Storytelling - Data Visualization of Exchange Rates.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title\n",
    "\n",
    "- Things\n",
    "- Stuff\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Period\\Unit:</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
       "      <th>[Swiss franc ]</th>\n",
       "      <th>[Chinese yuan renminbi ]</th>\n",
       "      <th>[Cypriot pound ]</th>\n",
       "      <th>[Czech koruna ]</th>\n",
       "      <th>[Danish krone ]</th>\n",
       "      <th>...</th>\n",
       "      <th>[Romanian leu ]</th>\n",
       "      <th>[Russian rouble ]</th>\n",
       "      <th>[Swedish krona ]</th>\n",
       "      <th>[Singapore dollar ]</th>\n",
       "      <th>[Slovenian tolar ]</th>\n",
       "      <th>[Slovak koruna ]</th>\n",
       "      <th>[Thai baht ]</th>\n",
       "      <th>[Turkish lira ]</th>\n",
       "      <th>[US dollar ]</th>\n",
       "      <th>[South African rand ]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.5758</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5748</td>\n",
       "      <td>1.5543</td>\n",
       "      <td>1.0827</td>\n",
       "      <td>7.9184</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.163</td>\n",
       "      <td>7.4369</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8708</td>\n",
       "      <td>90.8000</td>\n",
       "      <td>10.0510</td>\n",
       "      <td>1.6228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8480</td>\n",
       "      <td>9.0146</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>18.7212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.5836</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5172</td>\n",
       "      <td>1.5601</td>\n",
       "      <td>1.0833</td>\n",
       "      <td>7.9392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.147</td>\n",
       "      <td>7.4392</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8712</td>\n",
       "      <td>91.2000</td>\n",
       "      <td>10.0575</td>\n",
       "      <td>1.6253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.8590</td>\n",
       "      <td>8.9987</td>\n",
       "      <td>1.2276</td>\n",
       "      <td>18.7919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.5824</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5119</td>\n",
       "      <td>1.5640</td>\n",
       "      <td>1.0821</td>\n",
       "      <td>7.9653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.145</td>\n",
       "      <td>7.4393</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8720</td>\n",
       "      <td>90.8175</td>\n",
       "      <td>10.0653</td>\n",
       "      <td>1.6246</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.9210</td>\n",
       "      <td>9.0554</td>\n",
       "      <td>1.2338</td>\n",
       "      <td>18.5123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.5927</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.5517</td>\n",
       "      <td>1.5651</td>\n",
       "      <td>1.0803</td>\n",
       "      <td>7.9315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.227</td>\n",
       "      <td>7.4387</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8721</td>\n",
       "      <td>91.6715</td>\n",
       "      <td>10.0570</td>\n",
       "      <td>1.6180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.7760</td>\n",
       "      <td>9.0694</td>\n",
       "      <td>1.2271</td>\n",
       "      <td>18.4194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1.5928</td>\n",
       "      <td>1.9558</td>\n",
       "      <td>6.3241</td>\n",
       "      <td>1.5621</td>\n",
       "      <td>1.0811</td>\n",
       "      <td>7.9484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.141</td>\n",
       "      <td>7.4379</td>\n",
       "      <td>...</td>\n",
       "      <td>4.8713</td>\n",
       "      <td>90.3420</td>\n",
       "      <td>10.0895</td>\n",
       "      <td>1.6198</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36.7280</td>\n",
       "      <td>9.0579</td>\n",
       "      <td>1.2296</td>\n",
       "      <td>17.9214</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Period\\Unit: [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "0   2021-01-08               1.5758           1.9558            6.5748   \n",
       "1   2021-01-07               1.5836           1.9558            6.5172   \n",
       "2   2021-01-06               1.5824           1.9558            6.5119   \n",
       "3   2021-01-05               1.5927           1.9558            6.5517   \n",
       "4   2021-01-04               1.5928           1.9558            6.3241   \n",
       "\n",
       "  [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ] [Cypriot pound ]  \\\n",
       "0             1.5543         1.0827                   7.9184              NaN   \n",
       "1             1.5601         1.0833                   7.9392              NaN   \n",
       "2             1.5640         1.0821                   7.9653              NaN   \n",
       "3             1.5651         1.0803                   7.9315              NaN   \n",
       "4             1.5621         1.0811                   7.9484              NaN   \n",
       "\n",
       "  [Czech koruna ] [Danish krone ]  ... [Romanian leu ] [Russian rouble ]  \\\n",
       "0          26.163          7.4369  ...          4.8708           90.8000   \n",
       "1          26.147          7.4392  ...          4.8712           91.2000   \n",
       "2          26.145          7.4393  ...          4.8720           90.8175   \n",
       "3          26.227          7.4387  ...          4.8721           91.6715   \n",
       "4          26.141          7.4379  ...          4.8713           90.3420   \n",
       "\n",
       "  [Swedish krona ] [Singapore dollar ] [Slovenian tolar ] [Slovak koruna ]  \\\n",
       "0          10.0510              1.6228                NaN              NaN   \n",
       "1          10.0575              1.6253                NaN              NaN   \n",
       "2          10.0653              1.6246                NaN              NaN   \n",
       "3          10.0570              1.6180                NaN              NaN   \n",
       "4          10.0895              1.6198                NaN              NaN   \n",
       "\n",
       "  [Thai baht ] [Turkish lira ] [US dollar ]  [South African rand ]  \n",
       "0      36.8480          9.0146       1.2250                18.7212  \n",
       "1      36.8590          8.9987       1.2276                18.7919  \n",
       "2      36.9210          9.0554       1.2338                18.5123  \n",
       "3      36.7760          9.0694       1.2271                18.4194  \n",
       "4      36.7280          9.0579       1.2296                17.9214  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "exchange_rates = pd.read_csv('euro-daily-hist_1999_2020.csv')\n",
    "\n",
    "exchange_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Period\\Unit:</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
       "      <th>[Swiss franc ]</th>\n",
       "      <th>[Chinese yuan renminbi ]</th>\n",
       "      <th>[Cypriot pound ]</th>\n",
       "      <th>[Czech koruna ]</th>\n",
       "      <th>[Danish krone ]</th>\n",
       "      <th>...</th>\n",
       "      <th>[Romanian leu ]</th>\n",
       "      <th>[Russian rouble ]</th>\n",
       "      <th>[Swedish krona ]</th>\n",
       "      <th>[Singapore dollar ]</th>\n",
       "      <th>[Slovenian tolar ]</th>\n",
       "      <th>[Slovak koruna ]</th>\n",
       "      <th>[Thai baht ]</th>\n",
       "      <th>[Turkish lira ]</th>\n",
       "      <th>[US dollar ]</th>\n",
       "      <th>[South African rand ]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.8406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7643</td>\n",
       "      <td>1.6138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.938</td>\n",
       "      <td>7.4433</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3143</td>\n",
       "      <td>27.2075</td>\n",
       "      <td>9.1650</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>188.8400</td>\n",
       "      <td>42.560</td>\n",
       "      <td>42.5590</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>6.7855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.8474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.6165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.886</td>\n",
       "      <td>7.4431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3092</td>\n",
       "      <td>26.9876</td>\n",
       "      <td>9.1800</td>\n",
       "      <td>1.9436</td>\n",
       "      <td>188.8000</td>\n",
       "      <td>42.765</td>\n",
       "      <td>42.1678</td>\n",
       "      <td>0.3701</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>6.8283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.8820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>1.6116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58200</td>\n",
       "      <td>34.850</td>\n",
       "      <td>7.4452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>27.4315</td>\n",
       "      <td>9.3050</td>\n",
       "      <td>1.9699</td>\n",
       "      <td>188.7000</td>\n",
       "      <td>42.778</td>\n",
       "      <td>42.6949</td>\n",
       "      <td>0.3722</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>6.7307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.8944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7965</td>\n",
       "      <td>1.6123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58230</td>\n",
       "      <td>34.917</td>\n",
       "      <td>7.4495</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>26.5876</td>\n",
       "      <td>9.4025</td>\n",
       "      <td>1.9655</td>\n",
       "      <td>188.7750</td>\n",
       "      <td>42.848</td>\n",
       "      <td>42.5048</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>6.7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.9100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58231</td>\n",
       "      <td>35.107</td>\n",
       "      <td>7.4501</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>25.2875</td>\n",
       "      <td>9.4696</td>\n",
       "      <td>1.9554</td>\n",
       "      <td>189.0450</td>\n",
       "      <td>42.991</td>\n",
       "      <td>42.6799</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>6.9358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Period\\Unit: [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "5694   1999-01-08               1.8406              NaN               NaN   \n",
       "5695   1999-01-07               1.8474              NaN               NaN   \n",
       "5696   1999-01-06               1.8820              NaN               NaN   \n",
       "5697   1999-01-05               1.8944              NaN               NaN   \n",
       "5698   1999-01-04               1.9100              NaN               NaN   \n",
       "\n",
       "     [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ]  \\\n",
       "5694             1.7643         1.6138                      NaN   \n",
       "5695             1.7602         1.6165                      NaN   \n",
       "5696             1.7711         1.6116                      NaN   \n",
       "5697             1.7965         1.6123                      NaN   \n",
       "5698             1.8004         1.6168                      NaN   \n",
       "\n",
       "     [Cypriot pound ] [Czech koruna ] [Danish krone ]  ... [Romanian leu ]  \\\n",
       "5694          0.58187          34.938          7.4433  ...          1.3143   \n",
       "5695          0.58187          34.886          7.4431  ...          1.3092   \n",
       "5696          0.58200          34.850          7.4452  ...          1.3168   \n",
       "5697          0.58230          34.917          7.4495  ...          1.3168   \n",
       "5698          0.58231          35.107          7.4501  ...          1.3111   \n",
       "\n",
       "     [Russian rouble ] [Swedish krona ] [Singapore dollar ]  \\\n",
       "5694           27.2075           9.1650              1.9537   \n",
       "5695           26.9876           9.1800              1.9436   \n",
       "5696           27.4315           9.3050              1.9699   \n",
       "5697           26.5876           9.4025              1.9655   \n",
       "5698           25.2875           9.4696              1.9554   \n",
       "\n",
       "     [Slovenian tolar ] [Slovak koruna ] [Thai baht ] [Turkish lira ]  \\\n",
       "5694           188.8400           42.560      42.5590          0.3718   \n",
       "5695           188.8000           42.765      42.1678          0.3701   \n",
       "5696           188.7000           42.778      42.6949          0.3722   \n",
       "5697           188.7750           42.848      42.5048          0.3728   \n",
       "5698           189.0450           42.991      42.6799          0.3723   \n",
       "\n",
       "     [US dollar ]  [South African rand ]  \n",
       "5694       1.1659                 6.7855  \n",
       "5695       1.1632                 6.8283  \n",
       "5696       1.1743                 6.7307  \n",
       "5697       1.1790                 6.7975  \n",
       "5698       1.1789                 6.9358  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5699 entries, 0 to 5698\n",
      "Data columns (total 41 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   Period\\Unit:              5699 non-null   object \n",
      " 1   [Australian dollar ]      5699 non-null   object \n",
      " 2   [Bulgarian lev ]          5297 non-null   object \n",
      " 3   [Brazilian real ]         5431 non-null   object \n",
      " 4   [Canadian dollar ]        5699 non-null   object \n",
      " 5   [Swiss franc ]            5699 non-null   object \n",
      " 6   [Chinese yuan renminbi ]  5431 non-null   object \n",
      " 7   [Cypriot pound ]          2346 non-null   object \n",
      " 8   [Czech koruna ]           5699 non-null   object \n",
      " 9   [Danish krone ]           5699 non-null   object \n",
      " 10  [Estonian kroon ]         3130 non-null   object \n",
      " 11  [UK pound sterling ]      5699 non-null   object \n",
      " 12  [Greek drachma ]          520 non-null    object \n",
      " 13  [Hong Kong dollar ]       5699 non-null   object \n",
      " 14  [Croatian kuna ]          5431 non-null   object \n",
      " 15  [Hungarian forint ]       5699 non-null   object \n",
      " 16  [Indonesian rupiah ]      5699 non-null   object \n",
      " 17  [Israeli shekel ]         5431 non-null   object \n",
      " 18  [Indian rupee ]           5431 non-null   object \n",
      " 19  [Iceland krona ]          3292 non-null   float64\n",
      " 20  [Japanese yen ]           5699 non-null   object \n",
      " 21  [Korean won ]             5699 non-null   object \n",
      " 22  [Lithuanian litas ]       4159 non-null   object \n",
      " 23  [Latvian lats ]           3904 non-null   object \n",
      " 24  [Maltese lira ]           2346 non-null   object \n",
      " 25  [Mexican peso ]           5699 non-null   object \n",
      " 26  [Malaysian ringgit ]      5699 non-null   object \n",
      " 27  [Norwegian krone ]        5699 non-null   object \n",
      " 28  [New Zealand dollar ]     5699 non-null   object \n",
      " 29  [Philippine peso ]        5699 non-null   object \n",
      " 30  [Polish zloty ]           5699 non-null   object \n",
      " 31  [Romanian leu ]           5637 non-null   float64\n",
      " 32  [Russian rouble ]         5699 non-null   object \n",
      " 33  [Swedish krona ]          5699 non-null   object \n",
      " 34  [Singapore dollar ]       5699 non-null   object \n",
      " 35  [Slovenian tolar ]        2085 non-null   object \n",
      " 36  [Slovak koruna ]          2608 non-null   object \n",
      " 37  [Thai baht ]              5699 non-null   object \n",
      " 38  [Turkish lira ]           5637 non-null   float64\n",
      " 39  [US dollar ]              5699 non-null   object \n",
      " 40  [South African rand ]     5699 non-null   object \n",
      "dtypes: float64(3), object(38)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "exchange_rates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Time</th>\n",
       "      <th>[Australian dollar ]</th>\n",
       "      <th>[Bulgarian lev ]</th>\n",
       "      <th>[Brazilian real ]</th>\n",
       "      <th>[Canadian dollar ]</th>\n",
       "      <th>[Swiss franc ]</th>\n",
       "      <th>[Chinese yuan renminbi ]</th>\n",
       "      <th>[Cypriot pound ]</th>\n",
       "      <th>[Czech koruna ]</th>\n",
       "      <th>[Danish krone ]</th>\n",
       "      <th>...</th>\n",
       "      <th>[Romanian leu ]</th>\n",
       "      <th>[Russian rouble ]</th>\n",
       "      <th>[Swedish krona ]</th>\n",
       "      <th>[Singapore dollar ]</th>\n",
       "      <th>[Slovenian tolar ]</th>\n",
       "      <th>[Slovak koruna ]</th>\n",
       "      <th>[Thai baht ]</th>\n",
       "      <th>[Turkish lira ]</th>\n",
       "      <th>US_dollar</th>\n",
       "      <th>[South African rand ]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.9100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.8004</td>\n",
       "      <td>1.6168</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58231</td>\n",
       "      <td>35.107</td>\n",
       "      <td>7.4501</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3111</td>\n",
       "      <td>25.2875</td>\n",
       "      <td>9.4696</td>\n",
       "      <td>1.9554</td>\n",
       "      <td>189.0450</td>\n",
       "      <td>42.991</td>\n",
       "      <td>42.6799</td>\n",
       "      <td>0.3723</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>6.9358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.8944</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7965</td>\n",
       "      <td>1.6123</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58230</td>\n",
       "      <td>34.917</td>\n",
       "      <td>7.4495</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>26.5876</td>\n",
       "      <td>9.4025</td>\n",
       "      <td>1.9655</td>\n",
       "      <td>188.7750</td>\n",
       "      <td>42.848</td>\n",
       "      <td>42.5048</td>\n",
       "      <td>0.3728</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>6.7975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.8820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7711</td>\n",
       "      <td>1.6116</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58200</td>\n",
       "      <td>34.850</td>\n",
       "      <td>7.4452</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3168</td>\n",
       "      <td>27.4315</td>\n",
       "      <td>9.3050</td>\n",
       "      <td>1.9699</td>\n",
       "      <td>188.7000</td>\n",
       "      <td>42.778</td>\n",
       "      <td>42.6949</td>\n",
       "      <td>0.3722</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>6.7307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.8474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7602</td>\n",
       "      <td>1.6165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.886</td>\n",
       "      <td>7.4431</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3092</td>\n",
       "      <td>26.9876</td>\n",
       "      <td>9.1800</td>\n",
       "      <td>1.9436</td>\n",
       "      <td>188.8000</td>\n",
       "      <td>42.765</td>\n",
       "      <td>42.1678</td>\n",
       "      <td>0.3701</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>6.8283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.8406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7643</td>\n",
       "      <td>1.6138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.58187</td>\n",
       "      <td>34.938</td>\n",
       "      <td>7.4433</td>\n",
       "      <td>...</td>\n",
       "      <td>1.3143</td>\n",
       "      <td>27.2075</td>\n",
       "      <td>9.1650</td>\n",
       "      <td>1.9537</td>\n",
       "      <td>188.8400</td>\n",
       "      <td>42.560</td>\n",
       "      <td>42.5590</td>\n",
       "      <td>0.3718</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>6.7855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Time [Australian dollar ] [Bulgarian lev ] [Brazilian real ]  \\\n",
       "0 1999-01-04               1.9100              NaN               NaN   \n",
       "1 1999-01-05               1.8944              NaN               NaN   \n",
       "2 1999-01-06               1.8820              NaN               NaN   \n",
       "3 1999-01-07               1.8474              NaN               NaN   \n",
       "4 1999-01-08               1.8406              NaN               NaN   \n",
       "\n",
       "  [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ] [Cypriot pound ]  \\\n",
       "0             1.8004         1.6168                      NaN          0.58231   \n",
       "1             1.7965         1.6123                      NaN          0.58230   \n",
       "2             1.7711         1.6116                      NaN          0.58200   \n",
       "3             1.7602         1.6165                      NaN          0.58187   \n",
       "4             1.7643         1.6138                      NaN          0.58187   \n",
       "\n",
       "  [Czech koruna ] [Danish krone ]  ... [Romanian leu ] [Russian rouble ]  \\\n",
       "0          35.107          7.4501  ...          1.3111           25.2875   \n",
       "1          34.917          7.4495  ...          1.3168           26.5876   \n",
       "2          34.850          7.4452  ...          1.3168           27.4315   \n",
       "3          34.886          7.4431  ...          1.3092           26.9876   \n",
       "4          34.938          7.4433  ...          1.3143           27.2075   \n",
       "\n",
       "  [Swedish krona ] [Singapore dollar ] [Slovenian tolar ] [Slovak koruna ]  \\\n",
       "0           9.4696              1.9554           189.0450           42.991   \n",
       "1           9.4025              1.9655           188.7750           42.848   \n",
       "2           9.3050              1.9699           188.7000           42.778   \n",
       "3           9.1800              1.9436           188.8000           42.765   \n",
       "4           9.1650              1.9537           188.8400           42.560   \n",
       "\n",
       "  [Thai baht ] [Turkish lira ] US_dollar  [South African rand ]  \n",
       "0      42.6799          0.3723    1.1789                 6.9358  \n",
       "1      42.5048          0.3728    1.1790                 6.7975  \n",
       "2      42.6949          0.3722    1.1743                 6.7307  \n",
       "3      42.1678          0.3701    1.1632                 6.8283  \n",
       "4      42.5590          0.3718    1.1659                 6.7855  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates.rename(columns={'[US dollar ]': 'US_dollar',\n",
    "                               'Period\\\\Unit:': 'Time'},\n",
    "                      inplace=True)\n",
    "exchange_rates['Time'] = pd.to_datetime(exchange_rates['Time'])\n",
    "exchange_rates.sort_values('Time', inplace=True)\n",
    "exchange_rates.reset_index(drop=True, inplace=True)\n",
    "exchange_rates.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         62\n",
       "1.2276     9\n",
       "1.1215     8\n",
       "1.1305     7\n",
       "1.1346     6\n",
       "          ..\n",
       "1.3573     1\n",
       "1.2083     1\n",
       "1.1000     1\n",
       "1.0679     1\n",
       "1.3654     1\n",
       "Name: US_dollar, Length: 3528, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar = exchange_rates[['Time', 'US_dollar']].copy()\n",
    "euro_to_dollar['US_dollar'].value_counts() # 62 '-' characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5637 entries, 0 to 5698\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Time       5637 non-null   datetime64[ns]\n",
      " 1   US_dollar  5637 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(1)\n",
      "memory usage: 132.1 KB\n"
     ]
    }
   ],
   "source": [
    "euro_to_dollar = euro_to_dollar[euro_to_dollar['US_dollar'] != '-']\n",
    "euro_to_dollar['US_dollar'] = euro_to_dollar['US_dollar'].astype(float)\n",
    "euro_to_dollar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rolling Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 900x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "\n",
    "plt.subplot(3,2,1)\n",
    "plt.plot(euro_to_dollar['Time'], euro_to_dollar['US_dollar'])\n",
    "plt.title('Original values', weight='bold')\n",
    "\n",
    "for i, rolling_mean in zip([2, 3, 4, 5, 6],\n",
    "                           [7, 30, 50, 100, 365]):\n",
    "    plt.subplot(3,2,i)\n",
    "    plt.plot(euro_to_dollar['Time'],\n",
    "             euro_to_dollar['US_dollar'].rolling(rolling_mean).mean())\n",
    "    plt.title('Rolling Window:' + str(rolling_mean), weight='bold')\n",
    "    \n",
    "plt.tight_layout() # Auto-adjusts the padding between subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Time</th>\n",
       "      <th>US_dollar</th>\n",
       "      <th>rolling_mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.1789</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.1790</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.1743</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.1632</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.1659</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5694</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1.2296</td>\n",
       "      <td>1.211170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.2271</td>\n",
       "      <td>1.212530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.2338</td>\n",
       "      <td>1.213987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.2276</td>\n",
       "      <td>1.215357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.2250</td>\n",
       "      <td>1.216557</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5637 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time  US_dollar  rolling_mean\n",
       "0    1999-01-04     1.1789           NaN\n",
       "1    1999-01-05     1.1790           NaN\n",
       "2    1999-01-06     1.1743           NaN\n",
       "3    1999-01-07     1.1632           NaN\n",
       "4    1999-01-08     1.1659           NaN\n",
       "...         ...        ...           ...\n",
       "5694 2021-01-04     1.2296      1.211170\n",
       "5695 2021-01-05     1.2271      1.212530\n",
       "5696 2021-01-06     1.2338      1.213987\n",
       "5697 2021-01-07     1.2276      1.215357\n",
       "5698 2021-01-08     1.2250      1.216557\n",
       "\n",
       "[5637 rows x 3 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()\n",
    "euro_to_dollar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Design Decisions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are a few story ideas for our data:\n",
    "\n",
    "* We show how the euro-dollar rate has changed during the coronavirus pandemic. We can show the 2020 data and the 2016-2019 data as a baseline; we can use a line plot.\n",
    "\n",
    "* We show how the euro-dollar rate changed during the 2007-2008's financial crisis. We can also show the data for 2016 and 2009 for comparison; we can use a line plot.\n",
    "\n",
    "* We show comparatively how the euro-dollar rate changed under the last three US presidents (George W. Bush (2001-2009), Barack Obama (2009-2017), and Donald Trump (2017-2021)); we can use a line plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storytelling Data Visualization Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Financial Crisis Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#financial_crisis = euro_to_dollar.copy(\n",
    "#                   )[(euro_to_dollar['Time'].dt.year >= 2006\n",
    "#                   ) & (euro_to_dollar['Time'].dt.year <= 2009)]\n",
    "#financial_crisis_7_8 = euro_to_dollar.copy(\n",
    "#                   )[(euro_to_dollar.Time.dt.year >= 2007\n",
    "#                   ) & (euro_to_dollar.Time.dt.year <= 2008)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Adding the FiveThirtyEight style\n",
    "#import matplotlib.style as style\n",
    "#style.use('fivethirtyeight')\n",
    "\n",
    "### Adding the plot\n",
    "#fig,ax = plt.subplots(figsize=(8,3))\n",
    "#ax.plot(financial_crisis['Time'],\n",
    "#        financial_crisis['rolling_mean'],\n",
    "#        linewidth=1, color='#A6D785')\n",
    "\n",
    "### Highlighting the 2007-2008 period\n",
    "#ax.plot(financial_crisis_7_8['Time'],\n",
    "#        financial_crisis_7_8['rolling_mean'],\n",
    "#        linewidth=3, color='#e23d28')\n",
    "\n",
    "### Highlihting the peak of the crisis\n",
    "#ax.axvspan(xmin=13950, xmax=14140, ymin=0.09,\n",
    "#           alpha=0.3, color='grey')\n",
    "\n",
    "### Adding separate tick labels\n",
    "#ax.set_xticklabels([])\n",
    "#ax.set_yticklabels([])\n",
    "\n",
    "#x = 13110\n",
    "#for year in ['2006', '2007', '2008', '2009', '2010']:\n",
    "#    ax.text(x, 1.13, year, alpha=0.5, fontsize=11)\n",
    "#    x += 365\n",
    "\n",
    "#y = 1.193   \n",
    "#for rate in ['1.2', '1.3', '1.4', '1.5']:\n",
    "#    ax.text(13020, y, rate, alpha=0.5, fontsize=11)\n",
    "#    y += 0.1\n",
    "\n",
    "### Adding a title and a subtitle    \n",
    "#ax.text(13020, 1.67, \"Euro-USD rate peaked at 1.59 during 2007-2008's financial crisis\",\n",
    "#        weight='bold')\n",
    "#ax.text(13020, 1.63, 'Euro-USD exchange rates between 2006 and 2010',\n",
    "#        size=12)\n",
    "\n",
    "### Adding a signature\n",
    "#ax.text(13020, 1.07, '©DATAQUEST' + ' '*94 + 'Source: European Central Bank',\n",
    "#        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "#        size=10)\n",
    "\n",
    "### Add some transparency to the grid\n",
    "#ax.grid(alpha=0.5)\n",
    "\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Three US Presidencies Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "bush_obama_trump = euro_to_dollar.copy(\n",
    "                   )[(euro_to_dollar['Time'].dt.year >= 2001) & (euro_to_dollar['Time'].dt.year < 2021)]\n",
    "bush = bush_obama_trump.copy(\n",
    "       )[bush_obama_trump['Time'].dt.year < 2009]\n",
    "obama = bush_obama_trump.copy(\n",
    "       )[(bush_obama_trump['Time'].dt.year >= 2009) & (bush_obama_trump['Time'].dt.year < 2017)]\n",
    "trump = bush_obama_trump.copy(\n",
    "       )[(bush_obama_trump['Time'].dt.year >= 2017) & (bush_obama_trump['Time'].dt.year < 2021)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, you'll notice we used matplotlib's functional approach to build the graphs. We use this approach because it offers more flexibility in arranging the subplots:\n",
    "\n",
    "* We first build three of the graphs on a 2-by-3 grid (this grid should have six subplots, but we only build three; the bottom row remains empty).\n",
    "* We then build only the bottom graph of a 2-by-1 grid (this grid should have two subplots; the top row remains empty).\n",
    "* The two grids are merged, and we end up with three graphs on the top row and one graph on the bottom row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'style' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-7b8cb12b6d86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m### Adding the FiveThirtyEight style\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mstyle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'fivethirtyeight'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m### Adding the subplots\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'style' is not defined"
     ]
    }
   ],
   "source": [
    "### Adding the FiveThirtyEight style\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "### Adding the subplots\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax1 = plt.subplot(2,3,1)\n",
    "ax2 = plt.subplot(2,3,2)\n",
    "ax3 = plt.subplot(2,3,3)\n",
    "ax4 = plt.subplot(2,1,2)\n",
    "axes = [ax1, ax2, ax3, ax4]\n",
    "\n",
    "### Changes to all the subplots\n",
    "for ax in axes:\n",
    "    ax.set_ylim(0.8, 1.7)\n",
    "    ax.set_yticks([1.0, 1.2, 1.4, 1.6])\n",
    "    ax.set_yticklabels(['1.0', '1.2','1.4', '1.6'],\n",
    "                   alpha=0.3)\n",
    "    ax.grid(alpha=0.5)    \n",
    "    \n",
    "\n",
    "### Ax1: Bush\n",
    "ax1.plot(bush['Time'], bush['rolling_mean'],\n",
    "        color='#BF5FFF')\n",
    "ax1.set_xticklabels(['', '2001', '', '2003', '', '2005', '',\n",
    "                     '2007', '', '2009'],\n",
    "                   alpha=0.3)\n",
    "ax1.text(12300, 1.92, 'BUSH', fontsize=18, weight='bold',\n",
    "        color='#BF5FFF')\n",
    "ax1.text(12000, 1.8, '(2001-2009)', weight='bold',\n",
    "        alpha=0.3)\n",
    "\n",
    "\n",
    "### Ax2: Obama\n",
    "ax2.plot(obama['Time'], obama['rolling_mean'],\n",
    "        color='#ffa500')\n",
    "ax2.set_xticklabels(['', '2009', '', '2011', '', '2013', '',\n",
    "                     '2015', '', '2017'],\n",
    "                   alpha=0.3)\n",
    "ax2.text(15100, 1.92, 'OBAMA', fontsize=18, weight='bold',\n",
    "        color='#ffa500')\n",
    "ax2.text(14950, 1.8, '(2009-2017)', weight='bold',\n",
    "         alpha=0.3)\n",
    "\n",
    "\n",
    "### Ax3: Trump\n",
    "ax3.plot(trump['Time'], trump['rolling_mean'],\n",
    "        color='#00B2EE')\n",
    "ax3.set_xticklabels(['2017', '', '2018', '', '2019', '',\n",
    "                     '2020', '', '2021'],\n",
    "                   alpha=0.3)\n",
    "ax3.text(17650, 1.92, 'TRUMP', fontsize=18, weight='bold',\n",
    "        color='#00B2EE')\n",
    "ax3.text(17560, 1.8, '(2017-2021)', weight='bold',\n",
    "         alpha=0.3)\n",
    "\n",
    "### Ax4: Bush-Obama-Trump\n",
    "ax4.plot(bush['Time'], bush['rolling_mean'],\n",
    "        color='#BF5FFF')\n",
    "ax4.plot(obama['Time'], obama['rolling_mean'],\n",
    "        color='#ffa500')\n",
    "ax4.plot(trump['Time'], trump['rolling_mean'],\n",
    "        color='#00B2EE')\n",
    "ax4.grid(alpha=0.5)\n",
    "ax4.set_xticks([])\n",
    "\n",
    "### Adding a title and a subtitle\n",
    "ax1.text(10800, 2.35, 'EURO-USD rate averaged 1.22 under the last three US presidents',\n",
    "         fontsize=20, weight='bold')\n",
    "ax1.text(10800, 2.14, '''EURO-USD exchange rates under George W. Bush (2001 - 2009), Barack Obama (2009-2017),\n",
    "and Donald Trump (2017-2021)''',\n",
    "        fontsize=16)\n",
    "\n",
    "### Adding a signature\n",
    "ax4.text(10700, 0.65, '©DATAQUEST' + ' '*103 + 'Source: European Central Bank',\n",
    "        color = '#f0f0f0', backgroundcolor = '#4d4d4d',\n",
    "        size=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}