government_open_data/Thailand_income.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Average Household Income in Thailand\n",
    "## Import required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import feather\n",
    "import re\n",
    "\n",
    "pd.options.display.max_columns = 999\n",
    "pd.options.display.max_rows = 999\n",
    "\n",
    "from IPython.core.display import display\n",
    "from requests import get\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load & Clean Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. ข้อมูลรายได้เฉลี่ยต่อครัวเรือนแต่ละจังหวัด"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ข้อมูลจาก https://data.go.th/default.aspx\n",
    "df = pd.read_excel('./data/รายได้เฉลี่ยต่อเดือนต่อครัวเรือน 41-58.xls')\n",
    "\n",
    "col_names = ['ภาค และจังหวัด'] + [int(y) for y in df.iloc[2,2:12].values.tolist()]\n",
    "\n",
    "df_cut = pd.DataFrame(df.iloc[5:88,[0,2,3,4,5,6,7,8,9,10,11]].values,columns=col_names)\n",
    "\n",
    "df_cut = df_cut[df_cut['ภาค และจังหวัด'].apply(lambda x: 'ภาค' not in x)]\n",
    "\n",
    "df_cut = df_cut.drop(index=[0,1]).reset_index(drop=True)\n",
    "\n",
    "df_cut['ภาค และจังหวัด']=df_cut['ภาค และจังหวัด'].apply(lambda x:re.sub(r'\\s+','',x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. ข้อมูล Lat Lon แต่ละจังหวัด\n",
    "จากเว็บไซต์ [https://tha.timegenie.com/latitude_longitude/country/th](https://tha.timegenie.com/latitude_longitude/country/th)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](docs/household-income-2.png) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87 87 87\n"
     ]
    }
   ],
   "source": [
    "url = 'https://tha.timegenie.com/latitude_longitude/country/th'\n",
    "\n",
    "response = get(url)\n",
    "\n",
    "html_soup = BeautifulSoup(response.text, 'html.parser')\n",
    "\n",
    "lat = html_soup.find_all('h5')\n",
    "lat = lat[:-11]\n",
    "\n",
    "lon = html_soup.find_all('span')\n",
    "lon = lon[1:-9]\n",
    "\n",
    "lat = [l.text for l in lat]\n",
    "lat = [l for l in lat if \"N\" not in l]\n",
    "\n",
    "lon = [l.text for l in lon]\n",
    "lon = [l for l in lon if \"E\" not in l]\n",
    "\n",
    "lat_num = [l for l in lat if re.search(r'\\d+.\\d+',l)]\n",
    "\n",
    "lon_num = [l for l in lon if re.search(r'\\d+.\\d+',l)]\n",
    "\n",
    "prov = [p for p in lat if not re.search(r'\\d+',p)][1:]\n",
    "\n",
    "print(len(lat_num), len(lon_num), len(prov))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.59556</td>\n",
       "      <td>100.60722</td>\n",
       "      <td>สมุทรปราการ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.53917</td>\n",
       "      <td>102.78444</td>\n",
       "      <td>อุดรธานี</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13.85083</td>\n",
       "      <td>100.52222</td>\n",
       "      <td>นนทบุรี</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.36222</td>\n",
       "      <td>100.98333</td>\n",
       "      <td>ชลบุรี</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat        lon       province\n",
       "0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "1  13.59556  100.60722    สมุทรปราการ\n",
       "2  17.53917  102.78444       อุดรธานี\n",
       "3  13.85083  100.52222        นนทบุรี\n",
       "4  13.36222  100.98333         ชลบุรี"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_coor = pd.DataFrame({'province':prov,'lat':lat_num,'lon':lon_num})\n",
    "df_coor.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Merge ข้อมูลจากข้อ 1 & 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>ภาค และจังหวัด</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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>26054</td>\n",
       "      <td>26909</td>\n",
       "      <td>29589</td>\n",
       "      <td>29843</td>\n",
       "      <td>36658</td>\n",
       "      <td>39020</td>\n",
       "      <td>42380</td>\n",
       "      <td>48951</td>\n",
       "      <td>49190.8</td>\n",
       "      <td>45571.7</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>สมุทรปราการ</td>\n",
       "      <td>18100</td>\n",
       "      <td>15745</td>\n",
       "      <td>19680</td>\n",
       "      <td>19946</td>\n",
       "      <td>20382</td>\n",
       "      <td>21302</td>\n",
       "      <td>23359</td>\n",
       "      <td>23797.9</td>\n",
       "      <td>29575.3</td>\n",
       "      <td>25457.2</td>\n",
       "      <td>13.59556</td>\n",
       "      <td>100.60722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>นนทบุรี</td>\n",
       "      <td>24211</td>\n",
       "      <td>24566</td>\n",
       "      <td>29119</td>\n",
       "      <td>26658</td>\n",
       "      <td>31152</td>\n",
       "      <td>32743</td>\n",
       "      <td>34626</td>\n",
       "      <td>35119.7</td>\n",
       "      <td>30663.6</td>\n",
       "      <td>36884</td>\n",
       "      <td>13.85083</td>\n",
       "      <td>100.52222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ปทุมธานี</td>\n",
       "      <td>21793</td>\n",
       "      <td>19282</td>\n",
       "      <td>22838</td>\n",
       "      <td>21530</td>\n",
       "      <td>25143</td>\n",
       "      <td>26107</td>\n",
       "      <td>26686</td>\n",
       "      <td>21615.5</td>\n",
       "      <td>33461.3</td>\n",
       "      <td>41056.9</td>\n",
       "      <td>14.05000</td>\n",
       "      <td>100.48333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>พระนครศรีอยุธยา</td>\n",
       "      <td>12918</td>\n",
       "      <td>14904</td>\n",
       "      <td>13319</td>\n",
       "      <td>14980</td>\n",
       "      <td>19676</td>\n",
       "      <td>21676</td>\n",
       "      <td>25820</td>\n",
       "      <td>22301.7</td>\n",
       "      <td>26481.5</td>\n",
       "      <td>28379.4</td>\n",
       "      <td>14.35361</td>\n",
       "      <td>100.56917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ภาค และจังหวัด   2541   2543   2545   2547   2549   2550   2552     2554  \\\n",
       "0    กรุงเทพมหานคร  26054  26909  29589  29843  36658  39020  42380    48951   \n",
       "1      สมุทรปราการ  18100  15745  19680  19946  20382  21302  23359  23797.9   \n",
       "2          นนทบุรี  24211  24566  29119  26658  31152  32743  34626  35119.7   \n",
       "3         ปทุมธานี  21793  19282  22838  21530  25143  26107  26686  21615.5   \n",
       "4  พระนครศรีอยุธยา  12918  14904  13319  14980  19676  21676  25820  22301.7   \n",
       "\n",
       "      2556     2558       lat        lon  \n",
       "0  49190.8  45571.7  13.72917  100.52389  \n",
       "1  29575.3  25457.2  13.59556  100.60722  \n",
       "2  30663.6    36884  13.85083  100.52222  \n",
       "3  33461.3  41056.9  14.05000  100.48333  \n",
       "4  26481.5  28379.4  14.35361  100.56917  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = df_cut.merge(df_coor,how='left',left_on='ภาค และจังหวัด',right_on='province')\n",
    "df_all.drop('province',axis=1,inplace=True)\n",
    "df_all.columns = [str(col) for col in df_all.columns]\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Save & Load from check point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>ภาค และจังหวัด</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>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>กรุงเทพมหานคร</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>49190.8</td>\n",
       "      <td>45571.7</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>สมุทรปราการ</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>23797.9</td>\n",
       "      <td>29575.3</td>\n",
       "      <td>25457.2</td>\n",
       "      <td>13.59556</td>\n",
       "      <td>100.60722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>นนทบุรี</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>35119.7</td>\n",
       "      <td>30663.6</td>\n",
       "      <td>36884.0</td>\n",
       "      <td>13.85083</td>\n",
       "      <td>100.52222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ปทุมธานี</td>\n",
       "      <td>21793.0</td>\n",
       "      <td>19282.0</td>\n",
       "      <td>22838.0</td>\n",
       "      <td>21530.0</td>\n",
       "      <td>25143.0</td>\n",
       "      <td>26107.0</td>\n",
       "      <td>26686.0</td>\n",
       "      <td>21615.5</td>\n",
       "      <td>33461.3</td>\n",
       "      <td>41056.9</td>\n",
       "      <td>14.05000</td>\n",
       "      <td>100.48333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>พระนครศรีอยุธยา</td>\n",
       "      <td>12918.0</td>\n",
       "      <td>14904.0</td>\n",
       "      <td>13319.0</td>\n",
       "      <td>14980.0</td>\n",
       "      <td>19676.0</td>\n",
       "      <td>21676.0</td>\n",
       "      <td>25820.0</td>\n",
       "      <td>22301.7</td>\n",
       "      <td>26481.5</td>\n",
       "      <td>28379.4</td>\n",
       "      <td>14.35361</td>\n",
       "      <td>100.56917</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ภาค และจังหวัด     2541     2543     2545     2547     2549     2550  \\\n",
       "0    กรุงเทพมหานคร  26054.0  26909.0  29589.0  29843.0  36658.0  39020.0   \n",
       "1      สมุทรปราการ  18100.0  15745.0  19680.0  19946.0  20382.0  21302.0   \n",
       "2          นนทบุรี  24211.0  24566.0  29119.0  26658.0  31152.0  32743.0   \n",
       "3         ปทุมธานี  21793.0  19282.0  22838.0  21530.0  25143.0  26107.0   \n",
       "4  พระนครศรีอยุธยา  12918.0  14904.0  13319.0  14980.0  19676.0  21676.0   \n",
       "\n",
       "      2552     2554     2556     2558       lat        lon  \n",
       "0  42380.0  48951.0  49190.8  45571.7  13.72917  100.52389  \n",
       "1  23359.0  23797.9  29575.3  25457.2  13.59556  100.60722  \n",
       "2  34626.0  35119.7  30663.6  36884.0  13.85083  100.52222  \n",
       "3  26686.0  21615.5  33461.3  41056.9  14.05000  100.48333  \n",
       "4  25820.0  22301.7  26481.5  28379.4  14.35361  100.56917  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df_all.to_feather('raw-df_all')\n",
    "\n",
    "# df_all = feather.read_dataframe('raw-df_all')\n",
    "# df_all.dropna(inplace=True)\n",
    "# df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. จัดรูปข้อมูลใหม่ ก่อนเข้า plotly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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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>inc</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>prov</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>26909.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>29589.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>29843.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>36658.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2550</th>\n",
       "      <td>39020.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2552</th>\n",
       "      <td>42380.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2554</th>\n",
       "      <td>48951.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2556</th>\n",
       "      <td>49190.8</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2558</th>\n",
       "      <td>45571.7</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          inc       lat        lon           prov\n",
       "2541  26054.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2543  26909.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2545  29589.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2547  29843.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2549  36658.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2550  39020.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2552  42380.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2554  48951.0  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2556  49190.8  13.72917  100.52389  กรุงเทพมหานคร\n",
       "2558  45571.7  13.72917  100.52389  กรุงเทพมหานคร"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = [2541, 2543, 2545, 2547, 2549, 2550, 2552, 2554, 2556, 2558]\n",
    "years = [str(y) for y in years]\n",
    "\n",
    "prov = []\n",
    "yr   = []\n",
    "inc  = []\n",
    "lat, lon = [], []\n",
    "for row in range(df_all.shape[0]):\n",
    "    for y in years:\n",
    "        prov.append(df_all.iloc[row]['ภาค และจังหวัด'])\n",
    "        yr.append(y)\n",
    "        lat.append(df_all.iloc[row]['lat'])\n",
    "        lon.append(df_all.iloc[row]['lon'])\n",
    "        inc.append(df_all.iloc[row][y])\n",
    "        \n",
    "df_all_1 = pd.DataFrame({'prov':prov,'lat':lat,'lon':lon,'inc':inc},index=yr)\n",
    "df_all_1.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. สร้าง scatter plot รายได้แต่ละจังหวัด ในแต่ละปี บนแผนที่ด้วย plotly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "สร้าง list ของแต่ละ legend (ใช้่ช่วงรายได้) และ แต่ละปี"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0 - 10000',\n",
       " '10000 - 20000',\n",
       " '20000 - 30000',\n",
       " '30000 - 40000',\n",
       " '40000 - 50000']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "years = [str(year) for year in [2541,2543,2545,2547,2549,2550,2552,2554,2556,2558]]\n",
    "\n",
    "bins = pd.IntervalIndex.from_tuples([(0,1e4), (1e4, 2e4), (2e4,3e4),(3e4,4e4),(4e4,5e4)])\n",
    "\n",
    "def get_income_range(x):\n",
    "    for income_bin in bins:\n",
    "        if x in income_bin:\n",
    "            return '{} - {}'.format(int(income_bin.left), int(income_bin.right))\n",
    "\n",
    "df_all_1['income_range'] = df_all_1['inc'].apply(get_income_range)\n",
    "\n",
    "income_range = sorted(df_all_1['income_range'].unique())\n",
    "income_range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>inc</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>prov</th>\n",
       "      <th>income_range</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2541</th>\n",
       "      <td>26054.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>20000 - 30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2543</th>\n",
       "      <td>26909.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>20000 - 30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2545</th>\n",
       "      <td>29589.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>20000 - 30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2547</th>\n",
       "      <td>29843.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>20000 - 30000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2549</th>\n",
       "      <td>36658.0</td>\n",
       "      <td>13.72917</td>\n",
       "      <td>100.52389</td>\n",
       "      <td>กรุงเทพมหานคร</td>\n",
       "      <td>30000 - 40000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          inc       lat        lon           prov   income_range\n",
       "2541  26054.0  13.72917  100.52389  กรุงเทพมหานคร  20000 - 30000\n",
       "2543  26909.0  13.72917  100.52389  กรุงเทพมหานคร  20000 - 30000\n",
       "2545  29589.0  13.72917  100.52389  กรุงเทพมหานคร  20000 - 30000\n",
       "2547  29843.0  13.72917  100.52389  กรุงเทพมหานคร  20000 - 30000\n",
       "2549  36658.0  13.72917  100.52389  กรุงเทพมหานคร  30000 - 40000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](docs/household-income.gif) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "        });</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make figure\n",
    "figure = {\n",
    "    'data': [],\n",
    "    'layout': {},\n",
    "    'frames': []\n",
    "}\n",
    "\n",
    "# fill in most of layout\n",
    "figure['layout']['title'] = \"Thailand's income by province\"\n",
    "figure['layout']['hovermode'] = 'closest'\n",
    "figure['layout']['geo'] = dict(\n",
    "    scope = 'asia',\n",
    "    showland = True,\n",
    "    landcolor = 'rgb(217, 217, 217)',\n",
    "    subunitwidth=1,\n",
    "    countrywidth=1,\n",
    "    subunitcolor=\"rgb(255, 255, 255)\",\n",
    "    countrycolor=\"rgb(255, 255, 255)\",\n",
    "    lonaxis = dict( range= [ 90.0, 110.0 ] ),\n",
    "    lataxis = dict( range= [ 5.0, 21.0 ] ),\n",
    ")\n",
    "figure['layout']['sliders'] = {\n",
    "    'args': [\n",
    "        'transition', {\n",
    "            'duration': 400,\n",
    "            'easing': 'cubic-in-out'\n",
    "        }\n",
    "    ],\n",
    "    'initialValue': '2541',\n",
    "    'plotlycommand': 'animate',\n",
    "    'values': years,\n",
    "    'visible': True\n",
    "}\n",
    "figure['layout']['updatemenus'] = [\n",
    "    {\n",
    "        'buttons': [\n",
    "            {\n",
    "                'args': [None, {'frame': {'duration': 500, 'redraw': False},\n",
    "                         'fromcurrent': True, 'transition': {'duration': 300, 'easing': 'quadratic-in-out'}}],\n",
    "                'label': 'Play',\n",
    "                'method': 'animate'\n",
    "            },\n",
    "            {\n",
    "                'args': [[None], {'frame': {'duration': 0, 'redraw': False}, 'mode': 'immediate',\n",
    "                'transition': {'duration': 0}}],\n",
    "                'label': 'Pause',\n",
    "                'method': 'animate'\n",
    "            }\n",
    "        ],\n",
    "        'direction': 'left',\n",
    "        'pad': {'r': 10, 't': 87},\n",
    "        'showactive': False,\n",
    "        'type': 'buttons',\n",
    "        'x': 0.1,\n",
    "        'xanchor': 'right',\n",
    "        'y': 0,\n",
    "        'yanchor': 'top'\n",
    "    }\n",
    "]\n",
    "\n",
    "sliders_dict = {\n",
    "    'active': 0,\n",
    "    'yanchor': 'top',\n",
    "    'xanchor': 'left',\n",
    "    'currentvalue': {\n",
    "        'font': {'size': 20},\n",
    "        'prefix': 'Year:',\n",
    "        'visible': True,\n",
    "        'xanchor': 'right'\n",
    "    },\n",
    "    'transition': {'duration': 300, 'easing': 'cubic-in-out'},\n",
    "    'pad': {'b': 10, 't': 50},\n",
    "    'len': 0.9,\n",
    "    'x': 0.1,\n",
    "    'y': 0,\n",
    "    'steps': []\n",
    "}\n",
    "\n",
    "# make data\n",
    "year = '2541'\n",
    "for ra in income_range:\n",
    "    dataset_by_year = df_all_1.loc[year]\n",
    "    dataset_by_year_and_ra = dataset_by_year[dataset_by_year['income_range'] == ra]\n",
    "\n",
    "    data_dict = {\n",
    "        'type':'scattergeo',\n",
    "        'locationmode' : 'country names',\n",
    "        'lon': list(dataset_by_year_and_ra['lon']),\n",
    "        'lat': list(dataset_by_year_and_ra['lat']),\n",
    "        'mode': 'markers',\n",
    "        'text': list(dataset_by_year_and_ra['prov']),\n",
    "        'marker': {\n",
    "            'sizemode': 'area',\n",
    "            'size': list(dataset_by_year_and_ra['inc']/1e3*3)\n",
    "        },\n",
    "        'name': ra\n",
    "    }\n",
    "    figure['data'].append(data_dict)\n",
    "\n",
    "# make frames\n",
    "for year in years:\n",
    "    frame = {'data': [], 'name': str(year)}\n",
    "    for ra in income_range:\n",
    "        dataset_by_year = df_all_1.loc[year]\n",
    "        dataset_by_year_and_ra = dataset_by_year[dataset_by_year['income_range'] == ra]\n",
    "\n",
    "        data_dict = {\n",
    "            'type':'scattergeo',\n",
    "            'lon': list(dataset_by_year_and_ra['lon']),\n",
    "            'lat': list(dataset_by_year_and_ra['lat']),\n",
    "            'mode': 'markers',\n",
    "            'text': list(dataset_by_year_and_ra['prov']),\n",
    "            'marker': {\n",
    "                'sizemode': 'area',\n",
    "                'size': list(dataset_by_year_and_ra['inc']/1e3*3)\n",
    "            },\n",
    "            'name': ra\n",
    "        }\n",
    "        frame['data'].append(data_dict)\n",
    "\n",
    "    figure['frames'].append(frame)\n",
    "    \n",
    "    slider_step = {'args': [\n",
    "        [year],\n",
    "        {'frame': {'duration': 300, 'redraw': False},\n",
    "         'mode': 'immediate',\n",
    "       'transition': {'duration': 300}}\n",
    "     ],\n",
    "     'label': year,\n",
    "     'method': 'animate'}\n",
    "    sliders_dict['steps'].append(slider_step)\n",
    "\n",
    "    \n",
    "figure['layout']['sliders'] = [sliders_dict]\n",
    "\n",
    "iplot(figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. ลอง visualize mean & standard deviation ของรายได้แต่ละปี"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = []\n",
    "std = []\n",
    "for year in years:\n",
    "    mean.append(np.mean(df_all_1.loc[year]['inc']))\n",
    "    std.append(np.std(df_all_1.loc[year]['inc']))\n",
    "\n",
    "df = pd.DataFrame({'year':years,'mean income':mean,'std dev':std})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](docs/household-income-3.png) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~pcrete_poom/130.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace1 = go.Bar(\n",
    "    x=df['year'],\n",
    "    y=df['mean income'],\n",
    "#     name='Control',\n",
    "    error_y=dict(\n",
    "        type='data',\n",
    "        array=df['std dev'],\n",
    "        visible=True\n",
    "    )\n",
    ")\n",
    "data = [trace1]\n",
    "layout = go.Layout(\n",
    "    title = \"Thailand's mean income by year\",\n",
    "    barmode='group'\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='error-bar-bar')"
   ]
  }
 ],
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