BOT_REST_API_Demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url = \\\n",
    "    'https://iapi.bot.or.th/Stat/Stat-ReferenceRate/DAILY_REF_RATE_V1/'\n",
    "querystring = {'start_period': '2017-09-01', 'end_period': '2017-09-15'}\n",
    "headers = {'api-key': 'U9G1L457H6DCugT7VmBaEacbHV9RX0PySO05cYaGsm'}\n",
    "response = requests.request('GET', url, headers=headers,\n",
    "                            params=querystring)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"result\":{\"success\":\"true\",\"api\":\"Daily Weighted-average Interbank Exchange Rate - THB / USD\",\"timestamp\":\"2017-09-24 09:31:15\",\"data\":{\"data_header\":{\"report_name_eng\":\"Rates of Exchange of Commercial Banks in Bangkok Metropolis (2002-present)\",\"report_name_th\":\"อัตราแลกเปลี่ยนเฉลี่ยของธนาคารพาณิชย์ในกรุงเทพมหานคร (2545-ปัจจุบัน)\",\"report_uoq_name_eng\":\"(Unit : Baht / 1 Unit of Foreign Currency)\",\"report_uoq_name_th\":\"(หน่วย : บาท ต่อ 1 หน่วยเงินตราต่างประเทศ)\",\"report_source_of_data\":[{\"source_of_data_eng\":\"Bank of Thailand\",\"source_of_data_th\":\"ธนาคารแห่งประเทศไทย\"}],\"report_remark\":[],\"last_updated\":\"2017-09-22\"},\"data_detail\":[{\"period\":\"2017-09-15\",\"rate\":\"33.0670000\"},{\"period\":\"2017-09-14\",\"rate\":\"33.1190000\"},{\"period\":\"2017-09-13\",\"rate\":\"33.0870000\"},{\"period\":\"2017-09-12\",\"rate\":\"33.1170000\"},{\"period\":\"2017-09-11\",\"rate\":\"33.1280000\"},{\"period\":\"2017-09-08\",\"rate\":\"33.0680000\"},{\"period\":\"2017-09-07\",\"rate\":\"33.1260000\"},{\"period\":\"2017-09-06\",\"rate\":\"33.1650000\"},{\"period\":\"2017-09-05\",\"rate\":\"33.1730000\"},{\"period\":\"2017-09-04\",\"rate\":\"33.1820000\"},{\"period\":\"2017-09-01\",\"rate\":\"33.1830000\"}]}}}'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.text "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'dict'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'result': {'api': 'Daily Weighted-average Interbank Exchange Rate - THB / USD',\n",
       "  'data': {'data_detail': [{'period': '2017-09-15', 'rate': '33.0670000'},\n",
       "    {'period': '2017-09-14', 'rate': '33.1190000'},\n",
       "    {'period': '2017-09-13', 'rate': '33.0870000'},\n",
       "    {'period': '2017-09-12', 'rate': '33.1170000'},\n",
       "    {'period': '2017-09-11', 'rate': '33.1280000'},\n",
       "    {'period': '2017-09-08', 'rate': '33.0680000'},\n",
       "    {'period': '2017-09-07', 'rate': '33.1260000'},\n",
       "    {'period': '2017-09-06', 'rate': '33.1650000'},\n",
       "    {'period': '2017-09-05', 'rate': '33.1730000'},\n",
       "    {'period': '2017-09-04', 'rate': '33.1820000'},\n",
       "    {'period': '2017-09-01', 'rate': '33.1830000'}],\n",
       "   'data_header': {'last_updated': '2017-09-22',\n",
       "    'report_name_eng': 'Rates of Exchange of Commercial Banks in Bangkok Metropolis (2002-present)',\n",
       "    'report_name_th': 'อัตราแลกเปลี่ยนเฉลี่ยของธนาคารพาณิชย์ในกรุงเทพมหานคร (2545-ปัจจุบัน)',\n",
       "    'report_remark': [],\n",
       "    'report_source_of_data': [{'source_of_data_eng': 'Bank of Thailand',\n",
       "      'source_of_data_th': 'ธนาคารแห่งประเทศไทย'}],\n",
       "    'report_uoq_name_eng': '(Unit : Baht / 1 Unit of Foreign Currency)',\n",
       "    'report_uoq_name_th': '(หน่วย : บาท ต่อ 1 หน่วยเงินตราต่างประเทศ)'}},\n",
       "  'success': 'true',\n",
       "  'timestamp': '2017-09-24 09:31:15'}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j=pd.io.json.loads(response.text)\n",
    "print(type(j))\n",
    "j"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'period': '2017-09-15', 'rate': '33.0670000'},\n",
       " {'period': '2017-09-14', 'rate': '33.1190000'},\n",
       " {'period': '2017-09-13', 'rate': '33.0870000'},\n",
       " {'period': '2017-09-12', 'rate': '33.1170000'},\n",
       " {'period': '2017-09-11', 'rate': '33.1280000'},\n",
       " {'period': '2017-09-08', 'rate': '33.0680000'},\n",
       " {'period': '2017-09-07', 'rate': '33.1260000'},\n",
       " {'period': '2017-09-06', 'rate': '33.1650000'},\n",
       " {'period': '2017-09-05', 'rate': '33.1730000'},\n",
       " {'period': '2017-09-04', 'rate': '33.1820000'},\n",
       " {'period': '2017-09-01', 'rate': '33.1830000'}]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j['result']['data']['data_detail']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.DataFrame(j['result']['data']['data_detail'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>period</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-15</td>\n",
       "      <td>33.0670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-14</td>\n",
       "      <td>33.1190000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-13</td>\n",
       "      <td>33.0870000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-12</td>\n",
       "      <td>33.1170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-11</td>\n",
       "      <td>33.1280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-09-08</td>\n",
       "      <td>33.0680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-09-07</td>\n",
       "      <td>33.1260000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-09-06</td>\n",
       "      <td>33.1650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-09-05</td>\n",
       "      <td>33.1730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-09-04</td>\n",
       "      <td>33.1820000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>33.1830000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        period        rate\n",
       "0   2017-09-15  33.0670000\n",
       "1   2017-09-14  33.1190000\n",
       "2   2017-09-13  33.0870000\n",
       "3   2017-09-12  33.1170000\n",
       "4   2017-09-11  33.1280000\n",
       "5   2017-09-08  33.0680000\n",
       "6   2017-09-07  33.1260000\n",
       "7   2017-09-06  33.1650000\n",
       "8   2017-09-05  33.1730000\n",
       "9   2017-09-04  33.1820000\n",
       "10  2017-09-01  33.1830000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period    object\n",
       "rate      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period    datetime64[ns]\n",
       "rate             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.period=pd.to_datetime(df.period)\n",
    "df.rate=pd.to_numeric(df.rate)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('period', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-09-15</th>\n",
       "      <td>33.067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-14</th>\n",
       "      <td>33.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-13</th>\n",
       "      <td>33.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-12</th>\n",
       "      <td>33.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-11</th>\n",
       "      <td>33.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-08</th>\n",
       "      <td>33.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-07</th>\n",
       "      <td>33.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-06</th>\n",
       "      <td>33.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05</th>\n",
       "      <td>33.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-04</th>\n",
       "      <td>33.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>33.183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rate\n",
       "period            \n",
       "2017-09-15  33.067\n",
       "2017-09-14  33.119\n",
       "2017-09-13  33.087\n",
       "2017-09-12  33.117\n",
       "2017-09-11  33.128\n",
       "2017-09-08  33.068\n",
       "2017-09-07  33.126\n",
       "2017-09-06  33.165\n",
       "2017-09-05  33.173\n",
       "2017-09-04  33.182\n",
       "2017-09-01  33.183"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-09-01</th>\n",
       "      <td>33.183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-04</th>\n",
       "      <td>33.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-05</th>\n",
       "      <td>33.173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-06</th>\n",
       "      <td>33.165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-07</th>\n",
       "      <td>33.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-08</th>\n",
       "      <td>33.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-11</th>\n",
       "      <td>33.128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-12</th>\n",
       "      <td>33.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-13</th>\n",
       "      <td>33.087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-14</th>\n",
       "      <td>33.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-15</th>\n",
       "      <td>33.067</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              rate\n",
       "period            \n",
       "2017-09-01  33.183\n",
       "2017-09-04  33.182\n",
       "2017-09-05  33.173\n",
       "2017-09-06  33.165\n",
       "2017-09-07  33.126\n",
       "2017-09-08  33.068\n",
       "2017-09-11  33.128\n",
       "2017-09-12  33.117\n",
       "2017-09-13  33.087\n",
       "2017-09-14  33.119\n",
       "2017-09-15  33.067"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268f25a2cf8>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEhCAYAAACUW2yNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x268f1f0a898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}