read_csv('data/income.csv', skiprows=3)\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สังเกตว่าเราได้เลขปีเป็นหัวตารางแล้ว แต่ตารางในตัวแปร `income` ยังคงมีหลักว่างอยู่จำนวนมาก เราสามารถกรองข้อมูลให้เหลือเฉพาะหลักที่ต้องการได้ด้วยคำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Region and province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1998.0</td>\n",
       "      <td>-2000.0</td>\n",
       "      <td>-2002.0</td>\n",
       "      <td>-2004.0</td>\n",
       "      <td>-2006.0</td>\n",
       "      <td>-2007.0</td>\n",
       "      <td>-2009.0</td>\n",
       "      <td>-2011.0</td>\n",
       "      <td>-2013.0</td>\n",
       "      <td>-2015.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>Whole Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "      <td>Greater Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2541     2543     2545     2547     2549     2550     2552     2554  \\\n",
       "0  -1998.0  -2000.0  -2002.0  -2004.0  -2006.0  -2007.0  -2009.0  -2011.0   \n",
       "1      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "2  12492.0  12150.0  13736.0  14963.0  17787.0  18660.0  20904.0  23236.0   \n",
       "3  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0  37732.0  41631.0   \n",
       "4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0  48951.0   \n",
       "\n",
       "      2556     2558 Region and province  \n",
       "0  -2013.0  -2015.0                 NaN  \n",
       "1      NaN      NaN                 NaN  \n",
       "2  25194.0  26915.0      Whole Kingdom   \n",
       "3  43058.0  41002.0    Greater Bangkok   \n",
       "4  49191.0  45572.0            Bangkok   "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = ['2541', '2543', '2545', '2547', '2549', '2550', \n",
    "                    '2552', '2554', '2556', '2558', 'Region and province']\n",
    "income = income[selected_columns]\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "คราวนี้จะเห็นว่าตาราง `income` เหลือเฉพาะข้อมูลในหลักที่เราสนใจเท่านั้น อย่างไรก็ดี ข้อมูลในสองบรรทัดบนนั้นมีข้อมูลพิเศษเป็น `NaN` (not a number)\n",
    "เนื่องจากบรรทัดแรกเป็นข้อมูลปีค.ศ.ที่สอดคล้องกัน และบรรทัดต่อมาเป็นบรรทัดว่าง เราสามารถลดบรรทัดที่มีค่า `NaN` ทิ้งได้ง่ายโดยใช้คำสั่ง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Region and province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>Whole Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "      <td>Greater Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18100.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>21302.0</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>29575.0</td>\n",
       "      <td>25457.0</td>\n",
       "      <td>Samut Prakan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24211.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>32743.0</td>\n",
       "      <td>34626.0</td>\n",
       "      <td>35120.0</td>\n",
       "      <td>30664.0</td>\n",
       "      <td>36884.0</td>\n",
       "      <td>Nonthaburi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2541     2543     2545     2547     2549     2550     2552     2554  \\\n",
       "2  12492.0  12150.0  13736.0  14963.0  17787.0  18660.0  20904.0  23236.0   \n",
       "3  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0  37732.0  41631.0   \n",
       "4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0  48951.0   \n",
       "5  18100.0  15745.0  19680.0  19946.0  20382.0  21302.0  23359.0  23798.0   \n",
       "6  24211.0  24566.0  29119.0  26658.0  31152.0  32743.0  34626.0  35120.0   \n",
       "\n",
       "      2556     2558 Region and province  \n",
       "2  25194.0  26915.0      Whole Kingdom   \n",
       "3  43058.0  41002.0    Greater Bangkok   \n",
       "4  49191.0  45572.0            Bangkok   \n",
       "5  29575.0  25457.0       Samut Prakan   \n",
       "6  30664.0  36884.0         Nonthaburi   "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.dropna(inplace=True)\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "ถึงตรงนี้จะเห็นว่าเราได้ตารางข้อมูลรายได้ที่ _สะอาด_ แล้ว เพื่อความสะดวกมากขึ้นในการทำงานต่อไป เราจะเปลี่ยนชื่อ column ที่ระบุปีจาก string ให้เป็นตัวเลข และเปลี่ยนชื่อ column สุดท้ายเป็น `Province` ซึ่งทำได้ด้วยคำสั่งต่อไปนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Province</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12492.0</td>\n",
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       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>Whole Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "      <td>Greater Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18100.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>21302.0</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>29575.0</td>\n",
       "      <td>25457.0</td>\n",
       "      <td>Samut Prakan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>24211.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>32743.0</td>\n",
       "      <td>34626.0</td>\n",
       "      <td>35120.0</td>\n",
       "      <td>30664.0</td>\n",
       "      <td>36884.0</td>\n",
       "      <td>Nonthaburi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2541     2543     2545     2547     2549     2550     2552     2554  \\\n",
       "2  12492.0  12150.0  13736.0  14963.0  17787.0  18660.0  20904.0  23236.0   \n",
       "3  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0  37732.0  41631.0   \n",
       "4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0  48951.0   \n",
       "5  18100.0  15745.0  19680.0  19946.0  20382.0  21302.0  23359.0  23798.0   \n",
       "6  24211.0  24566.0  29119.0  26658.0  31152.0  32743.0  34626.0  35120.0   \n",
       "\n",
       "      2556     2558            Province  \n",
       "2  25194.0  26915.0      Whole Kingdom   \n",
       "3  43058.0  41002.0    Greater Bangkok   \n",
       "4  49191.0  45572.0            Bangkok   \n",
       "5  29575.0  25457.0       Samut Prakan   \n",
       "6  30664.0  36884.0         Nonthaburi   "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.columns = [2541, 2543, 2545, 2547, 2549, \n",
    "                  2550, 2552, 2554, 2556, 2558, 'Province']\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## การแสดงข้อมูลรายได้ด้วยภาพ\n",
    "\n",
    "หากเราต้องการวิเคราะห์รายได้เฉลี่ยต่อครัวเรือนในปี 2558 ของแต่ละจังหวัด ก็สามารถดูได้จากตาราง `income` (เราสามารถเรียกดูเฉพาะ column  `Province` และ `2558` ได้โดยการเรียก `income[['Province', 2558]]`\n",
    "\n",
    "อย่างไรก็ดี การแสดงข้อมูลด้วยวิธีดังกล่าวอาจไม่ใช่วิธีที่ดี ในวิทยาศาสตร์ข้อมูล เรานิยมแสดงข้อมูลด้วยภาพเนื่องจากทำให้เรามองเห็นภาพรวมของข้อมูลได้ดีกว่ารายการของตัวเลข ในหัวข้อนี้เราจะมาดูการใช้ Matplotlib ในการแสดงแผนภาพเบื้องต้น"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เริ่มต้นด้วยการ import `matplotlib.pyplot` ซึ่งเราจะเรียกแทนด้วยตัวแปร `plt` เพื่อความสะดวก\n",
    "นอกจากนี้ เราสามารถสั่ง `import seaborn; seaborn.set()` เพิ่มเติมเพื่อช่วยตกแต่งกราฟต่าง ๆ ให้สวยงาม"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "seaborn.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "บนเว็บไซต์ศูนย์กลางข้อมูลภาครัฐ (https://data.go.th/) มีข้อมูลตำแหน่ง (ละติจูด และ ลองจิจูด) ของตำบลในประเทศไทย ซึ่งจะช่วยให้เราแสดงข้อมูลรายได้ตามตำแหน่งได้ เราจะลองอ่านข้อมูลจากไฟล์ดังกล่าวมาลองพลอตตำแหน่งของแต่ละตำบลดูก่อน โดยข้อมูลดังกล่าวเก็บอยู่ในไฟล์ชื่ `tambon.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AD_LEVEL</th>\n",
       "      <th>TA_ID</th>\n",
       "      <th>TAMBON_T</th>\n",
       "      <th>TAMBON_E</th>\n",
       "      <th>AM_ID</th>\n",
       "      <th>AMPHOE_T</th>\n",
       "      <th>AMPHOE_E</th>\n",
       "      <th>CH_ID</th>\n",
       "      <th>CHANGWAT_T</th>\n",
       "      <th>CHANGWAT_E</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>910106</td>\n",
       "      <td>ต. เกาะสาหร่าย</td>\n",
       "      <td>Ko Sarai</td>\n",
       "      <td>9101</td>\n",
       "      <td>อ. เมืองสตูล</td>\n",
       "      <td>Mueang Satun</td>\n",
       "      <td>91</td>\n",
       "      <td>จ. สตูล</td>\n",
       "      <td>Satun</td>\n",
       "      <td>6.546</td>\n",
       "      <td>99.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>210114</td>\n",
       "      <td>ต. มาบตาพุด</td>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>2101</td>\n",
       "      <td>อ. เมืองระยอง</td>\n",
       "      <td>Mueang Rayong</td>\n",
       "      <td>21</td>\n",
       "      <td>จ. ระยอง</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.646</td>\n",
       "      <td>101.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>210114</td>\n",
       "      <td>ต. มาบตาพุด</td>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>2101</td>\n",
       "      <td>อ. เมืองระยอง</td>\n",
       "      <td>Mueang Rayong</td>\n",
       "      <td>21</td>\n",
       "      <td>จ. ระยอง</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.645</td>\n",
       "      <td>101.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>210114</td>\n",
       "      <td>ต. มาบตาพุด</td>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>2101</td>\n",
       "      <td>อ. เมืองระยอง</td>\n",
       "      <td>Mueang Rayong</td>\n",
       "      <td>21</td>\n",
       "      <td>จ. ระยอง</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.649</td>\n",
       "      <td>101.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>210114</td>\n",
       "      <td>ต. มาบตาพุด</td>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>2101</td>\n",
       "      <td>อ. เมืองระยอง</td>\n",
       "      <td>Mueang Rayong</td>\n",
       "      <td>21</td>\n",
       "      <td>จ. ระยอง</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.644</td>\n",
       "      <td>101.169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AD_LEVEL   TA_ID        TAMBON_T   TAMBON_E  AM_ID       AMPHOE_T  \\\n",
       "0         4  910106  ต. เกาะสาหร่าย   Ko Sarai   9101   อ. เมืองสตูล   \n",
       "1         4  210114     ต. มาบตาพุด  Maptaphut   2101  อ. เมืองระยอง   \n",
       "2         4  210114     ต. มาบตาพุด  Maptaphut   2101  อ. เมืองระยอง   \n",
       "3         4  210114     ต. มาบตาพุด  Maptaphut   2101  อ. เมืองระยอง   \n",
       "4         4  210114     ต. มาบตาพุด  Maptaphut   2101  อ. เมืองระยอง   \n",
       "\n",
       "        AMPHOE_E  CH_ID CHANGWAT_T CHANGWAT_E     LAT     LONG  \n",
       "0   Mueang Satun     91    จ. สตูล      Satun   6.546   99.706  \n",
       "1  Mueang Rayong     21   จ. ระยอง     Rayong  12.646  101.171  \n",
       "2  Mueang Rayong     21   จ. ระยอง     Rayong  12.645  101.170  \n",
       "3  Mueang Rayong     21   จ. ระยอง     Rayong  12.649  101.174  \n",
       "4  Mueang Rayong     21   จ. ระยอง     Rayong  12.644  101.169  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations = pd.read_csv('data/tambon.csv')\n",
    "locations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เนื่องจากเราต้องการเพียงข้อมูลละติจูดและลองจิจูดของแต่ละตำบลเท่านั้น เราสามารถกรองข้อมูลได้โดยใช้คำสั่งดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TAMBON_E</th>\n",
       "      <th>CHANGWAT_E</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LONG</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ko Sarai</td>\n",
       "      <td>Satun</td>\n",
       "      <td>6.546</td>\n",
       "      <td>99.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.646</td>\n",
       "      <td>101.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.645</td>\n",
       "      <td>101.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.649</td>\n",
       "      <td>101.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.644</td>\n",
       "      <td>101.169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TAMBON_E CHANGWAT_E     LAT     LONG\n",
       "0   Ko Sarai      Satun   6.546   99.706\n",
       "1  Maptaphut     Rayong  12.646  101.171\n",
       "2  Maptaphut     Rayong  12.645  101.170\n",
       "3  Maptaphut     Rayong  12.649  101.174\n",
       "4  Maptaphut     Rayong  12.644  101.169"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_columns = ['TAMBON_E', 'CHANGWAT_E', 'LAT', 'LONG']\n",
    "locations = locations[selected_columns]\n",
    "locations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "และทำการเปลี่ยนชื่อ column เพื่อความสะดวก"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tambon</th>\n",
       "      <th>Province</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ko Sarai</td>\n",
       "      <td>Satun</td>\n",
       "      <td>6.546</td>\n",
       "      <td>99.706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.646</td>\n",
       "      <td>101.171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.645</td>\n",
       "      <td>101.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.649</td>\n",
       "      <td>101.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maptaphut</td>\n",
       "      <td>Rayong</td>\n",
       "      <td>12.644</td>\n",
       "      <td>101.169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Tambon Province  Latitude  Longitude\n",
       "0   Ko Sarai    Satun     6.546     99.706\n",
       "1  Maptaphut   Rayong    12.646    101.171\n",
       "2  Maptaphut   Rayong    12.645    101.170\n",
       "3  Maptaphut   Rayong    12.649    101.174\n",
       "4  Maptaphut   Rayong    12.644    101.169"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locations.columns = ['Tambon', 'Province', 'Latitude', 'Longitude']\n",
    "locations.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ถึงตรงนี้ เราจะลองพลอตจุดสำหรับแต่ละตำบลตามพิกัดของตำบลนั้น ๆ ด้วยฟังก์ชัน `scatter` ของ `plt` ซึ่งจะรับพารามิเตอร์หลักสองตัวได้แก่\n",
    "ลิสต์ของข้อมูลตามแกน `x` และลิสต์ของข้อมูลตามแกน `y` ในที่นี้เราจะใช้ `locations['Longitude']` และ `locations['Latitude']` แทนข้อมูลดังกล่าวตามลำดับ\n",
    "\n",
    "ก่อนอื่นเราจะกำหนดขนาดภาพก่อนด้วยคำสั่งดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd5d748>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากนั้นสั่งให้ `plt` พลอตจุดต่าง ๆ ลงไป"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10ed2fe10>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.scatter(locations['Longitude'], locations['Latitude'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากนั้น หากต้องการให้ข้อมูลทั้งหมดที่พลอตอยู่บน `plt` แสดงออกมา ให้เรียกคำสั่งดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CVg1PJ08OUjdoWY4atAYJYBgYtg2JYRmT00H0DE6k/U6qx62KbOi9rzpWkITqBQP6oVe3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\nvwMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10bd5d748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากภาพ เราจะสามารถสังเกตความหนาแน่นของตำบลในแต่ละพื้นที่ได้จากจำนวนจุดในภาพ \n",
    "ถึงตรงนี้ เราจะกลับมาหาวิธีแสดงรายได้เฉลี่ยในปี 2558 ของแต่ละจังหวัดในตัวแปร `income` บนแผนที่เช่นนี้\n",
    "\n",
    "ขั้นตอนแรก เราต้องทำการรวมข้อมูลจากไฟล์ `income` และ `locations` เสียก่อน\n",
    "เพื่อให้ข้อมูลพิกัดและรายได้มีความสอดคล้องกัน เราสามารถรวม DataFrame เข้าด้วยกันได้ด้วยฟังก์ชัน `merge` ดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "location_income = pd.merge(income, locations, on='Province')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "argument `on='Province'` เป็นการบอกฟังก์ชัน `merge` ให้รวมข้อมูลโดยเทียบข้อมูลใร column `Province` ให้ตรงกัน\n",
    "อย่างไรก็ดี เมื่อเราเรียกดูสิ่งที่ได้จาก `location_income` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\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>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Province</th>\n",
       "      <th>Tambon</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [2541, 2543, 2545, 2547, 2549, 2550, 2552, 2554, 2556, 2558, Province, Tambon, Latitude, Longitude]\n",
       "Index: []"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะเห็นว่าตารางผลลัพธ์มีชื่อ column ของทั้งสองตารางรวมกันเรียบร้อยแล้ว แต่ไม่มีข้อมูล!!! นั่นหมายความว่าฟังก์ชัน `merge` ไม่พบแถวใน `income`\n",
    "และแถวใน `location` ที่มีค่าใน column `Province` ตรงกันเลย!!!\n",
    "\n",
    "ปัญหานี้เกิดจากข้อมูลในไฟล์ income.csv ใน column \"Region and province\" (ที่เราตั้งชื่อให้ใหม่ว่า \"Province\") \n",
    "นั้นมีช่องว่างอยู่รอบข้อความ เช่น `\"Bangkok \"` ซึ่งเมื่อนำมาเทียบกับ `\"Bangkok\"` ในไฟล์ location.csv จึงไม่ตรงกัน\n",
    "เราจำเป็นต้องไปจัดการลบช่องว่างใน column ดังกล่าวของทุกแถวใน `income` วิธีหนึ่งที่ทำได้คือการวนรอบดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "row = income.shape[0]\n",
    "income = income.reset_index()\n",
    "for i in range(row):\n",
    "    income.loc[i, 'Province'] = income.loc[i, 'Province'].strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "อย่างไรก็ดี การวนรอบด้วยคำสั่ง `for` นั้นจะเสียเวลาเกินจำเป็น Pandas เปิดช่องทางให้เราสามารถคำนวณข้อมูลในตารางได้โดยใช้เทคนิค\n",
    "_vectorization_ ซึ่งจะใช้เวลาเร็วกว่ามาก ใน jupyter notebook เราสามารถจับเวลาการทำงานของแต่ละเซลได้ด้วยคำสั่งพิเศษ\n",
    "`%%timeit` เรามาลองเปรียบเทียบการทำงานสองวิธีดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 40 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "for i in range(row):\n",
    "    income.loc[i, 'Province'] = income.loc[i, 'Province'].strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 232 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "income['Province'].str.strip()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะเห็นว่าการใช้ vectorization ทำงานได้เร็วกว่ามาก ดังนั้น ทุกครั้งที่เราเขียนโค้ดสำหรับประมวลผลข้อมูลด้วยวนรอบ `for` \n",
    "ควรนึกเสมอว่าเราสามารถทำกระบวนการดังกล่าวด้วย vectorization ได้หรือไม่\n",
    "\n",
    "ถึงตรงนี้ เราสามารถรวมไฟล์ `income` และ `locations` ใหม่ได้ดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Province</th>\n",
       "      <th>Tambon</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Sao Chingcha</td>\n",
       "      <td>13.753</td>\n",
       "      <td>100.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>San Chaopho Suea</td>\n",
       "      <td>13.754</td>\n",
       "      <td>100.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Talat Yot</td>\n",
       "      <td>13.760</td>\n",
       "      <td>100.498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Wat Ratchabophit</td>\n",
       "      <td>13.750</td>\n",
       "      <td>100.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "      <td>Samran Rat</td>\n",
       "      <td>13.751</td>\n",
       "      <td>100.503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     2541     2543     2545     2547     2549     2550     2552  \\\n",
       "0      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "1      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "2      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "3      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "4      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "\n",
       "      2554     2556     2558 Province            Tambon  Latitude  Longitude  \n",
       "0  48951.0  49191.0  45572.0  Bangkok      Sao Chingcha    13.753    100.500  \n",
       "1  48951.0  49191.0  45572.0  Bangkok  San Chaopho Suea    13.754    100.497  \n",
       "2  48951.0  49191.0  45572.0  Bangkok         Talat Yot    13.760    100.498  \n",
       "3  48951.0  49191.0  45572.0  Bangkok  Wat Ratchabophit    13.750    100.499  \n",
       "4  48951.0  49191.0  45572.0  Bangkok        Samran Rat    13.751    100.503  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location_income = pd.merge(income, locations, on='Province')\n",
    "location_income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "คราวนี้เราจะลองพลอตพิกัดของแต่ละตำบลด้วย `plt.scatter` เช่นเดิม แต่ละปรับโทนสีของแต่ละจุดของตำบลตามรายได้เฉลี่ยต่าครัวเรือนของจังหวัดของตำบลนั้น ๆ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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goNl0aDQgFgvLcaapMzAQp9n0yGQilEotolGNnp7wuHs4A8jG8wLa7YBGw8Z1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\nPVxod2Mwt2x2NHh0nsNgbmjG6iK9KAyh1U5OaDWRfLevC39sDb06THM1/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RkrjgsIMUTV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6jixzjMc1tzqQNhXtvKAOwqwKrFYtxbhBlw1NCXkK2vHkuXH8euvAj3+85N/+2xFpauh+3ZoJ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\nZLtPXwPVvGS4k3D4RsqzJznLRcv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LwEsXA8/vGyG4DAneKYkX7p9Zltd2T6mCouKoAxxN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zkdpcq8cWxkEe8chysTL3/6xHOlpySiRISt3OzsYAjDvYWQp2alf6GwgeN/zDzzrqLhoBFO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\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NkgzWBUQdmypUDaxYfzzCfr+ADW/5mSSs7IMRDHLLL8buibH5p8EiXC2q5CgD8dTUEJX0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\n8RFCCCHEgvH/ASf0bGv9LuCsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2a5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 12))\n",
    "plt.scatter(location_income['Longitude'], location_income['Latitude'],\n",
    "            c=location_income[2558], cmap='jet', alpha=0.3)\n",
    "plt.colorbar()\n",
    "plt.title('Income 2558')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากภาพ เราสามารถบอกได้คร่าว ๆ ว่าจังหวัดที่มีรายได้เฉลี่ยต่อครัวเรือนสูง คือจังหวัดที่มีโทนสีเป็นสีแดง ได่แก่ กทม และนครปฐม เป็นต้น\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## วิเคราะห์รายได้เฉลี่ย และทำนายรายได้เฉลี่ยปี 2560\n",
    "\n",
    "ในหัวข้อนี้ เราจะมาดูกระบวนการสำคัญอีกส่วนหนึ่งในงานด้านวิทยาศาสตร์ข้อมูล คือการวิเคราะห์ข้อมูล \n",
    "หากเราย้อนกลับมาดูที่ข้อมูลรายได้เฉลี่ยต่อครัวเรือนของแต่ละจังหวัดในไฟล์ `income` ดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>Whole Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "      <td>Greater Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>21302.0</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>29575.0</td>\n",
       "      <td>25457.0</td>\n",
       "      <td>Samut Prakan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>24211.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>32743.0</td>\n",
       "      <td>34626.0</td>\n",
       "      <td>35120.0</td>\n",
       "      <td>30664.0</td>\n",
       "      <td>36884.0</td>\n",
       "      <td>Nonthaburi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index     2541     2543     2545     2547     2549     2550     2552  \\\n",
       "0      2  12492.0  12150.0  13736.0  14963.0  17787.0  18660.0  20904.0   \n",
       "1      3  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0  37732.0   \n",
       "2      4  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0   \n",
       "3      5  18100.0  15745.0  19680.0  19946.0  20382.0  21302.0  23359.0   \n",
       "4      6  24211.0  24566.0  29119.0  26658.0  31152.0  32743.0  34626.0   \n",
       "\n",
       "      2554     2556     2558         Province  \n",
       "0  23236.0  25194.0  26915.0    Whole Kingdom  \n",
       "1  41631.0  43058.0  41002.0  Greater Bangkok  \n",
       "2  48951.0  49191.0  45572.0          Bangkok  \n",
       "3  23798.0  29575.0  25457.0     Samut Prakan  \n",
       "4  35120.0  30664.0  36884.0       Nonthaburi  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากการทำงานในหัวข้อก่อนหน้า ทำให้ `income` ของเรามี column `index` เกิดขึ้นมา เราสามารถลบออกได้ด้วยคำสั่ง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "      <th>Province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "      <td>Whole Kingdom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "      <td>Greater Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "      <td>Bangkok</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18100.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>21302.0</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>29575.0</td>\n",
       "      <td>25457.0</td>\n",
       "      <td>Samut Prakan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24211.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>32743.0</td>\n",
       "      <td>34626.0</td>\n",
       "      <td>35120.0</td>\n",
       "      <td>30664.0</td>\n",
       "      <td>36884.0</td>\n",
       "      <td>Nonthaburi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      2541     2543     2545     2547     2549     2550     2552     2554  \\\n",
       "0  12492.0  12150.0  13736.0  14963.0  17787.0  18660.0  20904.0  23236.0   \n",
       "1  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0  37732.0  41631.0   \n",
       "2  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0  42380.0  48951.0   \n",
       "3  18100.0  15745.0  19680.0  19946.0  20382.0  21302.0  23359.0  23798.0   \n",
       "4  24211.0  24566.0  29119.0  26658.0  31152.0  32743.0  34626.0  35120.0   \n",
       "\n",
       "      2556     2558         Province  \n",
       "0  25194.0  26915.0    Whole Kingdom  \n",
       "1  43058.0  41002.0  Greater Bangkok  \n",
       "2  49191.0  45572.0          Bangkok  \n",
       "3  29575.0  25457.0     Samut Prakan  \n",
       "4  30664.0  36884.0       Nonthaburi  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.drop('index', axis=1, inplace=True)\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สังเกตว่าในตอนนี้ ตาราง `income` ของเรามี _หัวแถว_  หรือ index เป็นตัวเลข 0, 1, 2, ... \n",
    "ซึ่งไม่มีประโยชน์ เราสามารถตั้งให้นำชื่อจังหวัดจาก column `Province` มาเป็น index ได้ด้วยคำสั่งดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\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>2541</th>\n",
       "      <th>2543</th>\n",
       "      <th>2545</th>\n",
       "      <th>2547</th>\n",
       "      <th>2549</th>\n",
       "      <th>2550</th>\n",
       "      <th>2552</th>\n",
       "      <th>2554</th>\n",
       "      <th>2556</th>\n",
       "      <th>2558</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Province</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Whole Kingdom</th>\n",
       "      <td>12492.0</td>\n",
       "      <td>12150.0</td>\n",
       "      <td>13736.0</td>\n",
       "      <td>14963.0</td>\n",
       "      <td>17787.0</td>\n",
       "      <td>18660.0</td>\n",
       "      <td>20904.0</td>\n",
       "      <td>23236.0</td>\n",
       "      <td>25194.0</td>\n",
       "      <td>26915.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greater Bangkok</th>\n",
       "      <td>24929.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>35007.0</td>\n",
       "      <td>37732.0</td>\n",
       "      <td>41631.0</td>\n",
       "      <td>43058.0</td>\n",
       "      <td>41002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangkok</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>39020.0</td>\n",
       "      <td>42380.0</td>\n",
       "      <td>48951.0</td>\n",
       "      <td>49191.0</td>\n",
       "      <td>45572.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Samut Prakan</th>\n",
       "      <td>18100.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>21302.0</td>\n",
       "      <td>23359.0</td>\n",
       "      <td>23798.0</td>\n",
       "      <td>29575.0</td>\n",
       "      <td>25457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nonthaburi</th>\n",
       "      <td>24211.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>32743.0</td>\n",
       "      <td>34626.0</td>\n",
       "      <td>35120.0</td>\n",
       "      <td>30664.0</td>\n",
       "      <td>36884.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    2541     2543     2545     2547     2549     2550  \\\n",
       "Province                                                                \n",
       "Whole Kingdom    12492.0  12150.0  13736.0  14963.0  17787.0  18660.0   \n",
       "Greater Bangkok  24929.0  25242.0  28239.0  28135.0  33088.0  35007.0   \n",
       "Bangkok          26054.0  26909.0  29589.0  29843.0  36658.0  39020.0   \n",
       "Samut Prakan     18100.0  15745.0  19680.0  19946.0  20382.0  21302.0   \n",
       "Nonthaburi       24211.0  24566.0  29119.0  26658.0  31152.0  32743.0   \n",
       "\n",
       "                    2552     2554     2556     2558  \n",
       "Province                                             \n",
       "Whole Kingdom    20904.0  23236.0  25194.0  26915.0  \n",
       "Greater Bangkok  37732.0  41631.0  43058.0  41002.0  \n",
       "Bangkok          42380.0  48951.0  49191.0  45572.0  \n",
       "Samut Prakan     23359.0  23798.0  29575.0  25457.0  \n",
       "Nonthaburi       34626.0  35120.0  30664.0  36884.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income.set_index('Province', inplace=True)\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "โดยทั่วไป เวลาวิเคราะห์ข้อมูลที่เกี่ยวข้องกับเวลา (เช่นปีใน `income`) เรามักจะให้ index ของแต่ละแถวเป็นเวลาเรียงลำดับกัน\n",
    "เราสามารถแก้ไข `income` ได้ง่ายโดยการสลับระหว่าง index และ column ซึ่งทำได้ด้วยการ transpose ตารางเดิมดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Province</th>\n",
       "      <th>Whole Kingdom</th>\n",
       "      <th>Greater Bangkok</th>\n",
       "      <th>Bangkok</th>\n",
       "      <th>Samut Prakan</th>\n",
       "      <th>Nonthaburi</th>\n",
       "      <th>Pathum Thani</th>\n",
       "      <th>Central Region</th>\n",
       "      <th>Phra Nakhon Si Ayutthaya</th>\n",
       "      <th>Ang Thong</th>\n",
       "      <th>Lop Buri</th>\n",
       "      <th>...</th>\n",
       "      <th>Surat Thani</th>\n",
       "      <th>Ranong</th>\n",
       "      <th>Chumphon</th>\n",
       "      <th>Songkhla</th>\n",
       "      <th>Satun</th>\n",
       "      <th>Trang</th>\n",
       "      <th>Phattalung</th>\n",
       "      <th>Pattani</th>\n",
       "      <th>Yala</th>\n",
       "      <th>Narathiwat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>12492.0</td>\n",
       "      <td>24929.0</td>\n",
       "      <td>26054.0</td>\n",
       "      <td>18100.0</td>\n",
       "      <td>24211.0</td>\n",
       "      <td>21793.0</td>\n",
       "      <td>12643.0</td>\n",
       "      <td>12918.0</td>\n",
       "      <td>10878.0</td>\n",
       "      <td>10587.0</td>\n",
       "      <td>...</td>\n",
       "      <td>11967.0</td>\n",
       "      <td>9033.0</td>\n",
       "      <td>10232.0</td>\n",
       "      <td>15057.0</td>\n",
       "      <td>8924.0</td>\n",
       "      <td>12973.0</td>\n",
       "      <td>12315.0</td>\n",
       "      <td>10737.0</td>\n",
       "      <td>7560.0</td>\n",
       "      <td>6281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>12150.0</td>\n",
       "      <td>25242.0</td>\n",
       "      <td>26909.0</td>\n",
       "      <td>15745.0</td>\n",
       "      <td>24566.0</td>\n",
       "      <td>19282.0</td>\n",
       "      <td>13012.0</td>\n",
       "      <td>14904.0</td>\n",
       "      <td>12544.0</td>\n",
       "      <td>10649.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10800.0</td>\n",
       "      <td>9410.0</td>\n",
       "      <td>10454.0</td>\n",
       "      <td>13505.0</td>\n",
       "      <td>9711.0</td>\n",
       "      <td>12782.0</td>\n",
       "      <td>11550.0</td>\n",
       "      <td>9759.0</td>\n",
       "      <td>9932.0</td>\n",
       "      <td>7802.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>13736.0</td>\n",
       "      <td>28239.0</td>\n",
       "      <td>29589.0</td>\n",
       "      <td>19680.0</td>\n",
       "      <td>29119.0</td>\n",
       "      <td>22838.0</td>\n",
       "      <td>14128.0</td>\n",
       "      <td>13319.0</td>\n",
       "      <td>11653.0</td>\n",
       "      <td>11010.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14056.0</td>\n",
       "      <td>10765.0</td>\n",
       "      <td>10922.0</td>\n",
       "      <td>14192.0</td>\n",
       "      <td>12702.0</td>\n",
       "      <td>14332.0</td>\n",
       "      <td>10820.0</td>\n",
       "      <td>9702.0</td>\n",
       "      <td>10018.0</td>\n",
       "      <td>7603.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>14963.0</td>\n",
       "      <td>28135.0</td>\n",
       "      <td>29843.0</td>\n",
       "      <td>19946.0</td>\n",
       "      <td>26658.0</td>\n",
       "      <td>21530.0</td>\n",
       "      <td>16355.0</td>\n",
       "      <td>14980.0</td>\n",
       "      <td>12855.0</td>\n",
       "      <td>15003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>16150.0</td>\n",
       "      <td>14328.0</td>\n",
       "      <td>11482.0</td>\n",
       "      <td>15481.0</td>\n",
       "      <td>11826.0</td>\n",
       "      <td>17038.0</td>\n",
       "      <td>14985.0</td>\n",
       "      <td>11837.0</td>\n",
       "      <td>11886.0</td>\n",
       "      <td>9240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>17787.0</td>\n",
       "      <td>33088.0</td>\n",
       "      <td>36658.0</td>\n",
       "      <td>20382.0</td>\n",
       "      <td>31152.0</td>\n",
       "      <td>25143.0</td>\n",
       "      <td>19279.0</td>\n",
       "      <td>19676.0</td>\n",
       "      <td>18300.0</td>\n",
       "      <td>19935.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21747.0</td>\n",
       "      <td>18679.0</td>\n",
       "      <td>17232.0</td>\n",
       "      <td>22093.0</td>\n",
       "      <td>14307.0</td>\n",
       "      <td>20801.0</td>\n",
       "      <td>18045.0</td>\n",
       "      <td>12547.0</td>\n",
       "      <td>14354.0</td>\n",
       "      <td>11586.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Province  Whole Kingdom  Greater Bangkok  Bangkok  Samut Prakan  Nonthaburi  \\\n",
       "2541            12492.0          24929.0  26054.0       18100.0     24211.0   \n",
       "2543            12150.0          25242.0  26909.0       15745.0     24566.0   \n",
       "2545            13736.0          28239.0  29589.0       19680.0     29119.0   \n",
       "2547            14963.0          28135.0  29843.0       19946.0     26658.0   \n",
       "2549            17787.0          33088.0  36658.0       20382.0     31152.0   \n",
       "\n",
       "Province  Pathum Thani  Central Region  Phra Nakhon Si Ayutthaya  Ang Thong  \\\n",
       "2541           21793.0         12643.0                   12918.0    10878.0   \n",
       "2543           19282.0         13012.0                   14904.0    12544.0   \n",
       "2545           22838.0         14128.0                   13319.0    11653.0   \n",
       "2547           21530.0         16355.0                   14980.0    12855.0   \n",
       "2549           25143.0         19279.0                   19676.0    18300.0   \n",
       "\n",
       "Province  Lop Buri     ...      Surat Thani   Ranong  Chumphon  Songkhla  \\\n",
       "2541       10587.0     ...          11967.0   9033.0   10232.0   15057.0   \n",
       "2543       10649.0     ...          10800.0   9410.0   10454.0   13505.0   \n",
       "2545       11010.0     ...          14056.0  10765.0   10922.0   14192.0   \n",
       "2547       15003.0     ...          16150.0  14328.0   11482.0   15481.0   \n",
       "2549       19935.0     ...          21747.0  18679.0   17232.0   22093.0   \n",
       "\n",
       "Province    Satun    Trang  Phattalung  Pattani     Yala  Narathiwat  \n",
       "2541       8924.0  12973.0     12315.0  10737.0   7560.0      6281.0  \n",
       "2543       9711.0  12782.0     11550.0   9759.0   9932.0      7802.0  \n",
       "2545      12702.0  14332.0     10820.0   9702.0  10018.0      7603.0  \n",
       "2547      11826.0  17038.0     14985.0  11837.0  11886.0      9240.0  \n",
       "2549      14307.0  20801.0     18045.0  12547.0  14354.0     11586.0  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "income = income.T\n",
    "income.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ถึงตรงนี้ หากเราอยากดูรายได้เฉลี่ยต่อครัวเรือนของประชากรทั้งประเทศในช่วงปี 2541 ถึง 2558 สามารถทำได้โดยการพลอตกราฟด้วย `plt` \n",
    "เช่นเดิม โดยใช้ index ของ `income` เป็นข้อมูลในแกน `x` และใช้ `income['Whole Kingdom']` เป็นข้อมูลในแกน `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Et99+m8rKSkpKSigrK2P58uUsXryYSCRCaWkpPp+PlStXkpeXR3FxMQBz586l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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3a5748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(income.index, income['Whole Kingdom'])\n",
    "plt.title('Average income per house')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สังเกตว่าแนวโน้มของรายได้เฉลี่ยนั้นดูเกือบจะเป็นเส้นตรง หากเราสมมติให้ข้อมูลดังกล่าวเรียงตัวเป็นเส้นตรงจริง ๆ\n",
    "เราสามารถหาเส้นตรงที่จะใช้เป็นแบบจำลองแทนข้อมูลชุดนี้ได้ด้วยโมดูล Scikit-Learn `sklearn`\n",
    "\n",
    "เราจะเริ่มจากการ import คลาสของแบบจำลองการเรียนรู้สำหรับสร้างเส้นตรงดังกล่าวที่ชื่อว่า Linear Regression\n",
    "และสร้างตัวแปร `model` เป็นตัวแปรของคลาสดังกล่าว"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "คราวนี้ เราจะให้ `model` ของเราเรียนรู้ที่จะสร้างเส้นตรงให้สอดคล้องกับข้อมูลที่มีมากที่สุด \n",
    "เราสามารถสั่งให้ `model` _ฟิต_ กับข้อมูลดังกล่าวได้ด้วยคำสั่ง `fit` กล่าวคือ `model` จะหาค่าพารามิเตอร์ `a` และ `b`\n",
    "เพื่อสร้างแบบจำลองเส้นตรงด้วยสมการ\n",
    "$$y = ax + b$$\n",
    "ให้ใกล้เคียงข้อมูลจริงมากที่สุด\n",
    "\n",
    "เนื่องจากคำสั่ง `fit` จะรับพารามิเตอร์สำคัญสองอย่าง ได้แก่ `X` ซึ่งเป็นข้อมูลตามแกน `x` ในรูปแบบ `np.array` สองมิติที่มีหนึ่ง column\n",
    "และ `y` ซึ่งเป็นข้อมูลตามแกน `y` ในรูปแบบ `np.array` หนึ่งมิติ\n",
    "ซึ่งเราต้องเตรียมข้อมูลจากตาราง `income` เสียก่อน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X = np.array(income.index).reshape(-1, 1) #แปลง income.index ให้เป็น np.array และปรับให้เป็น array สองมิติที่มี column เดียว\n",
    "y = income['Whole Kingdom'].values #เอาเฉพาะข้อมูลในตาราง income (ไม่เอาชื่อ index และ column) ซึ่งผลจะเป็น np.array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถดูลักษณะของข้อมูล `X` และ `y` ได้ดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X: [[2541]\n",
      " [2543]\n",
      " [2545]\n",
      " [2547]\n",
      " [2549]\n",
      " [2550]\n",
      " [2552]\n",
      " [2554]\n",
      " [2556]\n",
      " [2558]]\n",
      "y: [ 12492.  12150.  13736.  14963.  17787.  18660.  20904.  23236.  25194.\n",
      "  26915.]\n"
     ]
    }
   ],
   "source": [
    "print('X:', X)\n",
    "print('y:', y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ถึงตรงนี้ เราสั่งให้ `model` เรียนรู้จาก `X` และ `y` ได้ด้วยคำสั่ง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เพียงเท่านี้ เราก็ได้ `model` เป็นแบบจำลองเส้นตรงที่เป็นตัวแทนของรายได้เฉลี่ยปี 2541-2558 แล้ว\n",
    "เราสามารถพลอตเส้นตรงดังกล่าวเทียบกับรายได้จาก `income` ได้ดังนี้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sSUlJ2O123G53zT5utxuHw3HCcrfbTVxcXK3bxsXFnbbQsrKKs26usUlOdlBc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\n/C8tq+ORJ1u/qgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd869b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(income.index, income['Whole Kingdom'])\n",
    "plt.title('Average income per house')\n",
    "\n",
    "x_model = np.linspace(2541, 2558, 1000) #สร้าง np.array ที่มีสมาชิกเรียงตั้งแต่ 2541 จนถึง 2558 แบ่งออกเป็น 1000 ตัว\n",
    "y_model = model.predict(x_model.reshape(-1, 1)) #ทำนายค่า y จาก x แต่ละตัวใน x_model ด้วย model\n",
    "plt.plot(x_model, y_model, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถดู _คะแนน_ ความสอดคล้องของ `model` กับข้อมูลจริงของเราได้ด้วยคำสั่ง `model.score(X, y)`\n",
    "ซึ่งจะได้ค่าเป็น 1 ถ้าความสัมพันธ์ของ `X` และ `y` ตรงตามเส้นตรงที่ได้จาก `model` ทั้งหมด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.97456546784896025"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะเห็นว่าแบบจำลองเส้นตรงของเรานี้มีคะแนนสูงมากทีเดียว เราอาจนำไปทำนายรายได้เฉลี่ยต่อครัวเรือนของประชากรไทยได้ด้วยคำสั่ง"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 28468.88672566])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[2560]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะได้ว่าในปี 2560 รายได้เฉลี่ยต่อครัวเรือนของคนไทยทั้งประเทศ น่าจะมีค่าประมาณ 28468.89 บาท\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ปรับปรุงแบบจำลอง\n",
    "\n",
    "หากสังเกตให้ดี เราอาจมองว่าข้อมูลรายได้เฉลี่ยต่อครัวเรือนจากปี 2541-2558 นั้นไม่ได้เรียงเป็นเส้นตรง แต่มีความโค้งเล็กน้อย\n",
    "เราอาจนำสมมติฐานนี้มาปรับปรุงแบบจำลองของเราให้อยู่ในรูป _สมการพหุนามกำลังสอง_ แทน กล่าวคือ เราจะถือว่าถ้า $x$ เป็นปี\n",
    "และ $y$ เป็นรายได้เฉลี่ยของครัวเรือน ความสัมพันธ์ของ $x$ และ $y$ จะอยู่ในรูป\n",
    "$$ y = ax^2 + bx + c$$\n",
    "สำหรับค่าคงที่ $a,b,c$ บางตัว\n",
    "\n",
    "เราสามารถสร้างแบบจำลองดังกล่าวได้ด้วย Linear Regression เช่นเดียวกัน โดยมอง $x$ และ $x^2$ ให้เป็น _feature_\n",
    "ของปีแต่ละปี กล่าวคือถ้าเราให้ $f_1=x,f_2=x^2$ สิ่งที่เราต้องทำก็คือการหาค่าคงที่ $a,b,c$ ที่\n",
    "$$ y = af_2 + bf_1 + c$$\n",
    "สอดคล้องกับข้อมูลจริงมากที่สุด\n",
    "\n",
    "เราจะเริ่มด้วยการปรับข้อมูลปีใน `X` ให้มี feature เพิ่มขึ้นมา"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.array(income.index).reshape(-1, 1)\n",
    "\n",
    "# ปรับมิติของ X ให้มี 1 แถว\n",
    "X = X.T \n",
    "\n",
    "# สร้าง np.array อีกแถวโดยข้อมูลแต่ละตำแหน่งเป็นข้อมูลใน X ยกกำลังสอง และนำมาต่อด้านล่างของ X กลายเป็น array สองแถว\n",
    "X = np.vstack([X, X ** 2]) \n",
    "\n",
    "# transpose กลับให้แต่ละแถวเป็น feature ของปีใด ๆ\n",
    "X = X.T "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "เราสามารถเรียกดูมิติของ `X` ได้"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2541, 6456681],\n",
       "       [2543, 6466849],\n",
       "       [2545, 6477025],\n",
       "       [2547, 6487209],\n",
       "       [2549, 6497401],\n",
       "       [2550, 6502500],\n",
       "       [2552, 6512704],\n",
       "       [2554, 6522916],\n",
       "       [2556, 6533136],\n",
       "       [2558, 6543364]], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "คราวนี้เรามาลองสร้าง `model2` ให้เรียนรู้จากข้อมูล `X` ชุดใหม่นี้แทน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2 = LinearRegression()\n",
    "model2.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จากนั้น ลองพลอตเส้นแนวโน้มที่ได้ และแสดงคะแนน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EDZLmLYQQ9Yjm9GnCH38U/a6vcN0Rh/XNHDzX3+DtskQNk9XmQghRT+j27iay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\nMfmNZdhQ4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd6ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(income.index, income['Whole Kingdom'])\n",
    "plt.title('Average income per house')\n",
    "\n",
    "x_model = np.linspace(2541, 2558, 1000)\n",
    "X_model = np.vstack([x_model, x_model ** 2])\n",
    "y_model = model2.predict(X_model.T)\n",
    "plt.plot(x_model, y_model, color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9874884703063751"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2.score(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สังเกตว่าแบบจำลองใหม่นี้ให้คะแนนสูงว่าแบบจำลองแรก เรามาลองทำนายรายได้เฉลี่ยต่อครัวเรือนในปี 2560 ด้วยแบบจำลองใหม่กัน"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30260.36420834])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[2560, 2560 ** 2]])\n",
    "model2.predict(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "จะได้ว่ารายได้เฉลี่ยต่อเดือนต่อครัวเรือนในปี 2560 มีค่ามากกว่า 30000 บาท ขอแสดงความยินดีกับคนไทยทุกคน\n",
    "***"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}