project_5- euro daily exchange rates/20210817 - Project 5 - euro exchange rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <p style=\"text-align: center;\"> Storytelling data visualization of  euro exchange rate</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Introduction:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we will use a dataset regarding **euro currency** and our objective will be to find the best way to represent the data information in order to be easy to understand the evolution of the exchange rate euro/US dolar just checking data visualization. \n",
    "\n",
    "So, before start this project, it is important we clarify some concepts:\n",
    "\n",
    "\n",
    "- The euro (symbolized with €) is the official currency in most of the countries of the European Union.\n",
    "\n",
    "- As all other currency in the world, euro also has **exchange rate** regarding other currency, i.e., the rate at which one currency will be exchanged for another currency.\n",
    "\n",
    "- For example, if the exchange rate of the euro to the US dollar is 1.5, it means that we will get 1.5 US dollars per each 1.0 euro that we pay (one euro has more value than one US dollar at this exchange rate).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset describes the euro daily exchange rates between 1999 and 2021, and its source is the European Central Bank. The data is frequently updated and we can be download [here](https://www.kaggle.com/lsind18/euro-exchange-daily-rates-19992020). We will use one version download on January 2021.  \n",
    "\n",
    "Even we have information about much more exchange rates, our focus in the this project will be on the exchange rate between the euro and the American dollar (US dollar / USD).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. First overview of dataset: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us start to import the dataset and check the first and the last 5 five rows of dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#First: import the libraries we will need for this project:\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "# To enable Jupyter to display graphs\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Second: import the csv file into a pandas DataFrame:\n",
    "exchange_rates = pd.read_csv(\"euro-daily-hist_1999_2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <th>0</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.5758</td>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <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",
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       "    <tr>\n",
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       "      <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>5699 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",
       "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",
       "0                1.5543         1.0827                   7.9184   \n",
       "1                1.5601         1.0833                   7.9392   \n",
       "2                1.5640         1.0821                   7.9653   \n",
       "3                1.5651         1.0803                   7.9315   \n",
       "4                1.5621         1.0811                   7.9484   \n",
       "...                 ...            ...                      ...   \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",
       "0                 NaN          26.163          7.4369  ...          4.8708   \n",
       "1                 NaN          26.147          7.4392  ...          4.8712   \n",
       "2                 NaN          26.145          7.4393  ...          4.8720   \n",
       "3                 NaN          26.227          7.4387  ...          4.8721   \n",
       "4                 NaN          26.141          7.4379  ...          4.8713   \n",
       "...               ...             ...             ...  ...             ...   \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",
       "0              90.8000          10.0510              1.6228   \n",
       "1              91.2000          10.0575              1.6253   \n",
       "2              90.8175          10.0653              1.6246   \n",
       "3              91.6715          10.0570              1.6180   \n",
       "4              90.3420          10.0895              1.6198   \n",
       "...                ...              ...                 ...   \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",
       "0                   NaN              NaN      36.8480          9.0146   \n",
       "1                   NaN              NaN      36.8590          8.9987   \n",
       "2                   NaN              NaN      36.9210          9.0554   \n",
       "3                   NaN              NaN      36.7760          9.0694   \n",
       "4                   NaN              NaN      36.7280          9.0579   \n",
       "...                 ...              ...          ...             ...   \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",
       "0          1.2250                18.7212  \n",
       "1          1.2276                18.7919  \n",
       "2          1.2338                18.5123  \n",
       "3          1.2271                18.4194  \n",
       "4          1.2296                17.9214  \n",
       "...           ...                    ...  \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",
       "[5699 rows x 41 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Third: visualize some rows:\n",
    "exchange_rates #as alternative we could print rows using df.head() and df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "#Fourth: check some data details (type, null values, ...):\n",
    "exchange_rates.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking this information we can takes following notes:\n",
    "- Dataset has 5699 rows × 41 columns\n",
    "\n",
    "\n",
    "- There are some currency with many null values. For example \"[Greek drachma  ]\", \"[Maltese lira  ]\", \"[Cypriot pound  ]\", \"[Slovenian tolar  ]\" and \"[Slovak koruna   ]\", which more than half of values are null -> this information should be important if we will work with this exchange rates.\n",
    "\n",
    "\n",
    "- Although all columns are regarding numerical values, almost all columns are object type, except 3: \"[Iceland krona  ]\", \"[Romanian leu  ]\" and \"[Turkish lira  ]\". -> this should mean that we will need to convert the reaming data to numerical format too (float64), except the first column \"Period\\Unit:\" which should be convert to datetime type.\n",
    "\n",
    "\n",
    "- The columns names are not easy to work because square brackets, capital letters and spaces. -> To be easier we should simplified the column names at the least of the columns with what we will work.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Data Cleaning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Rename columns:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned before, we will focus on US dolar, so we will rename only `[US dollar ]` and `Period\\Unit:` columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "exchange_rates.rename({\"[US dollar ]\":\"US_dollar\", r\"Period\\Unit:\":\"Time\"}, axis=1, inplace=True) \n",
    "#In order to avoid `unicode error` in the 2nd column name, we need to use \"r\" before the string name\n",
    "#as we have \"\\U...\" it is interpreted as eight-character Unicode escape, if we don't put \"r\" or use double \"\\\\\" "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Time', '[Australian dollar ]', '[Bulgarian lev ]', '[Brazilian real ]',\n",
       "       '[Canadian dollar ]', '[Swiss franc ]', '[Chinese yuan renminbi ]',\n",
       "       '[Cypriot pound ]', '[Czech koruna ]', '[Danish krone ]',\n",
       "       '[Estonian kroon ]', '[UK pound sterling ]', '[Greek drachma ]',\n",
       "       '[Hong Kong dollar ]', '[Croatian kuna ]', '[Hungarian forint ]',\n",
       "       '[Indonesian rupiah ]', '[Israeli shekel ]', '[Indian rupee ]',\n",
       "       '[Iceland krona ]', '[Japanese yen ]', '[Korean won ]',\n",
       "       '[Lithuanian litas ]', '[Latvian lats ]', '[Maltese lira ]',\n",
       "       '[Mexican peso ]', '[Malaysian ringgit ]', '[Norwegian krone ]',\n",
       "       '[New Zealand dollar ]', '[Philippine peso ]', '[Polish zloty ]',\n",
       "       '[Romanian leu ]', '[Russian rouble ]', '[Swedish krona ]',\n",
       "       '[Singapore dollar ]', '[Slovenian tolar ]', '[Slovak koruna ]',\n",
       "       '[Thai baht ]', '[Turkish lira ]', 'US_dollar',\n",
       "       '[South African rand ]'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates.columns # to visualize the changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Convert `Time` column in datetime type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#format of information in this column is: 2021-01-08 \n",
    "exchange_rates[\"Time\"]=pd.to_datetime(exchange_rates[\"Time\"], format=\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates[\"Time\"].dtype #checking the changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Sort values by `Time` in ascending order:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As our goal is to create plots which show the exchange rate evolution by time, it is important to do this step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "exchange_rates.sort_values(\"Time\", ascending=True,inplace=True) # sort values by time\n",
    "exchange_rates.reset_index(drop=True, inplace=True) # reset the index (and drop the initial index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>1.5928</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",
       "    <tr>\n",
       "      <th>5695</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>5696</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>5697</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>5698</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5699 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",
       "5694 2021-01-04               1.5928           1.9558            6.3241   \n",
       "5695 2021-01-05               1.5927           1.9558            6.5517   \n",
       "5696 2021-01-06               1.5824           1.9558            6.5119   \n",
       "5697 2021-01-07               1.5836           1.9558            6.5172   \n",
       "5698 2021-01-08               1.5758           1.9558            6.5748   \n",
       "\n",
       "     [Canadian dollar ] [Swiss franc ] [Chinese yuan renminbi ]  \\\n",
       "0                1.8004         1.6168                      NaN   \n",
       "1                1.7965         1.6123                      NaN   \n",
       "2                1.7711         1.6116                      NaN   \n",
       "3                1.7602         1.6165                      NaN   \n",
       "4                1.7643         1.6138                      NaN   \n",
       "...                 ...            ...                      ...   \n",
       "5694             1.5621         1.0811                   7.9484   \n",
       "5695             1.5651         1.0803                   7.9315   \n",
       "5696             1.5640         1.0821                   7.9653   \n",
       "5697             1.5601         1.0833                   7.9392   \n",
       "5698             1.5543         1.0827                   7.9184   \n",
       "\n",
       "     [Cypriot pound ] [Czech koruna ] [Danish krone ]  ... [Romanian leu ]  \\\n",
       "0             0.58231          35.107          7.4501  ...          1.3111   \n",
       "1             0.58230          34.917          7.4495  ...          1.3168   \n",
       "2             0.58200          34.850          7.4452  ...          1.3168   \n",
       "3             0.58187          34.886          7.4431  ...          1.3092   \n",
       "4             0.58187          34.938          7.4433  ...          1.3143   \n",
       "...               ...             ...             ...  ...             ...   \n",
       "5694              NaN          26.141          7.4379  ...          4.8713   \n",
       "5695              NaN          26.227          7.4387  ...          4.8721   \n",
       "5696              NaN          26.145          7.4393  ...          4.8720   \n",
       "5697              NaN          26.147          7.4392  ...          4.8712   \n",
       "5698              NaN          26.163          7.4369  ...          4.8708   \n",
       "\n",
       "     [Russian rouble ] [Swedish krona ] [Singapore dollar ]  \\\n",
       "0              25.2875           9.4696              1.9554   \n",
       "1              26.5876           9.4025              1.9655   \n",
       "2              27.4315           9.3050              1.9699   \n",
       "3              26.9876           9.1800              1.9436   \n",
       "4              27.2075           9.1650              1.9537   \n",
       "...                ...              ...                 ...   \n",
       "5694           90.3420          10.0895              1.6198   \n",
       "5695           91.6715          10.0570              1.6180   \n",
       "5696           90.8175          10.0653              1.6246   \n",
       "5697           91.2000          10.0575              1.6253   \n",
       "5698           90.8000          10.0510              1.6228   \n",
       "\n",
       "     [Slovenian tolar ] [Slovak koruna ] [Thai baht ] [Turkish lira ]  \\\n",
       "0              189.0450           42.991      42.6799          0.3723   \n",
       "1              188.7750           42.848      42.5048          0.3728   \n",
       "2              188.7000           42.778      42.6949          0.3722   \n",
       "3              188.8000           42.765      42.1678          0.3701   \n",
       "4              188.8400           42.560      42.5590          0.3718   \n",
       "...                 ...              ...          ...             ...   \n",
       "5694                NaN              NaN      36.7280          9.0579   \n",
       "5695                NaN              NaN      36.7760          9.0694   \n",
       "5696                NaN              NaN      36.9210          9.0554   \n",
       "5697                NaN              NaN      36.8590          8.9987   \n",
       "5698                NaN              NaN      36.8480          9.0146   \n",
       "\n",
       "     US_dollar  [South African rand ]  \n",
       "0       1.1789                 6.9358  \n",
       "1       1.1790                 6.7975  \n",
       "2       1.1743                 6.7307  \n",
       "3       1.1632                 6.8283  \n",
       "4       1.1659                 6.7855  \n",
       "...        ...                    ...  \n",
       "5694    1.2296                17.9214  \n",
       "5695    1.2271                18.4194  \n",
       "5696    1.2338                18.5123  \n",
       "5697    1.2276                18.7919  \n",
       "5698    1.2250                18.7212  \n",
       "\n",
       "[5699 rows x 41 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exchange_rates # visualize the changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Isolate `Time` and `US_dollar` in new dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "euro_to_dollar=exchange_rates.copy()[[\"Time\", \"US_dollar\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.1789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.1632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.2276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.2250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5699 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time US_dollar\n",
       "0    1999-01-04    1.1789\n",
       "1    1999-01-05    1.1790\n",
       "2    1999-01-06    1.1743\n",
       "3    1999-01-07    1.1632\n",
       "4    1999-01-08    1.1659\n",
       "...         ...       ...\n",
       "5694 2021-01-04    1.2296\n",
       "5695 2021-01-05    1.2271\n",
       "5696 2021-01-06    1.2338\n",
       "5697 2021-01-07    1.2276\n",
       "5698 2021-01-08    1.2250\n",
       "\n",
       "[5699 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar # checking the new Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5699 entries, 0 to 5698\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Time       5699 non-null   datetime64[ns]\n",
      " 1   US_dollar  5699 non-null   object        \n",
      "dtypes: datetime64[ns](1), object(1)\n",
      "memory usage: 89.2+ KB\n"
     ]
    }
   ],
   "source": [
    "euro_to_dollar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking this information we can identify:\n",
    "- there is not null values in the US_dollar column\n",
    "- US_dollar is still with object type - we will need to convert this to a numerical type\n",
    "- Before converting it in numerical type, let us inspect the unique values in the \"US_dollar\" column in order to check if we detect something strange or if we can proceed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-         62\n",
       "1.2276     9\n",
       "1.1215     8\n",
       "1.1305     7\n",
       "1.1268     6\n",
       "          ..\n",
       "1.1159     1\n",
       "0.9838     1\n",
       "1.3112     1\n",
       "1.2752     1\n",
       "1.1078     1\n",
       "Name: US_dollar, Length: 3528, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar[\"US_dollar\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on this information we see we have 62 times \"-\" in this column.\n",
    "So, if we don't have information, it is not relevant information, for our project. So, we need to remove this rows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Drop rows without rate value in US_dollar column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "euro_to_dollar=euro_to_dollar[euro_to_dollar[\"US_dollar\"]!=\"-\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-01-04</td>\n",
       "      <td>1.1789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1999-01-05</td>\n",
       "      <td>1.1790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>1.1743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1999-01-07</td>\n",
       "      <td>1.1632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1999-01-08</td>\n",
       "      <td>1.1659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5696</th>\n",
       "      <td>2021-01-06</td>\n",
       "      <td>1.2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5697</th>\n",
       "      <td>2021-01-07</td>\n",
       "      <td>1.2276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5698</th>\n",
       "      <td>2021-01-08</td>\n",
       "      <td>1.2250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5637 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Time US_dollar\n",
       "0    1999-01-04    1.1789\n",
       "1    1999-01-05    1.1790\n",
       "2    1999-01-06    1.1743\n",
       "3    1999-01-07    1.1632\n",
       "4    1999-01-08    1.1659\n",
       "...         ...       ...\n",
       "5694 2021-01-04    1.2296\n",
       "5695 2021-01-05    1.2271\n",
       "5696 2021-01-06    1.2338\n",
       "5697 2021-01-07    1.2276\n",
       "5698 2021-01-08    1.2250\n",
       "\n",
       "[5637 rows x 2 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "euro_to_dollar #checking our new dataset:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Convert the`US_dollar` column to float type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/midl/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    }
   ],
   "source": [
    "euro_to_dollar.loc[:,\"US_dollar\"]=euro_to_dollar.loc[:,\"US_dollar\"].astype(float)\n",
    "euro_to_dollar.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Data Visualization:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will start to create a line plot to visualize the evolution of the euro-dollar exchage rate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(euro_to_dollar[\"Time\"],euro_to_dollar[\"US_dollar\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The line's shape is not smooth, because as usually the exchange rates go up and down, down and up again, day to day. \n",
    "In order to make easier our objective to check the evolution between 1999 and 2021, instead to focus on daily variation, we want to focus only on long-term trends. So, using **rolling mean (moving average)**, and taking 30 days (one month) as rolling window, we expect to get smooth new line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-5ebbf27d070a>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()\n"
     ]
    }
   ],
   "source": [
    "euro_to_dollar['rolling_mean'] = euro_to_dollar['US_dollar'].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(euro_to_dollar['Time'], euro_to_dollar['rolling_mean'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Historical facts and its impact on exchange rate evolution:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on some literature as [corporate finance institute](https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/euro-to-dollar-exchange-rate/), we can find some factors that have impact on exchange rate, from what we will highlight the following:\n",
    "* Countries that are included in the Eurozone (and changes to that list)\n",
    "* Employment rates, job creation, etc. such in Eurozone as well as in US side\n",
    "* Domestic politics and international policies, including the trade agreements, tariffs, and duties set internationally from US side.\n",
    "* Economic growth in Eurozone countries\n",
    "\n",
    "\n",
    "Relating this factores with some historical facts found in the [Europe Union website](https://europa.eu/euroat20/journey-of-the-euro/) and in news like [here](https://www.poundsterlinglive.com/eurusd/11303-eurusd-u-s-china-trade-fight-will-hurt-but-buy-the-dip-says-bank-of-america), we will try to check its impact in exchange rate (if any) using information in our dataset\n",
    "\n",
    "* 1999: The Euro currency is born\n",
    "* 2008: The global financial crisis \n",
    "* 2013-2015, the Euro fell heavily as debt problems emerged with the ‘PIIGS’ (Portugal, Italy, Irland, Greece and Spain).\n",
    "* 2018: China–United States trade war  - US began setting tariffs and other trade barriers on China \n",
    "* 2020: COVID-19 pandemic\n",
    "* USA President terms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bellow we can see one **exploratory graph** highlighting the fluctuations during these periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "style.use(\"fivethirtyeight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "decrease_period=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2007) & (euro_to_dollar['Time'].dt.year <= 2019)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ByPuf++4JrqoqJz/5GoDQ4Zee9/nRE8cBULBlp13jcgeq2Wwt9wQE9OlBizvHM/TwJsKuHg6qyrYrxlGy/zBYLJz67Jsa5xZu38Omvlew/55Hz9gOVQghhDgXSTTdQHlmNjkr/wQgaGA/F0dzpoA+PQAwnDjp4kjOLn3Bz5QeOoo+NJgWkyee9/n+3bug6HSUHUuk0s2nCJyvol37KE/PxCs2Br8uf0/LCB7UHwD1tC1GC7fuImf1Wiry8lHNZg498n+YiorJWfknWctWOj12IYQQTZskmm4gd/VaLOUVBA7og0/Hdq4O5wyeMVGA+xZuV81mkt6fDUDc1MlovTzPuw2Np4c1oVZVUub+YO8QXaqsaspD4IA+NbbjjLxxNJHjRhE4sB9dPnyNlvfdAcC+yQ+xscdl/C+uD4akU7bj0xcvd27gQgghmjxJNN1Azh9rAYgYc7VD9i1vLK/oSACMbrgVZVniKbYMvo7y1HQ0Xp7E3H5Lg9tqOXUyYC2R1JyUHU8CwLt1yxqP64MC6PrR6/T98WuibhpNxNiraz2//YtPoPH2In/tJrf9siGEEMI9SaLpYoakZHJXrwWNhpBhg10dTq08oiIAqMjIcqt5eqqqcvS51zEmp6EL8KfDq8+g8/NtcHshQy5G0eso3nOg2dw+r8wrIPXbRcDfUyDOJqB3dy5atZC+y+bR5T+v4xkTReQN1xJ7720EXXIRIEXthRBCnB9JNF0sb91mAMKvvhyfNnEujqZ2Wi9PvFu3RDWbKdi6y9Xh2GQsXkb+2k1ovL0Y8OdPxFQt6GkorY8Pgf37gKqSv36LnaJ0rcKdezGXlOLfuzshl597kZR/9y4E9utF1I2jGLT9d7p+/AaKotjmc6YvXOrokIUQQjQjkmi6kKqqpHxlLTDurqOZ1UKvsO4BXrRzr4sjscr+bQ2HH30egFYP3YNn1e39xgq5zFosv7oKQFOXWjXfNOiSixo1LSN6/A2g0VCwaTtmY7m9whNCCNHM1SvR3LhxIxMmTKBLly4EBQUxf/78el/g+PHjxMbG0qJFiwYH2VyVHUukLOEE+uAgom4a4+pw6uRTte+5ISnZxZGA2VjO4cdfAiD82ito9dA9dmvbv2c3AEoOJ9itTVcxl5WRt846MhtzHgXsa6MPCcKnbStUs5myhOP2CE8IIcQFoF6JZmlpKV27duWNN97A29u73o1XVFRw1113MWiQe2yp6G6S53wLWLec1Hh6uDiaulUvJDGcdH2imb7gZ+t+5u1a0232uyga+w3M+3fvjOKhp2jn3ia/H3jxgSNgseDXrTM+bVs1uj3fqooIJQebfhIuhBDCOer1CT1ixAheeOEFxo4di+Y8PtRffPFFunXrxtixYxscYHOlqqp1ERA0qO6js3lXzR8tOXAEs8HgsjjMBgOnPvkKgDZPPWTXJBOsI3fBgweAqlK8d79d23a2rKXWupf+vbvZpb3qBUFZv/5ul/aEEEI0fw6bo7lq1SpWrVrFm2++6ahLNGkVGVlUZOeiC/AnoF9PV4dzTl6xMfh26YCpqJjC7Xucem1LeQV7b3+A/7Xpx7r2F1OeloFvlw6EX3OFQ65XPXJXmnDCIe07S87v/wNo9CKpatXziEubwbQCIYQQzqFzRKMZGRk8/PDDzJs3D3//+u/bnZBw4XyAFS+wrt7VdmjDsWPHznn8ufomKyvLIftRK4qCyWQCQI2NhkMJnNoTT250mN2vdTaGzTvJ+3NDjcd8H76HY8ftN1fw9P4tDbCWSMrYtpPKJvo7ac7Npzw1HcXbiwxvPZl2eB2qyQRaLeVpGRzdvx/Fs36F8S+kv+uGaszfb1pamp2jEaerrX9Pf18UDVf93uCoz68LlaIoREREOO29t0OHDnU+75BE87777uOuu+6if//+53XeuYJtTnbuiAeg/b/uJOIcrzshIeHc/5A6ncMSzTZtrAuBjndsz6nV6wiyKLR24r/V3hnvAdZ6nlpvb1o9dA/Ro6+xW/v/7F/D9V5seXsW5Vt20y6uldvPn61N0oo5AAQN6EvHzp3t1m5eXCyGxJPEaD3wq8fvQH1+d0XD/37T0tKIiYlxQEQCzt6/p78vioY5/b3BUZ9fF6rqL0Lu8t7rkFvn69at48033yQ0NJTQ0FAeeughSktLCQ0NZe7cuY64ZJNSnpVD8e59oCiEDBno6nDqzbOqcHt5hvN2h6ksLCJ/w1ZQFPr/voiBG5YTPd6xc369W7fEq2UMFqMRY4rzR4vSF/zMuo4DOfjg0w1+881fvxWAFrffbM/Q8O/Rxdr+hq12bVcIIUTz5JARzU2bNtX4ecWKFbz77rusWbNGvn0D6d//hGo2E3zpxegC6j+1wNU8wkIBqMzNc9o1T344B7XSRODAfniEhjjtul4tW2BMTsOYkoZPu9ZOu66pqJjjr3+AubSMzJ9XEH7tlYRfe+V5tWExmSiOPwBAQL9edo0v5LJLyFq2kuL4g3ZtVwghRPNUr0SzpKSEEyesCyMsFgspKSnEx8cTHBxMy5YtmTFjBjt37mTZsmUAdO3atcb5u3fvRqPRnPH4hao6CYiecL1rAzlPHtHWEU3DyVSnXTN75Z8AxP3rDqddE6yJJmzHmOzcEc2CLTuozM23/Xzi7U/OO9HM/vV3zKVleLdphWeEfefSesZEA1CenmXXdoVzmIuKUXQ6FE8PTJnZ6CLCUXRaV4clhGjG6pVo7t69m9GjR9t+njlzJjNnzmTixInMmjWLjIwMEhMTHRZkc6KazRTtsZbN8e/RtBJvv66dQKOh9HACZoMRrbeXQ69nTM3AeDIFra8PIcOHOPRa/+TdKhaAor37ibntJqddt+zESQCib72RjEVLKTt6nMrCIvSBAfVuo/q2dsxtjSvSXhvP6OrpE5JoNhWqyYxh225KV/xB2YZtoAIWCwDa8FACJl6P/43X2b1UmBBCQD3naA4ZMoSCgoIz/ps1axYAs2bNYt++fWc9/9ZbbyU11XmjYO4sf+M2KjKz8Yprgbcdimg7k87XB68WUahmMxVZOQ6/XvoPPwLWbSE1OofM8jir8KuHA5C9Yo1TJ6mXHU8CwK9LR9tuTNWP1YeqqhRu2w1A4EV97B1ejXm6Mnnf/ZmLS8h85Hmyn3qVsrVbwGyxJZkA5uxc8v/zJQVffO/CKIUQzZl8hXUgVVVRT3tTByjefwiA0OFDmuQIgj4kGIDKvPxzHNl4pUesZZ/Crzu/W8f24NOxHR6R4ZgKCjGcR6LXGKrFQsGm7QD4dmpPQK/uAGT/d3W92yjYuI2y40noQ4JtC3fsSevni9bXB4vBSEVmtu3xYzPeYdeNU2wjssI9FH69gPK9B1G8vQi442aiv/mQlqsXEvnxa8R89zHBD1u3by2atwRD1RcUIYSwp6aX6TQRFXn5bBl8Heu7DKZgyw7b4yX7DwPg17Wjq0JrFH1wIACVeQUOv1ZZonW7S+82zh/5VRSFwKpC+hk//dcp1zSmpGFISkYfEkzQxX1tCXbx3votvDGmpnPw4ecAiJ40ziFlmRRFse6cBGT/tgaAlLkLSJ7zLYVbdnJi5gd2v6ZoGEtxCSW//gFA5PszCL73VjzatkLj5YlXr27oW8UScNMo/MePASBnxnuYnfB3LYS4sEii6SDJs+ZiPJmCuaSUvbc+QOH2PRhT0sj+r/WN379qtKqpcdaIpsVkwpB4CgDvVi0deq2zibnNWhooY8lyp9wmNiSlAODToQ2KVvv3rfMTSfU6/+hzr1ORkYV/7+60mnaXo8Ik+LJBABTvPcDxmR+S8Nzrtudy16zHcCrFYdcWf1NVFcvaLWQ98QqZj72EYeuuGs8XfLMI1WDEq19PPLt1Oms7wffdjkeHtliKiil20pcqIcSFQxJNByg7lsipz74BrLcaLUYju8ZNZvPFV6OaTARfNgj/7vYrou1M+uAgACrzCx16ndzVa7EYjXi3jkMfVP+FMPYUfOnF6IODKE9NJ+ndTx1+PcPJqhHc1tZ95b1aRKPx9KAiM5vKwqI6zy3PyCJ39Vo0Xl70+Po/Di2b5d/N+rubsXgZpz7+EoB2zz9G2DVXYCmvIG3+jw67trBSLRbrCOSn32LYshPj9j1kPf4yxUtXoaoqRQt+oXjhMlAUAm6vezGb4qEn6P7bASic/7OMagoh7EoSTQc48vQrYLEQeeMoBu1aQ+gVQ20T8PXBQXR89RkXR9hwzhrRrF4AEzZymEOvUxdFq6XDa9Z/q6T3Z5P714ZznNE4ZceslRuqV7wrWi3+vboB1pJFdclYshyA4CEX272k0T/5de9k+z0AazWClvfdQexdkwBI/ep7Kpwwh7e5U00mihYto3jpKiwlpTWeK/jsW8rWrAetlqCpdxIw8XoA8t6ZxanLbyL/k7kAhEy/H++qKSB18R7QB6+BfcFkIvPf/0fhdz9Svv+wddtRIYRoBEk07aw8K4eCzTtAo6Ht0/9G5+tD968+oMWUiUSMuZp+//0enya22vx0nlHhAA6/PVpetdDEMyrSodc5l8ix19CiKoE68sQMjKkZDrtW/kZrWaLA04qsR15/LYBtJfnZpC/4BYDoieMcE9xptN7etH3qIev/+/vRc94nKBoNQZdcREC/XpjLDOSv3+LwOJq74qWryP/oK/LemUXq+Psp+OoHjLv3Y9iyk6IffgGNBu2zDxI46QaCpt5J4OTx1hPNZhQPD8JmPI7/2JH1vl7wvbehDQmm8mQKBbPnkTH1aVLG3U3xTyukwoAQosGcWzOmGbBUVHLyo8/JX7+VwIv70uaxqTUWXaR+/QMAIcMG4RUTBYBGp2vSo5in8+tuXclcHH+ozuPMBiOp3yxE4+VJizvHoyjKeV2noqpOo0dkeMMCtaM2j00le8UflKdnsnnACKInjaPDy0/btY6oqqq2UdyA00ag/Htaa60W1bETj6m4BEPiSRQPPaGXX2q3mOoSPfEG9CFB+HbpaCt5pCgKYSOGUbRzL/kbtxE51n770V9o1PIKCr9eaPvZUlRM4dcLazwWOPkWyrpb514qikLQ3RPRRUdQfvgY/mNG4NH+/Pbi9ujYlhZL5lC2cTvG7Xsw7tqHKSWdvPfnYDEYCbzV8V9ihBDNjySa50FVVY48OYOMxdYdkAq37yZ3zTr6Lf8Orbc3ppJS0hf+AkDLe293YaSO49POOhpbnl73yN6xl94m7bvF1nPaxBFStYCkvqr3GK8uEO5K+uBA+iz6gt03301FZjbp3/9ERVYOPeZ+dN4J9NmY8gtRKyrRBfij9fa2Pe7buQMaby/Kjh6neP/hWuf2Fu221rD17dQejYfeLvGci6LV1rpjUfWK9IKN25wSR3NVfvAIlsIidHEtiPnqfco2bKN83yGKf1kJZjMeHdoSMOkGynJza5znd+0V+F17RYOvq+j1+A4bhO+wQaiqSvGi5eR//BUFn3+H98C+eDhxO1YhRPMgt87PQ/r8H8lYvAyNlyct778TXVAgpYcSOPmRdUFE0gezqcjMxrdze4KHDHRxtI6h8fJC0euwGMsxG8trPabsWKItyQQo3lf36Oc/qWYzpUePA+DbsX3Dg7Ujn3at6f/HEqJutpaCyf1jHRlVXyrsoTzLOlXgnyO4Wm8vIq67CrB+salN5s8rAGttVlfz69EFrY83hqRkmafZCKVrrPOBvQf0QfH0wPeKSwl55F6iv3yX4EfuJeLdF9B4ejo0BkVRCBg/Bp+rhoLZQu7r/5Fb6EKI8yaJZj0ZU9NJeOktADq98Tztn59Oj7n/AeDUx1+y/97HSJ41F4COrz1rt5Eud6Moim1Fs7m4pNZj8qq2QKxWsHn7eV3DkHgKi7Ecz5gol604r41HSDBdPniVDi8/BUB61ci2PVQXP/eoZSGPbxdrzdXqxUKnM5xKIWPRUgDCRgyzWzwNpdHpbPGWHjzq4miaJtVkovT3tQD4Xn15jec82rUm4Mbr0FZVf3CG0EfvQ+PvR8XRExQv+dVp1xVCNA+SaNbTkadexmIwEnrFUNuoVlD/PsTeezuq2Uz2Cmt9zKhbxhJ4cT9XhupwugBr8meqpeSOqqqkfmOdR9bq3/cCUHr42Hm1X3IoAbBuw+iOIm8cDYpC0fY9GFPT7dJmdTuetcxJra6nWT3Ke7qkD+bYjqmez+lqvh3bAlB84IiLI7EPi8lE/sZtVBbUXWLKXsr+twnVYETfuiWendo55Zp10fj7EXTfbQAULfgFtbzCxREJIZoSSTTrwVRaRv6GraDR0OmtF2o81/7Fx+k2620CB/aj5dTJdH7npWY7mllNF2gd0awsKj7juYqMLMqOHkfr422bp1qRl39et9xKDlsTTd8uHewQrf3pgwIIufxSVLOZ9KrRxMbK+2sjAAG9zyzk79+jC4pWS+HWXVRk/z0nz5iWQdaylQB0+/Qtt9nSNPjSiwHI/LHpj36pqsrhR19gzy33sGvMbVgcXO7HUlJqK03k24i5lvbmN2YEupYxmLNyKV62ytXhCCGaEPf4ZHJzqV//gFppIqBXN9sK22qKohAxZiR9f/ya9v/3GIpW66Ionaf61rmplkSzuuyRT6d26EOC0Pr5olZU1nrs2RTt2AOAn5smmgBRN14HQEkdq8HPR/XuP7WNhntGhhM4oC+q2UzR3gO2x3P/WIfFYCRo8AD86tj5xdnCRg5H0WopPZyAWl77PN6mImvZSjJ/sibMZceTOFU1H9sRVJOJnFfex5yTZ90e8pbRDrvW+VI0GoLumgBAyYo1qGaziyMSQjQVkmjWQ/Vt8Zb33eHiSNyDLdEsrCXRrNpGsXrbSI+wEAAqs3PPOLY2FXn55G/YisbTg+Chl9gjXIfw72G9TZ23fotdFr1UZOYAtd86h79Hd0urRnsBSvYfBiDsyssafX170np74dO+NarZTPnBhHOf4KbKs3I4+vSrAARd0h+A5DnfYjYYHHK9gjnfYdi0A8XTg7CXprvdl1bvQf3RBAZQeSwJw6Ydrg5HCNFESKJ5DkV7D1C89wCKXkfola5f1esO9IHWOZqVuXlnPFdy0Dovr3obRX1YKADl50g0K/MKSF+0lKPPvAZAQN+eeJy2+4y78W7bCr/unbEYjOSv29yotiwVlVTmF6BotehDa3/Nfl2t81Wzfv0dVVVRLRZy/1wP1Ky76S6CBltvnxfP/8nFkTTcsZfewlRUTECfHvT6/jMC+vTAVFRM1vK6d2k6H0kfzGFDj8tIueVf1iLsQPgrT553DUxn0Ph443+TdSS/bJ0U5BdC1I8kmnUwpmaw/55HAesiH62Pj4sjcg/VSU/hzr1nPJez6k8AW+Fwr9hoAEoPnn1hiKWykn13P8zhR5+3bbUYN+0uu8Zsb4qiEDJsMPD3dpkNVZFtHc3Uh4eedZ5lxHVXoQ8OomTfIUoOHKFg6y7K0zPxio0hoK/7JZqtHroHgPK9B7FUVJ7z+LLEU2QsWU5FTv1Gvh2tPD2TrKXW+a+d3noRjYee6FtvBLDVym2srGWrSHz7Yyrz8jGlZYBGQ8gTU/G+5CK7tO8IvsMGgaJQ+vtaTDlnftEUQoh/kkTzLCwVlRyY+gTlaRl4RkfS9ul/uzokt1G9/3ZZQs1yO5UFRRiT09B4exHQx7qopTrhzKtj1C99/o81tliMumWs03a4aQyftq0BKDtxslHtGNOsxe/r2qNcF+BP+ChrPc3Dj73AvjsfBCBi7NVuufjMMyIM307toLKSrLPs01524iSHH3+RI0+/wtZLR3Ho4efYNuyGRifu9pC+0LrIK/Sqy2xfrMKvsRaoL969zy63z5O/+A6A2HtuI+LN/6PFj1/gP6b+W0a6gr51S7z69waLhfKqjQKEEKIuF1yiqaoqmb+s4ORHX2AqKT3rcWnfLaZo5150QQH0+XmuW9/GdbbqWo//vHVevVuQV8sWtvllPh2spW7Kq7aU/CdLZSWpVcXd2z3/GL2XfHXGyn535d2qBQBlR080qp2Czdb5bn617Ppzulb/vhfP6EhKDhzGXFqGZ0wUcVOnNOrajhRzh3Xv7ZyVf57xnDEljZ2jbyP9h59Jm/d3cf/K/AK2Dh3DlsGj2HHNBIr3n1+xf3tQVZXUb60luqIn3GB7XB8UgH+f7ljKK8j88b+NuoYxJY2iXfEoHnraPDENn8H90VXNZ3Z3Xn17AFDy6x8ujkQI0RRccIlmwabtHJz2NCfe+A8Hpj5Za9kdU1Exx1/7AICOrz6Dd8sWTo7Svemrku5/li2qyLYmnh6nfWBWL245W6KZt3YzpYcS8IyJosUdtxB8yUVo9M7ZRrGx/Hv3QOvjTcnBI5Rn5TS4ndIj1jqjgf371HmcV0wUPb/7lIC+PfGKjaHja8+gDw5s8HUdLbRqakHBpu2oFkuN57KWr8JUUIhHZDjBlw2ixeQJ9F70ha10liHpFMXxBzn08HNO340mfcHPVGRm4xkVQdjImgXTo260rgQ/2y5N9ZU2/0dQVSKuuwqdn2+j2nI2v2uvQPHwwLhrH+b8AleHI4RwcxfcXuceEWFEjL2arKUryftzPUW74gns16vGMSlf/4DFaCTgot5EXH+tiyJ1X1pvL7Q+3pjLDJhLStH5+wFgqCrRc/rKaX1YCGg0VObkYamsPCOJzFu7CYDI669pcnNgtV6e+HRqR/Hu/RhOJtd567suthXnUZHnPNavcwf6Lf+uQddxNq9WsWgjw6jMzKH0UEKNEkyFO+MBaPvUv4keP9b2eL///sCpWV+jC/An7dtFlB4+RnH8QQKqpms4mqWykpMfWovgt/zXnWdMSwjoYx3Ny9+wFUt5BRpPj3O2WRx/kMylv2GpqCTsqsvwjAzn1GdzAYi57Sb7vgAn0AYH4tmzC8YdezFs2onfde5T71MI4X7qNaK5ceNGJkyYQJcuXQgKCmL+/Pl1Hn/48GFGjRpFhw4diIyMpFevXrz88stUVLh+RwnfDm3p9ulbxN59KwDHX/+gxohJ2YmTnPzPFwC0mnaXW85/cwf6qlHLitMWBGT9dzVAjbJEGp0Oj/Cqlef/GNUsO5ZI6twFAIRcNsih8TqKV4sYAIzJaQ1uo6Jqn3PPyIYlqu5KURQ8qwrQ52/cZnu8Mq+A3D/WgkZD0KCaC1982sTR+a0Xaf9/jxE9cRwAGXbc6vNcTv7nc4zJaXi1iiX2nlvPeN6vWye8YmMoz8gi938bz9le2g8/sePaiSR/9g2pX33P3on/YtvwcagVlQRfNqjJ7iLmM8z691r8y28ujkQI4e7qlWiWlpbStWtX3njjDby9vc95vIeHBxMnTuSnn35i+/btzJw5k3nz5vHqq682OmB7afXve9AHB1G4ZScZi6wfZGWJp9hzyz1YjEZCr7rMLfaOdlfVZXgqMq3Jo6qqFFWtQg+7aliNY307W2tA5q5ZX+Px3L82gMVC+LVX2HaTaWqqV9UbUxqRaFaVfvKIqL2GZlPmWTWfL3/jVttjRbv3oVaaCOzXq85pKRGjRgDWUW9n3D7P37CVpPc+A6DTmy/UWgFAo9cTProqrj831HiuIjeP5M/nUbh9DwA5v/+PI4+/BKqKf+/uhF97Bd5tWgGgCwqk46vPNNkvsr5XDQWNhoqEE1iMTbsovxDCsep163zEiBGMGGF9c33ggQfOeXzbtm1p27at7ee4uDg2bNjA5s2NqzdoTx5hobR+/AESnnudw4+/iEdYCClzF1Cenol321Z0fu9lV4fo1vx7dKV4935y/9pI0MCLMBeXYCmvQOvrc8a8wfCRl5O/dhPF/9hFp3plb1jVat6myKsqUTI0cKW0uawMU1ExGk8PdEEBdozMPXhWVR8o2LoLi8mERqcjb611JDCwf+86z/Xr3gmtrw+GEyfJ+99Gh1YiUC0WTrz1MQBxD0whZMjAsx4bMmQgybPmkjZ/CbF3TcS3U3vKjiex64Y7qczNR9HpiLnjFtK/t9YQjb71Rmviqiioqkrx3gN4t4mz1aNtijQ+3uhbt6TyxEnKDxzB2w1ruQoh3INTFgOdOHGCNWvWMHjwYGdcrt5iJ0+g1b/vBYuF+DumkffnerQ+3vRe+LmsMj+H4MEDAChLsK64to3KVd0mP513G2vxdsPJZNtjpqJiSg8dRePpQcR1Vzk6XIcJqlrAk79h6zmOrF3eWuuXL9/OHZrs6FZddBFheLeOw1xcQsm+Q5QmnCC1apV52DV1z+3TensTe89tAOSuXtvoWAynUijccWbtV7CujC/auRd9SDBx0+6us53goZdY73aoKgemPsHR599g+8hbqMy17hClmkykfvU9FqOR8OuuouPrz9n+bRVFIaB39yadZFbzvsR627/098b/2wghmi+HLgYaMWIEe/fupby8nDvvvJMXXqi7bE1CgvO3q1PHXoXnpm2U79gLikLQMw+RXFoMCfXfm9sZztU3WVlZDrm9qCgKJpPpjMfLzdYi3IWJJ0lISKDkV+v8TDUq4oxYTar1/JLjSbbnyg9YC7hrW7bg+KnG1aG0h4b+7ql6BXQ6yjOyOLr/AMo5FodUJqeh6HXooiIAKFxvXQxFj84u+f13Bk2PTpB0isNffkfp0lVgseB9+SCy/L3JOsdrNrayzoHN+P0vuP1GFF3DtmUsP3CEnOkzUI3leA8fTNC0KWirpn+oZjMZz70OgM+EMSRlZ0J2Zp3tqUMGwO//o/TIcUqPHAdA37k9YW88S9lvf1Hy43/xGtgXj0fu4XhiYp1tQeP+ftPSGj5tozEsHVsDUHroKBUuisEZauvfs70vivNT/Z7nqM+vC5WiKEREnPlZ7CgdOnSo83mHJppfffUVJSUl7N+/nxdeeIEPPviAxx577KzHnytYR2m38AsyFi8joFc3/Ht2dUkMdUlISDj3P6RO57BEs02bM7fDK/cPJBsgN58OHTpwJGshBUDsyOG0/EesllatydBoMGfnEucXgGd0JInL/yAbCOnZ1WX/7tXq0791yQkLoTwji7jgELxaRJ/1uIwlyzn08HNovLzoNutNQq8axsHCEoqBFn17Ee3ifnCEhIQEWo8ayYHlqyn92bpwRB8WQv8579WryoClVWu2vDub8tR0IsvKz6gQUV/735qFWjWX0PDnRip37eei337AOy6WkgNHSM3KwTMqgj5PP1KvPcbV9u1JMatkLf+dgN7dCRrYj9ArL0PjoYf+F8ELT5xXfA39+01LSyMmJua8z7MHi38AyYCSmkl0RGSDvwS4s7P179neF0X9nf6+66jPrwtV9RchV3+2VnPorfPY2Fg6d+7MTTfdxIsvvsibb77plt8CtV6etLj9ZrdMMt2VR3goik5HZW4+ZoPRdtvQI/LMBS0aD71tYVXGz9ZC19UFr8PPcfu0KaguYF9RRy3NsmOJHH3GuhjOYjSyb8rDnPr0a0oPW79x+nZs5/hAXSTs6uEEXtwXsJYH6v7F+/UuZaXx0BNctSVjyYGzb2NaF1NpGXnrrXtz9/phNj7t22AqKGTfXY+Q9d/VnPz4SwCCBg+oV5IJ1jfylvfeTr9l8+jw8lOEX3ulNcm8gGj8/dC1iEKtqMCwaburwxFCuCmnFWy3WCyYTCbMZrOzLikcSNFqbbv+5P1vIxVVuwR5hNY+tzWiaqVu/vqtVBYUYUg6hcbTg9CrLnNOwA6kD7POS63es7w2Wb+uxlxmIOiS/kTdYq0beeL1Dyg9fAyNlxc+zTjR1Oh09PzmY/r/vph+v863zWutL98u1i0gG7pLUNq8RZiLS/Dt0pHgIQOtc7CjIig9dJQD900na9lKUBSix1/foPYvZH5V26IaNu90cSRCCHdVr0SzpKSE+Ph44uPjsVgspKSkEB8fT3KydXHHjBkzGDNmjO34BQsW8Msvv3D06FGSkpL4+eefefnllxk7diyenp6OeSXC6cKvs64WL9y+2zaiqT/LNnrBQwaieOjJX7eZkx9b65T6de3UZHYBqounbUQz96zHlB617v4TdfNoOr87A9/O7W3PRYy6Cp1v0ypWf750/n41Crafj+BB/QHIXv476nl+UbWYTLY6nK0fuc9a2zMqggFrfiJ64g1ofX0I6NuTbp++ZVvgJurPq6qqgHFnPKpJBhGEEGeqV6K5e/duhg4dytChQzEYDMycOZOhQ4fy+uvWCfQZGRkknjbhXafT8d577zF8+HAGDx7Mm2++yT333MMnn3zimFchXMKvkzVZKo4/aCtY7nmWWpAeoSHEVBXgTp41F4DIG5rHrks+7VsDULBtV63PqxaLbbWzb6f2KBoNXT96A42XFwAhwx1Xtqc58O/ZFc/oSExFxRhOppzXuXn/20Tp4WN4hIcSelo/64MC6PzODIYe3UK/5d8RMWakvcO+IHh0bIs2NBhTeiZlDay8IIRo3uq1GGjIkCEUFBSc9flZs2bV+Pmmm27ippua3tZq4vxU3zov2LwDgIC+PdGHBJ31+BZ3TSL1m4W2nwMHnN8tVHcVXFVzsXjvgVqfLz1yjPLUdDyjImzzgP26duTidUsp3n+YsGYwfcDRfDu3pzw9k9Ijx/Bp26re5+VXzc2MnjiuyW1x2hQoej3+N15HwZzvKPxqAd4X9ULTxPZuF0I4ltPmaIrmx7tNXI1b5RGj6x4V8m3fhvCqmpkaTw98O7nHirjG8oqxrjQ/22Igw6lUAHy7dqyx24xXi2jCR15e6w40oia/qnmauWvW1fscs8FIxhLrbfOQyy45x9GioXyvGY42NJjKxFPkfzbP1eEIIdyMfMKJBtPodLbb4fD3gp+6tH3qIVpOnczF65Y1m1W6uuBAFA89pqJizAbjGc8bT1lv99a13aKoW+SNowDrto71LYNSsv8wpoIifDq0bbJ7ijcFurAQwl55CrRaSpauxLh7n6tDEkK4EUk0RaO0nDqZmDtuocfcj/CMjjzn8T7tWtP+/x7DK9Y1tf8cQVEUPMLPXuKoel6hV8vm85qdzbdTe3RBAVTm5lORkVWvc4r2WBOewIt6Nctdl9yJV4/OBN5hnS5V8OUCqYkohLCRRFM0ij4wgE4z/++Cn2dYvfK8PLNmEqSqKtm/rQHAr1tnp8fVXCiKgn/3LgCkV60iP5f0hUsBCDzPckqiYQJuGYPG34/yvQco+WWlq8MRQrgJSTSFsAPvNtYFKiX7D9d4vDwtg4qMLPQhwbZFQ6JhWkyZCEDad0vOWebIVFRM6aGjaDw9iLy+eVQ3cHcaP18CJ98CQN57syn87kcXRySEcAeSaAphB0EDrTvfFO2Kr/F46VHrPti+ndrL7dtGChsxDK/YGMpT00n56vs6jy2p2nHJp2M7NOfYf17YT8AtYwi82/qFoGDOdxi21l7ySwhx4ZBEUwg78GoZC0B5RnaNx231Mzu2dXpMzY2i0dD22UcAOPHGf85atxT+Lmvk17VhReJFwwVNHk/g5PGgquS+MwtLLQvkhBAXDkk0hbADj4jat6HMWmadqxZatde7aJyIUVcRMnwIFmM58bc9QNnxpFqPy/n9f7bjhfMF3DoOXXQk5oxsytZudnU4QggXkkRTCDuo3hHp9FXnlspKjCdTQFEIGniRq0JrVhStlh5ff0joVZdhLi1j35R/Yyotq3GMpbKSsmPWncoC+vVyRZgXPI2XJwGTbgCg8KsFqCaTiyMSQriKJJpC2IEuKABFr6tRS9OYnIZqNuMZHYnWy9PFETYfGp2OLu+9gnfrOMqOJ5H+w081ni/YvAOLsRzvtq3QBwa4KErhe83l6GKjMaVnknbHvzFlZp/7JCFEsyOJphB2oGg0eEZGAFB23DqaVppwArDuoCTsSx8SRLsXpgOQPGcelspK23Op3y4Czr1TlXAsjacnwVPvBMCUnEbG/U9RmZLu4qiEEM4miaYQdhIybDAAOavXApC7+n8ABF0it80dIeyqy/Bp15ry1HSyf/0dAFNp2Wn7m9/gyvAE4DN0ILG/fI02IgxzTh75n3zt6pCEm1MrKzHuPUDx0lVYiktcHY6wA52rAxCiuQga2Je07xZTEn8Q+LvEjszPdAxFo6Hl/Xdy5IkZJL0/m7BrriTly/mYS0rx79lVtvx0E9rQYKI+fo3UiVMxbN6BKTsXXXioq8MSbqgyOY3MB5/DnJcPQP4nX6MNDsSjYzvQaTGlZxFwy2h8h1/q4kjF+ZBEUwg78e/RFYCCbbsoO5ZI8e79oCj4de3o4siar8hxo0h8+xPKjiexbdhYKguKAGjz+DQXRyZOp4uOxGfoQMr+2kTp72sJvHWcq0MSbqho0TJbkql4e6EajJgMRkxpmbZjcmYkoA0Pw6uH7LTWVEiiKYSdeLdrTcBFvSnasYetl40FIHBAH1mQ4kBaL0+6z3mP+DunYUxOA6xbToZcPtjFkYl/8h7Un7K/NlFxKMHVoQg3ZMrJo2TZ76AoRH/xLvr2rTFu240pJ4/KpGQUrZaKw8cw7tpHzvNvErPgMzSyyLJJkERTCDtRFIXYKRM4uGOP7bH2LzzuuoAuEIH9e9Pz20/ZfdMUsKi0nj4VRSPTz92NR9WmBcY9+7EYyyVJEDUUL1oGFgveg/vbfle8B/arcYxqMpE++REqT6ZQvGgZgXfc7IpQxXmSd2Mh7Cjs6isIGX4pioeeFndNIqB3d1eHdEEIvKgXA/76hYs3/kqI7CnvlvRt4tC3bYWlsJjyfYdcHY5wIxaDkaIf/wuA/7hrz3qcotMROGUCAIZtu50Sm2g8GdEUwo60Xp70mvepq8O4IPlIGSm3pigKnt07UXniJJUnU/Du39vVIQk3Ydi6Cyoq8ejSAe8Bfeo81mtAb9BoKN97kIpjSXi0b+2UGEXDyYimEEIIp9DHxQJQmZTi4kiEOyn++TeAeq0m1/r74XvVUADK/rfJoXEJ+6hXorlx40YmTJhAly5dCAoKYv78+XUev379eiZOnEinTp2Ijo5m0KBBzJs3zy4BCyGEaJr0VaNPhs3bUc1m1wYj3IKqqpQfOQ6Az5VD6nWOz2WXAFC2cRuqxeKw2IR91CvRLC0tpWvXrrzxxht4e3uf8/ht27bRrVs3vvnmGzZv3szdd9/NI488wuLFixsdsBBCiKbJq093tCHBmLNyMaVnuToc4QaMp1JRS0rRBAeiDQ2u1zle/Xqi8fej8lgS5fuPODhC0Vj1mqM5YsQIRowYAcADDzxwzuOnT59e4+e7776b9evXs2zZMm6+WVaJCSHEhUjRaNC3aYk5L5/KU6noY6NdHZJwsYwflwPg2b0ziqLU6xyNjzc+wy6hZPlqKg4ewatnF0eGKBrJaXM0i4uLCQoKctblhBBCuCFdnHXHJlNV3VNxYSvcugsAv5HDzus8j6qNMMoPHrV3SMLOnLLqfOXKlaxdu5ZVq1bVeVxCghTyPZtz9U1WVhaqqtr9uoqiYDKZ7N6uu5HfPceRvj23xvz9pqU1rYTNHOgHQOHho5Q0gdhr698L5X3R0Y4ePEjBzr0A5IcGUnAevw9qmPU2e9m+w03ub8DRFEUhIiLCae+9HTp0qPN5hyeaW7Zs4d577+XNN9+kX79+dR57rmAvVAkJCef+h9TpHJZotmnTxu7tupP69K9oGOnb+mno329aWhoxMTEOiMhxDN27kgXocwuIcvPYz9a/F8L7oqMlJCTgsy0e1WBEFxtDi+5dz+t8NTKSZG8v1Jw8Ij296z2/80JQ/UXIXd57HXrrfPPmzdx8880888wz3H333Y68lBBCiCZA19KauJlOpbo4EmEvqsVCwZYdpC9aSnl65rlPwLraPHOJdX5m0ORbzvuailaLR+f2AJTLtqZuzWGJ5saNG7n55pt58skn67WASAghRPOniwwDDz3m3HwsZQZXhyMaSVVVDk9/kd033sXhR59n6+U3kL7g5zpH6FVVpeDDLyjavQ+tvx8+lw5o0LU9u3YCoGzt5gadL5yjXolmSUkJ8fHxxMfHY7FYSElJIT4+nuTkZABmzJjBmDFjbMevX7+em2++mSlTpnDLLbeQmZlJZmYmOTk5jnkVQgghmgRFq7WtNq9MllHNpsxsLOfg1CfJWLQUAL+unTAXl3B4+oukfHn2etsZi5ZS+stK0Gjo/PZLaPx8G3R9v2uHA1D250bU8ooGtSEcr16J5u7duxk6dChDhw7FYDAwc+ZMhg4dyuuvvw5ARkYGiYmJtuO///57ysrK+Oijj+jUqZPtv8svv9wxr0IIIUSToYuOBMCcKYMPTVnynG/JWr4Kra8P3T9/j4t+X0S7563lDY+99DbF8QfPOOfkx19y+LEXAGjz+ANEjB7R4Ovr41qgb98ataKCktVrG9yOcKx6JZpDhgyhoKDgjP9mzZoFwKxZs9i3b5/t+FmzZtV6/OnHCCGEuDBVJ5oVR4+7OBLRUGXHk0h6/zMAunz4GuHXXomiKMTdfyct7poEqsrx196vcU7+5h0kvvUxAH4Tr6fVQ/c0Oo7qskiFcxehVlY2uj1hf7LXuRBCCKfyHtAbAOOu/a4NRDRY5i8rUCsqCbvmCsKuHl7juTaPTUUX4E/+hq1kr/yT8qwckj6cQ/xtD6CazbT81x0E/et2FE3jUxD/W8agi47EnJlN4Xc/Nbo9YX+SaAohhHAqfdtWAFSmprs4EtEQ5rIyUr76AYCYiePO2NFHHxxIy/vvBGD/3Y+wqc9wEt/6GIvRSPTEG2j33KN2i0XRaAj+t7WqTckvKzEXl9itbWEfTinYLoQQQlTThoeChx5LXgGWMgMaH29XhyTOQ87qdZgKCvHr3pmQywfXekyrh+6hIjObtO9/RNHpCOjdg9aP3EfwpRfbPR7vwf3Rd2hDZUIiKdfehuLliS4mEn3LGHQxUQRMGoc2KMDu1xX1I4mmEEIIp1I0GvQxUVQmJVOZmIxnt46uDknUk6qqJL7zCQCRN1x31tvfikZDx9efo+Przzk8JkVRiHjtadLvfwpLXgGqsZzKE6eoPHEKgJIVa4j67C1btQPVZCbvP19QmZCI/y1j8L18kMNjvJBJoimEEMLpPPt0pzIpmbINWyXRbEIKt+3CcOIkHuGhtLhzvKvDsdFFR9Ji/ieU7zuE4uONYeN2yo8cp3z/YSyFxRR88T3hL1lXxJet30LJz78BUL7/MGXDLyX4obvQhYW48iU0W5JoCiGEcDrvfj0p+fk3Kk+cdHUo4jwkvv0pAJE3jUbr7eXiaGrS+PnifclFAHj16gaAKSuH1IlTKVuzntRjifhc0o/SPzfUOK/szw1UHEsk5psPUXSSFtmbLAYSQgjhdDpb0fY0F0ci6qsiL5+CrTtR9Dq7lCZyBl1EGEF3TQTAdDKFogVLMWfloouOJO6PRUTNfgttaDCmU6mULPvdxdE2T5JoCiGEcLrqRNOUmoGltMzF0Yj6SP1mIVgsBA8ZiD6w6SyuCbx1HC1+/oqwFx7Fo3N7PPv2IHzmMyieHnh27UjIo/cBUPj9z7LDkANIoimEEMLpNJ6eeHTrBBYLJSvWuDocUQ9FO/YAED3hBtcG0gC6sBB8r7qM6M/fIerDV/Bo19r2nPelA9DFWGtxFlfN3RT2I4mmEEIIl/AfOxIA4469Lo5E1EfJ4WMA+Hfr7OJI7EvRagmeNgWAgm8WYsrMdnFEzYskmkIIIVzCq19PAAxbd8lcTTdXHH+QiowstD7eeMW1cHU4dud96QC8+vdCLSkjffIjVBxPqvG8arFQtn4rBV/9QMaDz5Iy7m4Kvl6AWiHbXp6LJJpCCCFcQhcRhs/wS8FsoWztZleHYzemomIyfvwVQ1Kyq0Oxm8xlKwHranN7bB3pbhSNhtAnHkAbEoylpJSs6TMo/WsT5QeOkP/5fNJufZDsZ2dS+PVCyvcexJydS+FXC0i/61EMO/bKPut1kHX8QgghXMZn2CDK/txA8eJf8b95FBpPT1eH1CiVBUXsvG4ShqRTKDodHV55mhZ33OLqsBpFVVXy120BIGzk5S6OxnF00ZHE/PApGQ88Q+XxJHJeeOsfB+jwu+5KtKFB6FpEUzB7HpUnU8h69EV0LaIIf/VpPNq3dkns7kwSTSGEEC7jM2QAupYxmJLTKN+1z1YHsak69tJbGJKsO9KoJhNHn3mVvHWbafPYVPy6Ns3C9Hn/20jJgcNo/XwJ7NfL1eE4lMbHm8h3X6Ro4VIMO/ZiKSxG37olPpcOwPuSfuiiImzHevXtQeGXP2DYvgdTagbpUx5B3yoWv+uvxn/MSBQPvQtfifuQRFMIIYTLKDodvsMvpfCbRRQtXu6URNOwZScVR08QMOkGuxbozt+4jYzFywDo88s3FO3Yw/FX3yfntzXk/LaGXj/MJmToJXa7njNYKio5MfM/AMTdPxmdv5+LI3I8bWgwwQ9MJvgcx+nCQgh9ahqW8nJyXnoXw8btVJ5MIf/DLyicuxDPnl3xHzsS74v71nq+Oa+Awvk/YdyxB48uHQi+73a0IUF2fz2uJommEEIIl/K7/moKv12McfteLCWlaPx87dq+qqoYt++hdPU6ND7eFP+0AgBzUTEhD95lt2scffY1AFo9dA9B/fsQ1L8POn9/Tn70BcaUNPZOup+LVvyAf8+u52zLkHgKXVAAHiHnSncc69jL71By4DC6oECiJza9skbOoPH0JGLms1jKDBi27aZg9neYUtIwrN+KYf1WfK8ZTuj0+1E8PWznVCSeImv6DMzZuQBUnjiFYfNOIt76Pzw7tXfVS3GI5jejVwghRJOiCwvBo1M7ACoSTti9/fwPPrcu7lj5ly3JBChZuorKU6l2uUbh9t2UHUtEHxpM68em2h6Pue0m+q/5Eb9unUFVib/zQfLWbkJV1VrbSV+4lG3DrmfrkNFs7HEZx159D4uLioifeOM/pH79A4peR6/vPsXztNvG4kwaH298hw0i5tsPifzPq/iNHQlaDaW//cmpkRPJ/r83qTieRP6c78ic9izm7Fz0HdoQ8sRUtFHhWPIKyHr0JUzpma5+KXYliaYQQgiXsyWaR47X6/jKU6kUfL2QimNJlG3cTunqtbUmb+aiYop/WVnjMf/xY/Aa2BfVWE7Oax+eNek7H0nvfQZA1I2j0fxjbp7Oz5d+v84naFB/KrJy2DvpfrZfdfMZq9JTvpzP4ceep+xYIhov66Ko5Flz2TnmNioLixod4/ko2LqTkx9/CUDnt18ioE8Pp16/KVP0erz6dCf08alEvv8yuhZRYDZTtnYz6ZMfoWjeEizFJXj160nUx6/jP2YkMd/8B49unbAUl5B+35MY9xxw9cuwG0k0hRBCuJxHR2uiWV6PRDP/s29Ju3UahV/9QPqUR8h++jVyXn4fw4ZtZxxbtGApWCx49etJq/W/0HL1QkIevIvwF6ejDQmm4uBRjFt3NSp2Q1Iy+Ru2ovH0IO6hu2s9RuOhp8fcj2j92P3og4MoPXSU469/YHs+6YM5JLzwJgBtnniQIQc30v3LD9CHhVCy/zBHn361UTGeD9Vstk4DUFWiJ40j6uYxTrt2c+PVpzstFnxGi8Vz8OxrTda14aGEz3yWiPdeQuPjDVhHQyNmPmNNNgsKyZz+EmVVK/2bOkk0hRBCuFx9RzRNGVkULV4OgFL1IV0tf9Y3qBaL7eeSlX9RNG8JAAETxgLYRgo1fr74jRkBgGFL4xLN1O8Wg6oSMXpknXMqdb4+tJn+ABetXACKQs7KPyncsZes/64m8e2PAWj/4hO0fuQ+NJ4ehF89nL4/zQWNhqxlKynYvrtRcdZX8pxvKT18DH1wEB1efsop12zudFERRH7wMjELP6PFgs/wuXTAGfVItcFBRH34Cj5XDIGKSrJfeJvSP9a5KGL7qVeiuXHjRiZMmECXLl0ICgpi/vz5dR5vNBqZOnUqgwYNIiwsjOuuu84uwQohhGiePNrGgV6HKTkNS2nZWY8rWvIrVFTiNaAPcat+oOWK74j5/lO0IcGYktMo/vG/AFgMRgq++B4A/1vG4D2w3xltefW3luopXbMBS5nhvGMu3LmXo8+/QfKsuQDE1LNepldsDC3uuAXVbGbX2Ns5cN90AFpPn0rL+26vcaxPu9bETpkAQNK7s7A4uDC44VSKbRpAx5n/h9bb+xxniPpSFAV9TFSdZY8UTw/CXnyMgEk3gNlMzoz3yHrq1Vp/Py0lpRQtXk7ue7Mp27QDS2kZ5QePUpnmXnM865VolpaW0rVrV9544w286/FLZzab8fLy4r777mPEiBGNDlIIIUTzpuj1eLRrDUDJijW1HmMpM1Dy6x/AaSOU/n7oW8YQcPuNABR8+QNqeQUlS1dhzsxGFxtD8AN31tqeZ/fO6NvGYSkopHzf4XrHqqoqhx75P3aNuZ3Ur6zJbPDQSwjo27PebbSf8STh115h+zn82itp/ci/aj029p7b0Hh5kr9+CwcfeBLVbK73dc5X+vc/YS4zEDR4AOGjrnLYdcTZKYpC0P13EDT1ThRvLwybdpD52EuoJpPtGMOWnaTd8W/y//MlJT//RvZTr5J89SQy/vUkaZMfpvT3ta57Af9Qr0RzxIgRvPDCC4wdOxZNPbae8vX15f3332fy5Mm0aNH89kQVQghhf/7XXwNAyW9/1vp80eLlqKVlaMNC8OrVrcZzATeNQt+uNWppGUULl9q2tAycMh5Fq621PUWjwatqkUvF0fotQir++TdODr3BVi9TFxRA4MV96fLBqyiKUq82ADR6Pd3mvEefH7/ikm2r6P75e2fd2tE7LpZe33+G1s+X7BVrSF/4S72vcz5MJaWkzlsMQOtH/nVer0fYl6IoBE66geg5b6MJDqTiwBFyZ35E+eFjZDz4HFlPvII5OxePLh3wGzMCbXQEircX2vBQ/EddiVdf91m8JXU0hRBCuAXfq4aS9+HnVCYkYkrPRBcdaXvOXFBE8YKlAAQ/fE+ttx+Dp95J1uMzKPjcOr1L8fLEe2DtxbKreXS21iw0bN1F4O031XlsyW9/kvfebNvPbZ99hFbTGl6HU1EUggbWr0B90MX96PTm8xyc9jQn//M50RPH2T0RLNiyE1NBIX49uhDUxHdoai70rVsS/sqTZD3+MqW/r7WNVCpengROHk/AhLFnfJFSFAXTaaOfruZWiWZCQoKrQ3Bb5+qbrKwsu5To+Cd3+4V1FPndcxzp23NrzN9vWlqanaNxsd5dYfMu0n9djfa64baHzctXYykpRencjoL2cRTW8rrV2AiUPt1Qdx8Aby80D04ms6QYSorPejm1vXVuaPneg6QmHEPx9anxfHX/qpWVmL5eAIDm6mFEP3ofFZ6eTv39Vru0QxMchDE5jYO/rcajQxu7tl+w4ncAlO6dOHbsmF3brk113znq86vZCA9G8+yDmL9aCDn5aC7pi2bCGEr9fCnNPHM+pqIoREREOO13s0OHDnU+71aJ5rmCvVAlJCSc+x9Sp3NYotmmjX3fzNxNffpXNIz0bf009O83LS2NmJgYB0TkOqVXXUbO5l1o/txE1B03o/H0RLVYSF1pHckJmzIRn9jYs56vfvAKlYmn0EVH2krHnEtaXCyVx5MIq7TgeVp/nt6/xb+uJi89C01IELHTp9Kua5dGvMqGs1x7Benzf8Tn8HHaXGu/NRAWk4mNK/8CoN240QQ7+O/29PcGR31+NSsxMXD5kHodWj1A5C7vvVLeSAghhNvwurgvGj9fTClplCxfDVi35zPn5KENC8H7kjNXj59O0WjwaNe63kkmgD7OmkxWJtccJVVT0km//ynSbn+IvDc/ASBo8ng03l7n85LsKnykdZQ3Z9Vfdm23LOEEpsJivGJjCB7U365tiwubJJpCCCHchjbAn6D7bgOgPP4gYK2PCeDZu5tDFqjoW7UEwLhrn+0x1WLB/Om3VBw4QmXVDj66uBb4XjO81jacJWjwALQ+3pTsP0zhjr12a7dwm7VGp3/v7nZrUwioZ6JZUlJCfHw88fHxWCwWUlJSiI+PJznZ+sc3Y8YMxoypuXPA4cOHiY+PJzc3l9LSUtv5QgghRF08e1pvSxt3xFOZmo6xKgkKmjzeIdfzGX4pAIbNOwDrzjh5H3yOevwkaDQE3HEzAROuJ/LDV2wF311F6+VJ9CRrKadTn3xpt3bTvrMWtg+57BK7tSkE1HOO5u7duxk9erTt55kzZzJz5kwmTpzIrFmzyMjIIDExscY5N998sy0RBRg6dCgABQUFdghbCCFEc6Vv2wp9u9ZUHk8i67GXAPAe3B99q7PPzWzU9VrHonh7YckvxJxfSMFXP1BStT962IuP4VuViLqLuPvvJOXL+eT8/j8yl/5G5NhrGtVe0Z79lBw8gsbLi8hxo+wUpRBW9Uo0hwwZUmeCOGvWrDMe27dvXy1HCiGEEHVTFAWv3t2oPJ6EqWqXk6C7Jjr0eh7tWlG+/wip4/+FajACoL3/drdLMgE8oyNpM/0BEt/5hGMz3iHsqsvQ+vic+8R/UFWVkx/MIfEd6/zT0OGD0bp4xFY0PzJHs5nw9vZGURS7/1efnaCEEI3jqL/fpvzf6fUv9W1b4dmpnUOvFzxtCpqgAFuSGfzgXWiHD3Lb98VWD9+Lf8+uVGRmk/Xr6ga1kblkuS3JjJ40jk5vvmjPEOtNfv/t/LfjBr+fp3Or8kai4aKiolwdghCigRr692symZpt+TG1dWv8i0rJXrGGDq88TYCjX2ebNnS4fCiFO/eiDw4ioFc3EhIS3LZ/FY2GyHHXURx/kIKN24i+ZWy9zzUVFZO3fgvHZrwDQKuH76Ptkw86KtRzks8v+ysuPnvtWGeTRFMIIYTbURSFuPsnE3f/ZKddU+fvR+iwwU67XmMFD74YgPyN21BVFUX5e0W+arFw7KW3SJ23GJ92bWjzxDTCR15O5tLfOPrMq5gKrYlIyOWDaTN9qkviFxcGSTSFEEKIJsi3c3v0IcGUp2diSDyFT9tWtueOv/oeKV9+D0DpoaPsv+thPKMiKM/Isp7bqR0RY64h9p5bz7oXvBD2IHM0hRBCiCZI0WgIqiqunvbdYtvjeWs3kTz7WwC6fvIGcQ/eDWBLMltPn0r/P36k9SP3ofPzdXLU4kIjI5pCCCFEExU59mqyf/2d5NnfEjSwH6FXDSN5jjXJjL33diKvv5bI6yF4UH+yf1uDf8+uxFTV4RTCGSTRFEIIIZqo8GuvJHriDaT/8DP7pjxse1zr403c/Xfafg65bBAhlw1yRYjiAie3zoUQQogmrONrzxF+7RW2XYsUvY6un76FZ1SEiyMTQkY0hRBCiCZN4+lB98/fR1VVinbvwyMsBO84x+yiJMT5kkRTCCGEaAYURSGwb09XhyFEDXLrXAghhBBCOIQkmkIIIYQQwiEk0RRCCCGEEA4hiaYQQgghhHAIpaCgQHV1EEIIIYQQovmREU0hhBBCCOEQkmgKIYQQQgiHkERTCCGEEEI4hCSaQgghhBDCISTRFEIIIYQQDiGJpoO99957XH755bRs2ZJ27doxfvx4Dh48WOMYVVWZOXMmnTt3Jioqiuuuu45Dhw7VOKa8vJwnnniCtm3bEhMTw4QJE0hNTa1xzLFjx5g0aRJt27YlNjaWK6+8kj/++MPhr9GV7NW/c+fOZdSoUcTFxREUFMTJkyfPuFZBQQH33XcfcXFxxMXFcd9991FQUODIl+dyzurfkydP8uCDD9KrVy+ioqLo1asXM2bMwGAwOPw1uoozf3erGY1GBg8eTFBQELt373bI63IXzu7fNWvWcNVVVxEdHU1cXBxjxoxx2GtzB87s3wvts80efZufn88TTzxB//79iYqKolu3bjz22GPk5eXVaMcZn2uSaDrYhg0buPvuu1m1ahXLli1Dp9Nx/fXXk5+fbzvmww8/5JNPPuHNN9/kzz//JDw8nBtuuIHi4mLbMc888wzLly/nyy+/ZMWKFRQXFzN+/HjMZrPtmPHjx1NeXs7SpUtZt24dAwcOZNKkSSQmJjr1NTuTvfq3rKyM4cOH8/TTT5/1Wvfccw/x8fEsXryYJUuWEB8fz7/+9S+Hvj5Xc1b/JiQkYDabee+999iyZQtvvfUWCxYsqPPfo6lz5u9uteeff54WLVo45PW4G2f276+//spdd93F+PHjWbduHatXr+a2225z6OtzNWf274X22WaPvk1PTyc9PZ0ZM2awadMmZs+ezaZNm7j77rtrXMsZn2tSR9PJSkpKiIuLY/78+VxzzTWoqkrnzp259957efzxxwEwGAx06NCBV155hSlTplBYWEj79u355JNPuOWWWwBISUmhR48eLFmyhCuuuILc3FzatWvHsmXLGDp0KAAmk4mIiAi+/vprxo4d67LX7EwN6d/T7d69m8svv5y9e/fSqlUr2+NHjhzh4osvZuXKlQwcOBCAzZs3c80117B9+3Y6dOjgvBfpQo7q39p88cUXvPbaa832w+SfHN23//3vf3nllVf45ptvuPjii/nrr7/o06ePU16bO3BU/5rNZnr16sUTTzzBnXfe6dTX5E4c1b/y2db4vq32+++/M378eE6ePElAQIDTPtdkRNPJSkpKsFgsBAUFAdZbhpmZmQwfPtx2jLe3N4MGDWLr1q0A7Nmzh8rKyhrHxMbG0qlTJ9sxISEhdOrUiYULF1JSUoLZbGbu3Ln4+flx8cUXO+8FulhD+rc+tm3bdkZfDhw4EF9f3/Nqp6lzVP/Wpri42HadC4Ej+zY1NZXp06czZ84cvLy87Bl2k+Go/t2zZw8pKSl4eHgwdOhQOnbsyA033MDevXvt/RLcmqP6Vz7b7Ne3xcXFeHp64uPjAzjvc00STSd7+umn6dGjBwMGDAAgMzMTgPDw8BrHhYeHk5WVBUBWVhZarZbQ0NCzHqMoCj///DOHDh2iZcuWRERE8MYbb7BkyRKioqIc/bLcRkP6tz6ysrIIDQ1FURTbY4qiEBYWdl7tNHWO6t9/Sk5O5qOPPjrjNk9z5qi+NZvN3HvvvUybNo2ePXvaL+AmxlH9m5SUBMBrr73G9OnTWbRoETExMYwaNYr09HT7BN8EOKp/5bPNPn1bUFDAa6+9xh133IFOpwOc97kmiaYTPfvss2zZsoV58+ah1WprPHf6PzRYJ/r+87F/Ov0YVVWZPn06ISEh/Pbbb6xZs4axY8dyxx13kJaWZt8X4qbs3b//VNvxDWmnqXJ0/1bLysrixhtv5PLLL2fatGkNjrcpcWTfvvvuu+j1eh588EG7xNoUObJ/LRYLAI8//jhjx46ld+/efPjhhwQGBrJw4cLGB98EOLJ/L/TPNnv0bWlpKRMnTiQ6OpqXX365zjbqaqehJNF0kmeeeYYff/yRZcuW0bp1a9vjkZGRAGd8e8jJybF9W4mIiMBsNpObm3vWY9atW8fKlSv54osvGDhwIL179+bdd9/Fx8eH+fPnO/CVuYfG9G99REREkJOTg6r+PaVZVVVyc3PPq52mytH9Wy0zM5PRo0fTpUsXZs+efUEk8Y7u27Vr17J+/XrCwsIIDQ2lb9++AFx55ZXce++9jX8Bbs7R/VvdTqdOnWyP6XQ62rZtS0pKSiMibxoc3b8X8mebPfq2pKSEm266CYCFCxfWmDrjrM81STSd4KmnnmLJkiUsW7aMjh071niuVatWREZG8tdff9keMxqNbN682TZvonfv3uj1+hrHpKam2ibygnXlHoBGU/OfVKPR2L5xN1eN7d/6GDBgACUlJWzbts322LZt2ygtLW3284Sc0b8AGRkZjBo1io4dO/Lll1/abu80Z87o208++YQNGzawfv161q9fz+LFiwH4/PPPeemll+zyOtyVM/q3d+/eeHp6kpCQYHvMYrGQmJhIy5YtG/8i3Jgz+vdC/WyzR98WFxdz0003YbFYWLRoEX5+fjXacdbnWvN/J3exxx9/nIULF/Ldd98RFBRkm1vh6+uLn58fiqIwdepU3n33XTp06ED79u1555138PX1tX0LCQwM5Pbbb+eFF14gPDyc4OBgnnvuObp168awYcMA6y9McHAw06ZN48knn8Tb25tvvvmGpKQkRo4c6aqX73D26F+wjqRlZmZy7NgxwLrKvLCwkJYtWxIcHEynTp248sorefTRR/nwww9RVZVHH32UkSNHNusV587q3/T0dEaNGkVUVBQzZ86sMXofFhZ2xi2j5sBZfXv6SEh1+wBt2rRp1qWOnNW/AQEBTJkyhTfeeIMWLVoQFxfHnDlzKCwstFUJaY6c1b8X4mebPfq2uLiYcePGUVxczPz58ykrK7Ml7cHBwXh4eDjtc03KGznY2VbNPvXUUzzzzDOAdaj6jTfeYO7cuRQUFNCvXz/eeecdunbtajveaDTy/PPPs2TJEoxGI0OHDuXdd98lNjbWdszu3bt55ZVX2L17NyaTiY4dO/Lkk0822z9GsF//zpw5kzfffPOMdj755BNuvfVWwFoA96mnnuK3334D4JprruGtt95q1iujndW/8+fPP+t8zPqUQmqKnPm7e7qTJ0/Sq1evZl/eyJn9W1lZySuvvMKCBQswGAz07NmT1157jd69e9v9dbkLZ/bvhfbZZo++Xb9+PaNHj661neXLlzNkyBDAOZ9rkmgKIYQQQgiHkDmaQgghhBDCISTRFEIIIYQQDiGJphBCCCGEcAhJNIUQQgghhENIoimEEEIIIRxCEk0hhBBCCOEQkmgKIYQQQgiHkERTCCGEEEI4hCSaQgghhBDCIf4f05SROM2wZQoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,3))\n",
    "ax.plot(decrease_period['Time'], decrease_period['rolling_mean'],\n",
    "            color='#af0b1e',linewidth=2)\n",
    "ax.axvspan(xmin=13950, xmax=14140, ymin=0.09,color=\"grey\", alpha=0.3)\n",
    "ax.axvspan(xmin=15950, xmax=16850, ymin=0.09, color='grey',alpha=0.3)\n",
    "ax.axvspan(xmin=17550, xmax=18250, ymin=0.09, color='grey',alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1 The impact of the Global Financial crisis in exchange rate EUR/ US dollar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us focus what happen between 2006-2010.\n",
    "In Appendix we left some calculations we made to get some values to create this plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "financial_crisis=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2006) & (euro_to_dollar['Time'].dt.year <= 2010)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Create the plot area and structure\n",
    "fig, (ax1, ax2) = plt.subplots(figsize=(8,3), nrows=2, ncols=1)\n",
    "axes=[ax1, ax2]\n",
    "\n",
    "\n",
    "#First version of plots - like a shadow of every elements\n",
    "for ax in axes:\n",
    "    ax.plot(financial_crisis['Time'], financial_crisis['rolling_mean'],\n",
    "            color='#af0b1e', alpha=0.1)\n",
    "    ax.set_ylim(1.12, 1.6) #use same y axis for both graph\n",
    "    #ax.set_yticks([1.0, 1.2,1.4, 1.6])\n",
    "    ax.set_yticklabels([])\n",
    "    #ax.set_facecolor=\"#dbd7d7\"\n",
    "    ax.tick_params(colors='#948484', which='both', labelsize=10)\n",
    "    ax.grid(b=True, color=\"grey\", alpha=0.1,linestyle=\":\")\n",
    "    \n",
    "\n",
    "    \n",
    "### Highlihting the rate growing before crises\n",
    "ax1.plot(financial_crisis['Time'][:660], financial_crisis['rolling_mean'][:660], color='green', linewidth=2.55, alpha=0.8)\n",
    "#ax1.xaxis.tick_top()\n",
    "ax1.set_xticklabels([])\n",
    "ax1.text(x=13000,y=1.12,s=\"r*=1.1826\",size=10) #check appendix\n",
    "ax1.text(x=14000,y=1.6,s=\"r*=1.599\",size=10) #check appendix\n",
    "ax1.text(x=14250,y=1.65,s=\"Positive rate evolution\",size=11,color=\"green\", weight=\"bold\")\n",
    "ax1.text(x=14250,y=1.55,s=\"until middle 2008\",size=11, color=\"green\", weight=\"bold\")\n",
    "\n",
    "\n",
    "### Highlihting the floated rate after crisis\n",
    "ax2.plot((financial_crisis['Time'][660:1300]), (financial_crisis['rolling_mean'][660:1300]),color='#af0b1e', linewidth=2.5, alpha=0.8)\n",
    "ax2.text(x=13030,y=1.45,s=\"The rate drops and\",size=11, color=\"#af0b1e\", weight=\"bold\")\n",
    "ax2.text(x=13030,y=1.35,s=\"grows after 2008\",size=11, color=\"#af0b1e\", weight=\"bold\")\n",
    "\n",
    "### Highlihting the peak of the crisis\n",
    "ax1.axvspan(xmin=13950, xmax=14140, ymin=0.09,\n",
    "           alpha=0.3, color='grey')\n",
    "ax2.axvspan(xmin=13950, xmax=14140, ymin=0.09,\n",
    "           alpha=0.3, color='grey')\n",
    "ax2.text(x=14900,y=1.26,s=\"r*=1.1.2939\",size=10) #check appendix\n",
    "\n",
    "### Highlihting the moment of the dratic rate drop\n",
    "#ax1.axvline(x=14090, ymin=-5, ymax=1.3, c=\"red\",linestyle=\":\", linewidth=2)\n",
    "#ax2.axvline(x=14090, ymin=-3, ymax=2, c=\"red\", linewidth=2,linestyle=\":\")\n",
    "\n",
    "\n",
    "#Title and subtitle:\n",
    "ax1.text(x=13000,y=2.05,s=\"EUR/USD rate drastic drop after the financial crisis\",size=18, weight=\"bold\")\n",
    "ax1.text(x=13200,y=1.90,s=\"The global financial crisis has a big impact on the exchange rate. \",size=13)\n",
    "ax1.text(x=13000,y=1.80,s=\"The rate started to float after 2008 and never reach again its highest value 1.599\",size=13)\n",
    "\n",
    "#r description\n",
    "ax.text(x=13000,y=0.9, s=\"*r means exchange rate EUR/USD\", size=11)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "ax2.text(13000,0.8,'©Mariana Lourenço' +' '*59 +\"Source: European Central Bank\", color=\"#f0f0f0\",backgroundcolor=\"#4d4d4d\", size=12)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2 The impact of the COVID-19 Pandemic in EUR/USD rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#2016-2019:\n",
    "before_pandemic = euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2016) & (euro_to_dollar['Time'].dt.year <= 2019)]\n",
    "\n",
    "#2020:\n",
    "after_pandemic =euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2020) & (euro_to_dollar['Time'].dt.year <= 2021)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x108 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import matplotlib.dates as mdates\n",
    "\n",
    "fig, ax= plt.subplots(figsize=(9,1.5))\n",
    "\n",
    "ax.plot(before_pandemic['Time'], before_pandemic['rolling_mean'],color='lightblue')\n",
    "ax.plot(after_pandemic['Time'], after_pandemic['rolling_mean'],color='green' )\n",
    "#ax.set_ylim(1.1, 1.3) #use same y axis for both graph\n",
    "#ax.set_yticks([1.1, 1.2,1.3])\n",
    "#ax.set_yticklabels([1.1, 1.2,1.3])\n",
    "ax.tick_params(colors='#948484', which='both', labelsize=12)\n",
    "ax.yaxis.grid(False)\n",
    "ax.set_yticklabels([])\n",
    "ax.axhline(1.22,c=\"black\", linewidth=2.5, alpha=0.8)\n",
    "ax.text(x=16530,y=1.21, s=\"r*=1.225\", size=11)\n",
    "ax.annotate('Mar-2020', xy=(18400, 1.08), xytext=(18500, 0.95),\n",
    "            arrowprops=dict(facecolor='black', shrink=0.05),\n",
    "            )\n",
    "\n",
    "#Title\n",
    "ax.text(x=16600,y=1.45,s=\"COVID-19 Pandemic rises hopes that the euro will valorize \",size=18, weight=\"bold\")\n",
    "ax.text(x=16750,y=1.40, s=\"Checking evolution of EUR/USD rate, since first cases in Europe (Mar/2020)\",size=14)\n",
    "ax.text(x=16800,y=1.35, s=\" the rate grew, and in Jan-2021 reached the highest values since 2018\",size=14)\n",
    "\n",
    "\n",
    "#r description\n",
    "ax.text(x=16530,y=0.92, s=\"*r means exchange rate EUR/USD\", size=11)\n",
    "\n",
    "#Foot signature/source of graph:\n",
    "ax.text(16530,0.88,'©Mariana Lourenço' +' '*78 +\"Source: European Central Bank\", backgroundcolor=\"#4d4d4d\", size=12,color=\"#f0f0f0\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we explore the dataset with exchange rate EUR/USD information between 1999 and 2021 and we related its evolution with some historical facts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After explored and cleaned the data, we selected 2 of those historical facts, and we created one graphic set per event, in order to explain to audience the impact of each event in exchange rate. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first selected event was the global financial crisis in 2008. Based in the graph created, we could easily to understand that until 2008, exchange rate was growing, and drastic drop after started the global financial crisis. After 2008 we see that exchange rate floated and never reach again the highest value 1.599."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we selected the Covid-19 pandemic event. Based in the graph, we can understand that after the first cases appeared in western world the exchange rate EUR/USD reversed the negative trend and equaled values from two years ago."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We focus our work in exchange rate EUR/USD. However, as we saw at the beggining, our dataset has much more information and we can do similar work with other currency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Apendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auxiliary calculations for graph 5.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "before=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2006) & (euro_to_dollar['Time'].dt.year <= 2008)]\n",
    "print(before.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "before.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "before[before[\"rolling_mean\"]== 1.5743]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "before.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "before[before[\"Time\"]== \"2006-01-02\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "euro_to_dollar[euro_to_dollar[\"Time\"]==\"2011-12-30\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Auxiliary calculations for graph 5.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandemic=euro_to_dollar.copy()[(euro_to_dollar['Time'].dt.year >= 2020) & (euro_to_dollar['Time'].dt.year <= 2021)]\n",
    "print(before.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandemic.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandemic[pandemic[\"rolling_mean\"]<  1.085]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandemic[pandemic[\"US_dollar\"]==  1.0707]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pandemic[pandemic[\"Time\"]==  \"2021-01-08\"]"
   ]
  }
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