tak-akashi/FX Index Calculation & Visualization.ipynb

## FX Index Calculation & Visualization.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import zipfile, io\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://www.bis.org/statistics/full_webstats_eer_d_dataflow_csv_row.zip'\n",
    "\n",
    "res = requests.get(url)\n",
    "z = zipfile.ZipFile(io.BytesIO(res.content))\n",
    "z.extractall()\n",
    "\n",
    "os.rename('WEBSTATS_EER_D_DATAFLOW_csv_row.csv', 'BIS/WEBSTATS_EER_D_DATAFLOW_csv_row.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('BIS/WEBSTATS_EER_D_DATAFLOW_csv_row.csv', skiprows=[0,1,2,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_broad = df.iloc[:, :62].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reference area</th>\n",
       "      <th>AE:United Arab Emirates</th>\n",
       "      <th>AR:Argentina</th>\n",
       "      <th>AT:Austria</th>\n",
       "      <th>AU:Australia</th>\n",
       "      <th>BE:Belgium</th>\n",
       "      <th>BG:Bulgaria</th>\n",
       "      <th>BR:Brazil</th>\n",
       "      <th>CA:Canada</th>\n",
       "      <th>CH:Switzerland</th>\n",
       "      <th>...</th>\n",
       "      <th>SG:Singapore</th>\n",
       "      <th>SI:Slovenia</th>\n",
       "      <th>SK:Slovak Republic</th>\n",
       "      <th>TH:Thailand</th>\n",
       "      <th>TR:Turkey</th>\n",
       "      <th>TW:Chinese Taipei</th>\n",
       "      <th>US:United States</th>\n",
       "      <th>VE:Venezuela</th>\n",
       "      <th>XM:Euro area</th>\n",
       "      <th>ZA:South Africa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4574</th>\n",
       "      <td>1996-04-11</td>\n",
       "      <td>92.09</td>\n",
       "      <td>329.74</td>\n",
       "      <td>96.36</td>\n",
       "      <td>86.82</td>\n",
       "      <td>97.12</td>\n",
       "      <td>1432.38</td>\n",
       "      <td>151.97</td>\n",
       "      <td>75.27</td>\n",
       "      <td>83.75</td>\n",
       "      <td>...</td>\n",
       "      <td>90.30</td>\n",
       "      <td>130.17</td>\n",
       "      <td>72.56</td>\n",
       "      <td>125.61</td>\n",
       "      <td>1954.80</td>\n",
       "      <td>116.88</td>\n",
       "      <td>95.57</td>\n",
       "      <td>1309.65</td>\n",
       "      <td>84.61</td>\n",
       "      <td>179.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4575</th>\n",
       "      <td>1996-04-12</td>\n",
       "      <td>92.06</td>\n",
       "      <td>329.47</td>\n",
       "      <td>96.37</td>\n",
       "      <td>86.87</td>\n",
       "      <td>97.14</td>\n",
       "      <td>1431.58</td>\n",
       "      <td>152.20</td>\n",
       "      <td>75.24</td>\n",
       "      <td>83.55</td>\n",
       "      <td>...</td>\n",
       "      <td>90.03</td>\n",
       "      <td>129.54</td>\n",
       "      <td>72.50</td>\n",
       "      <td>125.67</td>\n",
       "      <td>1954.29</td>\n",
       "      <td>116.84</td>\n",
       "      <td>95.49</td>\n",
       "      <td>1309.07</td>\n",
       "      <td>84.63</td>\n",
       "      <td>178.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4578</th>\n",
       "      <td>1996-04-15</td>\n",
       "      <td>92.28</td>\n",
       "      <td>329.96</td>\n",
       "      <td>96.20</td>\n",
       "      <td>87.11</td>\n",
       "      <td>96.95</td>\n",
       "      <td>1437.28</td>\n",
       "      <td>152.51</td>\n",
       "      <td>75.30</td>\n",
       "      <td>83.18</td>\n",
       "      <td>...</td>\n",
       "      <td>90.10</td>\n",
       "      <td>130.32</td>\n",
       "      <td>72.86</td>\n",
       "      <td>125.63</td>\n",
       "      <td>1953.23</td>\n",
       "      <td>117.04</td>\n",
       "      <td>95.64</td>\n",
       "      <td>1310.83</td>\n",
       "      <td>84.38</td>\n",
       "      <td>177.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4579</th>\n",
       "      <td>1996-04-16</td>\n",
       "      <td>92.21</td>\n",
       "      <td>329.81</td>\n",
       "      <td>96.31</td>\n",
       "      <td>86.68</td>\n",
       "      <td>97.04</td>\n",
       "      <td>1432.27</td>\n",
       "      <td>152.38</td>\n",
       "      <td>75.13</td>\n",
       "      <td>83.30</td>\n",
       "      <td>...</td>\n",
       "      <td>90.05</td>\n",
       "      <td>129.55</td>\n",
       "      <td>72.49</td>\n",
       "      <td>125.58</td>\n",
       "      <td>1936.91</td>\n",
       "      <td>116.96</td>\n",
       "      <td>95.55</td>\n",
       "      <td>1310.50</td>\n",
       "      <td>84.46</td>\n",
       "      <td>175.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4580</th>\n",
       "      <td>1996-04-17</td>\n",
       "      <td>92.20</td>\n",
       "      <td>329.74</td>\n",
       "      <td>96.32</td>\n",
       "      <td>86.36</td>\n",
       "      <td>97.05</td>\n",
       "      <td>1419.98</td>\n",
       "      <td>152.49</td>\n",
       "      <td>75.13</td>\n",
       "      <td>83.39</td>\n",
       "      <td>...</td>\n",
       "      <td>90.16</td>\n",
       "      <td>129.69</td>\n",
       "      <td>72.63</td>\n",
       "      <td>125.51</td>\n",
       "      <td>1936.94</td>\n",
       "      <td>116.96</td>\n",
       "      <td>95.54</td>\n",
       "      <td>1310.28</td>\n",
       "      <td>84.47</td>\n",
       "      <td>175.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Reference area  AE:United Arab Emirates  AR:Argentina  AT:Austria  \\\n",
       "4574     1996-04-11                    92.09        329.74       96.36   \n",
       "4575     1996-04-12                    92.06        329.47       96.37   \n",
       "4578     1996-04-15                    92.28        329.96       96.20   \n",
       "4579     1996-04-16                    92.21        329.81       96.31   \n",
       "4580     1996-04-17                    92.20        329.74       96.32   \n",
       "\n",
       "      AU:Australia  BE:Belgium  BG:Bulgaria  BR:Brazil  CA:Canada  \\\n",
       "4574         86.82       97.12      1432.38     151.97      75.27   \n",
       "4575         86.87       97.14      1431.58     152.20      75.24   \n",
       "4578         87.11       96.95      1437.28     152.51      75.30   \n",
       "4579         86.68       97.04      1432.27     152.38      75.13   \n",
       "4580         86.36       97.05      1419.98     152.49      75.13   \n",
       "\n",
       "      CH:Switzerland       ...         SG:Singapore  SI:Slovenia  \\\n",
       "4574           83.75       ...                90.30       130.17   \n",
       "4575           83.55       ...                90.03       129.54   \n",
       "4578           83.18       ...                90.10       130.32   \n",
       "4579           83.30       ...                90.05       129.55   \n",
       "4580           83.39       ...                90.16       129.69   \n",
       "\n",
       "      SK:Slovak Republic  TH:Thailand  TR:Turkey  TW:Chinese Taipei  \\\n",
       "4574               72.56       125.61    1954.80             116.88   \n",
       "4575               72.50       125.67    1954.29             116.84   \n",
       "4578               72.86       125.63    1953.23             117.04   \n",
       "4579               72.49       125.58    1936.91             116.96   \n",
       "4580               72.63       125.51    1936.94             116.96   \n",
       "\n",
       "      US:United States  VE:Venezuela  XM:Euro area  ZA:South Africa  \n",
       "4574             95.57       1309.65         84.61           179.53  \n",
       "4575             95.49       1309.07         84.63           178.82  \n",
       "4578             95.64       1310.83         84.38           177.16  \n",
       "4579             95.55       1310.50         84.46           175.58  \n",
       "4580             95.54       1310.28         84.47           175.80  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fx_broad.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Reference area'] = pd.to_datetime(df['Reference area'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_broad.set_index(keys='Reference area', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_broad.index.name = 'Date'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_index = fx_broad / fx_broad.loc['2008-01-01',:] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_fx_list = ['AE:United Arab Emirates','AU:Australia','BR:Brazil','CA:Canada','CH:Switzerland',\n",
    "               'CN:China','DK:Denmark','GB:United Kingdom','HK:Hong Kong SAR',\n",
    "               'ID:Indonesia','IN:India','JP:Japan','KR:Korea','MX:Mexico',\n",
    "               'MY:Malaysia','NO:Norway','NZ:New Zealand','RU:Russia','SA:Saudi Arabia',\n",
    "               'SE:Sweden','SG:Singapore','TH:Thailand','TW:Chinese Taipei',\n",
    "                'US:United States','XM:Euro area']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "fx_index_main = fx_index[main_fx_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "wk_chg = fx_index_main.pct_change(periods=5).iloc[-1]* 100\n",
    "hy_chg = fx_index_main.pct_change(periods=130).iloc[-1] * 100\n",
    "y2_chg = fx_index_main.pct_change(periods=520).iloc[-1] * 100\n",
    "y5_chg = fx_index_main.pct_change(periods=1300).iloc[-1] * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "table = pd.DataFrame([fx_index_main.iloc[-1], wk_chg, hy_chg, y2_chg, y5_chg], \n",
    "                     index=['FX_index', 'weekly-change', 'half-year-change', \n",
    "                            '2-year-change', '5-year-change'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "table_T = table.T.sort_values(by='weekly-change', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FX_index</th>\n",
       "      <th>weekly-change</th>\n",
       "      <th>half-year-change</th>\n",
       "      <th>2-year-change</th>\n",
       "      <th>5-year-change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MX:Mexico</th>\n",
       "      <td>63.800748</td>\n",
       "      <td>1.213626</td>\n",
       "      <td>3.937119</td>\n",
       "      <td>0.027259</td>\n",
       "      <td>-27.083954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IN:India</th>\n",
       "      <td>66.202554</td>\n",
       "      <td>0.864748</td>\n",
       "      <td>-2.392782</td>\n",
       "      <td>-1.633944</td>\n",
       "      <td>-1.608014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR:Brazil</th>\n",
       "      <td>70.840825</td>\n",
       "      <td>0.464977</td>\n",
       "      <td>-3.473123</td>\n",
       "      <td>-3.445293</td>\n",
       "      <td>-17.247344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RU:Russia</th>\n",
       "      <td>47.965611</td>\n",
       "      <td>0.409836</td>\n",
       "      <td>-4.410517</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>-40.072317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA:Saudi Arabia</th>\n",
       "      <td>116.812609</td>\n",
       "      <td>0.407332</td>\n",
       "      <td>4.257080</td>\n",
       "      <td>0.478511</td>\n",
       "      <td>12.792201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NZ:New Zealand</th>\n",
       "      <td>97.526567</td>\n",
       "      <td>0.367682</td>\n",
       "      <td>-2.258538</td>\n",
       "      <td>-3.041894</td>\n",
       "      <td>-2.900401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HK:Hong Kong SAR</th>\n",
       "      <td>106.589147</td>\n",
       "      <td>0.346183</td>\n",
       "      <td>3.254068</td>\n",
       "      <td>-1.142962</td>\n",
       "      <td>8.409987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID:Indonesia</th>\n",
       "      <td>71.192253</td>\n",
       "      <td>0.284172</td>\n",
       "      <td>-3.552883</td>\n",
       "      <td>-8.290021</td>\n",
       "      <td>-20.293619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XM:Euro area</th>\n",
       "      <td>101.445004</td>\n",
       "      <td>0.272505</td>\n",
       "      <td>2.017208</td>\n",
       "      <td>8.743504</td>\n",
       "      <td>6.518267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TH:Thailand</th>\n",
       "      <td>113.453007</td>\n",
       "      <td>0.246126</td>\n",
       "      <td>0.054590</td>\n",
       "      <td>6.943499</td>\n",
       "      <td>5.944123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AE:United Arab Emirates</th>\n",
       "      <td>124.620893</td>\n",
       "      <td>0.237168</td>\n",
       "      <td>4.744203</td>\n",
       "      <td>0.390227</td>\n",
       "      <td>13.537369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US:United States</th>\n",
       "      <td>127.789214</td>\n",
       "      <td>0.226025</td>\n",
       "      <td>7.890163</td>\n",
       "      <td>3.778001</td>\n",
       "      <td>24.309171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DK:Denmark</th>\n",
       "      <td>101.559588</td>\n",
       "      <td>0.203232</td>\n",
       "      <td>1.063934</td>\n",
       "      <td>4.217413</td>\n",
       "      <td>4.776361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH:Switzerland</th>\n",
       "      <td>141.092388</td>\n",
       "      <td>0.140218</td>\n",
       "      <td>1.948106</td>\n",
       "      <td>-2.513249</td>\n",
       "      <td>8.556867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TW:Chinese Taipei</th>\n",
       "      <td>114.616845</td>\n",
       "      <td>0.043603</td>\n",
       "      <td>0.578643</td>\n",
       "      <td>5.927978</td>\n",
       "      <td>9.580667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SG:Singapore</th>\n",
       "      <td>118.337537</td>\n",
       "      <td>0.035533</td>\n",
       "      <td>0.923104</td>\n",
       "      <td>-0.115310</td>\n",
       "      <td>4.646408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MY:Malaysia</th>\n",
       "      <td>89.186458</td>\n",
       "      <td>0.011333</td>\n",
       "      <td>1.987750</td>\n",
       "      <td>-0.898372</td>\n",
       "      <td>-12.666997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA:Canada</th>\n",
       "      <td>80.451923</td>\n",
       "      <td>-0.047784</td>\n",
       "      <td>-3.517066</td>\n",
       "      <td>-0.664846</td>\n",
       "      <td>-15.604196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AU:Australia</th>\n",
       "      <td>92.314911</td>\n",
       "      <td>-0.112829</td>\n",
       "      <td>-3.414794</td>\n",
       "      <td>-1.326349</td>\n",
       "      <td>-10.774037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GB:United Kingdom</th>\n",
       "      <td>80.069959</td>\n",
       "      <td>-0.273556</td>\n",
       "      <td>0.582465</td>\n",
       "      <td>-1.144923</td>\n",
       "      <td>-1.343089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE:Sweden</th>\n",
       "      <td>92.964435</td>\n",
       "      <td>-0.384216</td>\n",
       "      <td>-4.022011</td>\n",
       "      <td>-4.375997</td>\n",
       "      <td>-14.218009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KR:Korea</th>\n",
       "      <td>91.545304</td>\n",
       "      <td>-0.462210</td>\n",
       "      <td>-1.549272</td>\n",
       "      <td>2.304918</td>\n",
       "      <td>12.859084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN:China</th>\n",
       "      <td>133.248759</td>\n",
       "      <td>-0.469677</td>\n",
       "      <td>2.130718</td>\n",
       "      <td>2.425167</td>\n",
       "      <td>6.629590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NO:Norway</th>\n",
       "      <td>83.325240</td>\n",
       "      <td>-0.533272</td>\n",
       "      <td>2.411077</td>\n",
       "      <td>2.853033</td>\n",
       "      <td>-14.830256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP:Japan</th>\n",
       "      <td>110.610010</td>\n",
       "      <td>-0.852903</td>\n",
       "      <td>2.611850</td>\n",
       "      <td>-5.732060</td>\n",
       "      <td>-0.445800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           FX_index  weekly-change  half-year-change  \\\n",
       "MX:Mexico                 63.800748       1.213626          3.937119   \n",
       "IN:India                  66.202554       0.864748         -2.392782   \n",
       "BR:Brazil                 70.840825       0.464977         -3.473123   \n",
       "RU:Russia                 47.965611       0.409836         -4.410517   \n",
       "SA:Saudi Arabia          116.812609       0.407332          4.257080   \n",
       "NZ:New Zealand            97.526567       0.367682         -2.258538   \n",
       "HK:Hong Kong SAR         106.589147       0.346183          3.254068   \n",
       "ID:Indonesia              71.192253       0.284172         -3.552883   \n",
       "XM:Euro area             101.445004       0.272505          2.017208   \n",
       "TH:Thailand              113.453007       0.246126          0.054590   \n",
       "AE:United Arab Emirates  124.620893       0.237168          4.744203   \n",
       "US:United States         127.789214       0.226025          7.890163   \n",
       "DK:Denmark               101.559588       0.203232          1.063934   \n",
       "CH:Switzerland           141.092388       0.140218          1.948106   \n",
       "TW:Chinese Taipei        114.616845       0.043603          0.578643   \n",
       "SG:Singapore             118.337537       0.035533          0.923104   \n",
       "MY:Malaysia               89.186458       0.011333          1.987750   \n",
       "CA:Canada                 80.451923      -0.047784         -3.517066   \n",
       "AU:Australia              92.314911      -0.112829         -3.414794   \n",
       "GB:United Kingdom         80.069959      -0.273556          0.582465   \n",
       "SE:Sweden                 92.964435      -0.384216         -4.022011   \n",
       "KR:Korea                  91.545304      -0.462210         -1.549272   \n",
       "CN:China                 133.248759      -0.469677          2.130718   \n",
       "NO:Norway                 83.325240      -0.533272          2.411077   \n",
       "JP:Japan                 110.610010      -0.852903          2.611850   \n",
       "\n",
       "                         2-year-change  5-year-change  \n",
       "MX:Mexico                     0.027259     -27.083954  \n",
       "IN:India                     -1.633944      -1.608014  \n",
       "BR:Brazil                    -3.445293     -17.247344  \n",
       "RU:Russia                     0.625000     -40.072317  \n",
       "SA:Saudi Arabia               0.478511      12.792201  \n",
       "NZ:New Zealand               -3.041894      -2.900401  \n",
       "HK:Hong Kong SAR             -1.142962       8.409987  \n",
       "ID:Indonesia                 -8.290021     -20.293619  \n",
       "XM:Euro area                  8.743504       6.518267  \n",
       "TH:Thailand                   6.943499       5.944123  \n",
       "AE:United Arab Emirates       0.390227      13.537369  \n",
       "US:United States              3.778001      24.309171  \n",
       "DK:Denmark                    4.217413       4.776361  \n",
       "CH:Switzerland               -2.513249       8.556867  \n",
       "TW:Chinese Taipei             5.927978       9.580667  \n",
       "SG:Singapore                 -0.115310       4.646408  \n",
       "MY:Malaysia                  -0.898372     -12.666997  \n",
       "CA:Canada                    -0.664846     -15.604196  \n",
       "AU:Australia                 -1.326349     -10.774037  \n",
       "GB:United Kingdom            -1.144923      -1.343089  \n",
       "SE:Sweden                    -4.375997     -14.218009  \n",
       "KR:Korea                      2.304918      12.859084  \n",
       "CN:China                      2.425167       6.629590  \n",
       "NO:Norway                     2.853033     -14.830256  \n",
       "JP:Japan                     -5.732060      -0.445800  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1283fea58>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_T.sort_values(by='5-year-change', \n",
    "                    ascending=True).drop('FX_index',axis=1).plot(kind='barh',fontsize=12,\n",
    "                                                                 figsize=(8,20), colormap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}