Bitcoin and cryptocurrency project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a70e87c8",
   "metadata": {},
   "source": [
    "# Project Exploring the Bitcoin Cryptocurrency Market\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe0412c",
   "metadata": {},
   "source": [
    "## 1.Bitcoin and Cryptocurrencies: Full dataset, filtering, and reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20950911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>24h_volume_usd</th>\n",
       "      <th>available_supply</th>\n",
       "      <th>id</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>market_cap_usd</th>\n",
       "      <th>max_supply</th>\n",
       "      <th>name</th>\n",
       "      <th>percent_change_1h</th>\n",
       "      <th>percent_change_24h</th>\n",
       "      <th>percent_change_7d</th>\n",
       "      <th>price_btc</th>\n",
       "      <th>price_usd</th>\n",
       "      <th>rank</th>\n",
       "      <th>symbol</th>\n",
       "      <th>total_supply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>9.007640e+09</td>\n",
       "      <td>1.672352e+07</td>\n",
       "      <td>bitcoin</td>\n",
       "      <td>1512549554</td>\n",
       "      <td>2.130493e+11</td>\n",
       "      <td>2.100000e+07</td>\n",
       "      <td>Bitcoin</td>\n",
       "      <td>0.12</td>\n",
       "      <td>7.33</td>\n",
       "      <td>17.45</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>12739.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>BTC</td>\n",
       "      <td>1.672352e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.551330e+09</td>\n",
       "      <td>9.616537e+07</td>\n",
       "      <td>ethereum</td>\n",
       "      <td>1512549553</td>\n",
       "      <td>4.352945e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ethereum</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-3.93</td>\n",
       "      <td>-7.33</td>\n",
       "      <td>3.617670e-02</td>\n",
       "      <td>452.652000</td>\n",
       "      <td>2</td>\n",
       "      <td>ETH</td>\n",
       "      <td>9.616537e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.111350e+09</td>\n",
       "      <td>1.684044e+07</td>\n",
       "      <td>bitcoin-cash</td>\n",
       "      <td>1512549578</td>\n",
       "      <td>2.529585e+10</td>\n",
       "      <td>2.100000e+07</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "      <td>1.65</td>\n",
       "      <td>-5.51</td>\n",
       "      <td>-4.75</td>\n",
       "      <td>1.200500e-01</td>\n",
       "      <td>1502.090000</td>\n",
       "      <td>3</td>\n",
       "      <td>BCH</td>\n",
       "      <td>1.684044e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.936090e+09</td>\n",
       "      <td>2.779530e+09</td>\n",
       "      <td>iota</td>\n",
       "      <td>1512549571</td>\n",
       "      <td>1.475225e+10</td>\n",
       "      <td>2.779530e+09</td>\n",
       "      <td>IOTA</td>\n",
       "      <td>-2.38</td>\n",
       "      <td>83.35</td>\n",
       "      <td>255.82</td>\n",
       "      <td>4.241800e-04</td>\n",
       "      <td>5.307460</td>\n",
       "      <td>4</td>\n",
       "      <td>MIOTA</td>\n",
       "      <td>2.779530e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.315050e+08</td>\n",
       "      <td>3.873915e+10</td>\n",
       "      <td>ripple</td>\n",
       "      <td>1512549541</td>\n",
       "      <td>9.365343e+09</td>\n",
       "      <td>1.000000e+11</td>\n",
       "      <td>Ripple</td>\n",
       "      <td>0.56</td>\n",
       "      <td>-3.70</td>\n",
       "      <td>-14.79</td>\n",
       "      <td>1.932000e-05</td>\n",
       "      <td>0.241754</td>\n",
       "      <td>5</td>\n",
       "      <td>XRP</td>\n",
       "      <td>9.999309e+10</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>1321</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>turbocoin</td>\n",
       "      <td>1512368664</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TurboCoin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.12</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>1322</td>\n",
       "      <td>TURBO</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>1322</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>birds</td>\n",
       "      <td>1512535772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Birds</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.62</td>\n",
       "      <td>-42.10</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.000122</td>\n",
       "      <td>1323</td>\n",
       "      <td>BIRDS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>1323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bitcoincashscrypt</td>\n",
       "      <td>1512548078</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BitcoinCashScrypt</td>\n",
       "      <td>-0.37</td>\n",
       "      <td>-37.39</td>\n",
       "      <td>-27.69</td>\n",
       "      <td>5.000000e-07</td>\n",
       "      <td>0.006202</td>\n",
       "      <td>1324</td>\n",
       "      <td>BCCS</td>\n",
       "      <td>2.502380e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>1324</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>swisscoin</td>\n",
       "      <td>1512540278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Swisscoin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.39</td>\n",
       "      <td>-22.84</td>\n",
       "      <td>1.000000e-08</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>1325</td>\n",
       "      <td>SIC</td>\n",
       "      <td>1.020000e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>1325</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>faceblock</td>\n",
       "      <td>1512435283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Faceblock</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.83</td>\n",
       "      <td>1.400000e-07</td>\n",
       "      <td>0.001654</td>\n",
       "      <td>1326</td>\n",
       "      <td>FBL</td>\n",
       "      <td>1.000000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1326 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            24h_volume_usd  available_supply                 id  last_updated  \\\n",
       "0        0    9.007640e+09      1.672352e+07            bitcoin    1512549554   \n",
       "1        1    1.551330e+09      9.616537e+07           ethereum    1512549553   \n",
       "2        2    1.111350e+09      1.684044e+07       bitcoin-cash    1512549578   \n",
       "3        3    2.936090e+09      2.779530e+09               iota    1512549571   \n",
       "4        4    2.315050e+08      3.873915e+10             ripple    1512549541   \n",
       "...    ...             ...               ...                ...           ...   \n",
       "1321  1321             NaN               NaN          turbocoin    1512368664   \n",
       "1322  1322             NaN               NaN              birds    1512535772   \n",
       "1323  1323             NaN               NaN  bitcoincashscrypt    1512548078   \n",
       "1324  1324             NaN               NaN          swisscoin    1512540278   \n",
       "1325  1325             NaN               NaN          faceblock    1512435283   \n",
       "\n",
       "      market_cap_usd    max_supply               name  percent_change_1h  \\\n",
       "0       2.130493e+11  2.100000e+07            Bitcoin               0.12   \n",
       "1       4.352945e+10           NaN           Ethereum              -0.18   \n",
       "2       2.529585e+10  2.100000e+07       Bitcoin Cash               1.65   \n",
       "3       1.475225e+10  2.779530e+09               IOTA              -2.38   \n",
       "4       9.365343e+09  1.000000e+11             Ripple               0.56   \n",
       "...              ...           ...                ...                ...   \n",
       "1321             NaN           NaN          TurboCoin                NaN   \n",
       "1322             NaN           NaN              Birds                NaN   \n",
       "1323             NaN           NaN  BitcoinCashScrypt              -0.37   \n",
       "1324             NaN           NaN          Swisscoin                NaN   \n",
       "1325             NaN           NaN          Faceblock                NaN   \n",
       "\n",
       "      percent_change_24h  percent_change_7d     price_btc     price_usd  rank  \\\n",
       "0                   7.33              17.45  1.000000e+00  12739.500000     1   \n",
       "1                  -3.93              -7.33  3.617670e-02    452.652000     2   \n",
       "2                  -5.51              -4.75  1.200500e-01   1502.090000     3   \n",
       "3                  83.35             255.82  4.241800e-04      5.307460     4   \n",
       "4                  -3.70             -14.79  1.932000e-05      0.241754     5   \n",
       "...                  ...                ...           ...           ...   ...   \n",
       "1321                 NaN               8.12  1.000000e-08      0.000114  1322   \n",
       "1322               10.62             -42.10  1.000000e-08      0.000122  1323   \n",
       "1323              -37.39             -27.69  5.000000e-07      0.006202  1324   \n",
       "1324                4.39             -22.84  1.000000e-08      0.000123  1325   \n",
       "1325                 NaN              -6.83  1.400000e-07      0.001654  1326   \n",
       "\n",
       "     symbol  total_supply  \n",
       "0       BTC  1.672352e+07  \n",
       "1       ETH  9.616537e+07  \n",
       "2       BCH  1.684044e+07  \n",
       "3     MIOTA  2.779530e+09  \n",
       "4       XRP  9.999309e+10  \n",
       "...     ...           ...  \n",
       "1321  TURBO           NaN  \n",
       "1322  BIRDS           NaN  \n",
       "1323   BCCS  2.502380e+06  \n",
       "1324    SIC  1.020000e+10  \n",
       "1325    FBL  1.000000e+07  \n",
       "\n",
       "[1326 rows x 16 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing pandas\n",
    "import pandas as pd\n",
    "\n",
    "#importing matplotlib \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Reading datasets cryptocurrency.csv into pandaas\n",
    "bitcoin=pd.read_csv(\"cryptocurrency.csv\")\n",
    "\n",
    "bitcoin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6d1483c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     id  market_cap_usd\n",
      "0               bitcoin    2.130493e+11\n",
      "1              ethereum    4.352945e+10\n",
      "2          bitcoin-cash    2.529585e+10\n",
      "3                  iota    1.475225e+10\n",
      "4                ripple    9.365343e+09\n",
      "...                 ...             ...\n",
      "1321          turbocoin             NaN\n",
      "1322              birds             NaN\n",
      "1323  bitcoincashscrypt             NaN\n",
      "1324          swisscoin             NaN\n",
      "1325          faceblock             NaN\n",
      "\n",
      "[1326 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "# Selecting the 'id' and 'market_cap_usd' columns\n",
    "b1=bitcoin[['id','market_cap_usd']]\n",
    "print(b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9018cec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_count: 1326\n",
      "market_cap_usd_count: 1031\n"
     ]
    }
   ],
   "source": [
    "# Counting number of values  \n",
    "print(f\"id_count: {b1['id'].count()}\\nmarket_cap_usd_count: {b1['market_cap_usd'].count()}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c3e7532",
   "metadata": {},
   "source": [
    "## 2.Discard the cryptocurrencies without a market capitalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b92b7e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id_count: 1031\n",
      "market_cap-usd_count: 1031\n"
     ]
    }
   ],
   "source": [
    "# Filtering out rows without  a market capitalization\n",
    "b1=bitcoin.dropna(subset=['market_cap_usd'])\n",
    "print('id_count: ',b1['id'].count(),'\\n','market_cap-usd_count: ',b1['market_cap_usd'].count(),sep='')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5ee0346",
   "metadata": {},
   "source": [
    "## 3.How big is Bitcoin compared with the rest of the cryptocurrencies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c47d565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "TOP_CAP_TITLE = 'Top 10 market capitalization'\n",
    "TOP_CAP_YLABEL = '% of total cap'\n",
    "#  Selecting the first 10 rows and setting the index\n",
    "rows10=bitcoin.iloc[0:10].set_index('id')\n",
    " \n",
    "# Calculating market_cap_perc\n",
    "rows10=rows10.assign(market_cap_perc=lambda x:(x.market_cap_usd/bitcoin.market_cap_usd.sum()*100))\n",
    "# Plotting the barplot with the title defined above\n",
    "plt.bar(rows10.index.values,rows10['market_cap_usd'])\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Top 10 market capitalization')\n",
    "plt.xlabel('id')\n",
    "\n",
    "# Annotating the y axis with the label defined above\n",
    "plt.ylabel(TOP_CAP_YLABEL)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b55553c",
   "metadata": {},
   "source": [
    "## 4. Making the plot easier to read and more informative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16415991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = ['red', 'yellow', 'black', 'blue', 'orange','green','indigo','grey','blue','blue']\n",
    "#Plotting market_cap_usd as before but adding the colors\n",
    "plt.bar(first10.index.values,first10['market_cap_usd'],color=col )\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Top 10 market capitalization')\n",
    "#scaling the y-axis  \n",
    "plt.yscale('log')\n",
    "plt.xlabel('id')\n",
    "\n",
    "# Annotating the y axis with 'USD'\n",
    "plt.ylabel('USD')\n",
    "# Final touch! Removing the xlabel as it is not very informative\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03ddd1b4",
   "metadata": {},
   "source": [
    "## 5.What is going on?! Volatility in cryptocurrencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "49b8bf9b",
   "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>percent_change_24h</th>\n",
       "      <th>percent_change_7d</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>flappycoin</th>\n",
       "      <td>-95.85</td>\n",
       "      <td>-96.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credence-coin</th>\n",
       "      <td>-94.22</td>\n",
       "      <td>-95.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coupecoin</th>\n",
       "      <td>-93.93</td>\n",
       "      <td>-61.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tyrocoin</th>\n",
       "      <td>-79.02</td>\n",
       "      <td>-87.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petrodollar</th>\n",
       "      <td>-76.55</td>\n",
       "      <td>542.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bitok</th>\n",
       "      <td>-74.37</td>\n",
       "      <td>-71.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bankcoin</th>\n",
       "      <td>-68.56</td>\n",
       "      <td>50.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tellurion</th>\n",
       "      <td>-63.75</td>\n",
       "      <td>-52.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shadow-token</th>\n",
       "      <td>-61.40</td>\n",
       "      <td>-59.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eusd</th>\n",
       "      <td>-58.89</td>\n",
       "      <td>-25.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               percent_change_24h  percent_change_7d\n",
       "id                                                  \n",
       "flappycoin                 -95.85             -96.61\n",
       "credence-coin              -94.22             -95.31\n",
       "coupecoin                  -93.93             -61.24\n",
       "tyrocoin                   -79.02             -87.43\n",
       "petrodollar                -76.55             542.96\n",
       "bitok                      -74.37             -71.67\n",
       "bankcoin                   -68.56              50.14\n",
       "tellurion                  -63.75             -52.59\n",
       "shadow-token               -61.40             -59.05\n",
       "eusd                       -58.89             -25.50"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volatility = bitcoin[['id', 'percent_change_24h', 'percent_change_7d']]\n",
    "## Setting the index to 'id' and dropping all NaN rows\n",
    "volatility=volatility.set_index('id').dropna()\n",
    "# Sorting the DataFrame by percent_change_24h in ascending order\n",
    "volatility=volatility.sort_values(by='percent_change_24h',ascending=True)\n",
    "\n",
    "#checking the first few rows\n",
    "volatility.head(10)\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26c3caf3",
   "metadata": {},
   "source": [
    "## 6.Well, we can already see that things are a bit crazy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c45e18a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xQ5lggANJ0S5rSboSWB7YpVatgiAIgnFD1xGN7RuANwGvBz4BvMb2LXUrJulzkixpubwdCQWDIAjGIGWizt7btGtdSU8Ct9p+pJVMv0haFXgb8I/C7mJCwc1JCQU3r6P9IAiCYPgo4zrbG9gSuDRvvxmYDkySdJjtU2vQ6wjgiwwtVINCQkHgmpxefcVYsBYEQTC6KRMMMB/watvvs/0+YH1SYMDmwEHDrZCkHYEHbN/cdCgSCgZBEIxByoxoVrX9z8L2I3nfvyT9u0qjkv4AvLLFoS8DXyK5zSphewowBWDy5Mk9JSMMgiAIhp8yhuYySb8Gzsrb78v7FgWeqNKo7e1a7Zf0WlLq9JslQUoaeIOkzYiEgkEQBGOSMoZmX5Jx2SpvnwKck+dKthlOZWzfCryisS3p78Bk249KugD4tKTTSW67SCgYBEEwBuhqaLJBOTu/BkkkFAyCIBiDlA1vPpw00lB+2fYSNeuG7TUK7yOhYBAEwRikjOvse8C7bd9ZtzJBEATB+KNMePM/w8gEQRAEVSkzopkm6QzgPOCFxk7b59alVBAEQTB+KGNoliBNvhfXthgIQxMEQRB0pUzUWUR3BUEQBJUpE3W2ECnf2WuAhRr7bX+sRr2CIAiCXkiL3MvhkU2aUiYY4FRSupi3A5eTVuQ/XadSQRAEwfihjKFZ2/ZXgWdtnwy8i0jPHwRBEJSkjKFpJM58QtIGwJIU0sQEQRAEQSfKRJ1NkbQ08FVSSefFgK/VqlUQBEEwbigTdXZ8fns5sGa96gRBEATjjTJRZwuSsjevUTzf9mH1qRUEQRCMF8rM0ZxPKqM8C3i28AqCYBiQNK+kG3PdJyRNknStpBmSzpC0QN6/YN6ekY+vMVDFg+FH6u01RigzR7OK7e1r1yQIJi77A3eSsnBAypZ+hO3TJR1LWsd2TP77uO21Je2Wz/vAIBQOgl4oM6K5Kle+DIJgmJG0CmnJwPF5W8BbGKr/dDKwU36/Y94mH982nx8Eo5q2IxpJt5Jyms0HfFTS3aSkmo16NBuOjIpBMK75MfBFYPG8vSzwhO1Zeft+YOX8fmXgPgDbsyQ9mc9/tPiBkvYB9gFYbbXV6tQ9CErRyXW2w4hpEQQTEEk7AI/Yni7pzcP1ubanAFMAJk+ePLK5RoKgBW0Nje17ASRtAdxu++m8vQTwauDeEdEwCMYvWwHvkfROUh7BJYAjgaUkzZdHNasAD+TzHwBWBe6XNB9p8fRjI692EPRGmTmaY4BnCtvP5H1BEPSB7UNsr5JLlu8G/NH2h4BLgV3yaXuSIj8hLZjeM7/fJZ8fI5Zg1FPG0Kj4Y7b9MuWi1YIgqMZBwIGSZpDmYE7I+08Als37DwQOHpB+QdATZQzN3ZL2kzR/fu0P3F2nUpI+I+kuSbdL+l5h/yF5DcFfJL29Th2CYCSxfZntHfL7u21vZntt27vafiHvfz5vr52P13ofBsFwUWZk8l/AUcBXSFFol5AjWupA0jakMM7X2X5B0ivy/vVJ7oXXACsBf5C0ru2X6tIlmBiE8ykI6qVMrrNHSA/4keKTwHcLvbhH8v4dgdPz/nuy+2Az4OoR1C0IgiDokTKus5FmXeANOcXG5ZI2zftnryHIFNcXzEbSPpKmSZo2c+bMEVA3CIIg6MRAJvUl/YFUtbOZL5N0WgbYAtgUOFNS6azRsYYgCIJgdDEQQ2N7u3bHJH0SODdHul0n6WVgOYbWEDQori8IJjgxzxIEo5fSrjNJW0j6naTLJO1Uo07nAdvkNtcFFiCl2LgA2C1nsJ0ErANcV6MeQRAEwTDQKdfZK20/XNh1ILAzKdfZtSSDUAcnAidKug14Edgzj25ul3QmcAepZMG+EXEWBEEw+unkOjtW0g3A92w/DzxBWo38MvBUXQrZfhHYo82xbwHfqqvtYPCECywIxh9tXWe2dwJuBH4t6SPAAcCCpJXKO42AbkEQBME4oOMcje1fAW8nJe/7JfBX20fZjrjhIAiCoBRtDY2k90i6FPgdcBupkt+Okk6XtNZIKRgEQRCMbTrN0fw/0sr7hYGLbG8GfE7SOqR5kpHMFhAEQRCMUToZmieB9wKLAI00MNj+G2FkgiAI2tNLhe0JEAHTaY5mZ9LE/3zAB0dGnSAIgmC80anC5qPA0SOoSzBOmAAdtCAIemA0JtUMgiAIxhFRKTMIgvFN1fmSXuSaZYM5iBFNEARBUCthaIIgCIJaCddZEASjn3BjjWnC0ARBMHLE+pIJSbjOgiAIgloJQxMEQRDUSrjOgraE5yJoScyXBD0ShiYIJioxXxKMEOE6C4IBIWlVSZdKukPS7ZL2z/uXkXSxpL/lv0vn/ZJ0lKQZkm6RtPFgv0EQlCMMzTjH7u0VjCizgM/ZXh/YAthX0vrAwcAlttcBLsnbAO8A1smvfYBjRl7lIOidMDRBMCBsP2T7hvz+aeBOYGVgR+DkfNrJDJVO3xE4xYlrgKUkrTiyWgdB74w6QyNpI0nXSLpJ0jRJm+X94TYIxi2S1gD+A7gWWMH2Q/nQw8AK+f3KwH0FsfvzvubP2iffO9Nmzoyq68HgGXWGBvge8A3bGwFfy9sQboNgnCJpMeAc4ADbTxWP2TbQk1PT9hTbk21PXn755YdR0yCoxmg0NAaWyO+XBB7M7ye02yDmWcYnkuYnGZmf2T437/5n47ed/zYq3D4ArFoQXyXvC4JRzWg0NAcA35d0H/AD4JC8P9wGwbhCkoATgDtt/6hw6AJgz/x+T+D8wv6PZDfyFsCTBRdbEIxaBrKORtIfgFe2OPRlYFvgs7bPkfR+0o24XdnPtj0FmAIwefLk6N8Ho5mtgA8Dt0q6Ke/7EvBd4ExJewP3Au/Pxy4E3gnMAJ4DPjqi2gZBRQZiaGy3NRySTgH2z5tnAcfn9+E2CMYVtv8MtFs1uW2L8w3sW6tSQVADo9F19iDwpvz+LcDf8vtwGwRBEIxBRmMKmo8DR0qaD3ieFGEG4TYIgiAYk4w6Q5PdCZu02B9ugyAIgjHIaHSdBUEQBOOIMDRBEARBrYShCYIgCGolDE0QBEFQK2FogiAIgloJQxMEQRDUShiaIAiCoFbC0ARBEAS1EoYmCIIgqJUwNEEQBEGthKEJgiAIaiUMTRAEQVArYWiCIAiCWglDEwRBENRKGJogCIKgVkZdPZqxgD2yckEQBGOZGNEEQRAEtTKhRzQxwgiCIKifGNEEQRAEtRKGJgiCIKiVgRgaSbtKul3Sy5ImNx07RNIMSX+R9PbC/u3zvhmSDh55rYMgCIIqDGpEcxvwXuCK4k5J6wO7Aa8Btgd+KmleSfMCPwHeAawP7J7PDYIJRXS4grHIQIIBbN8JIKn50I7A6bZfAO6RNAPYLB+bYfvuLHd6PveOkdE4CAZPocP1VuB+4HpJF9iO+yAY1Yy2OZqVgfsK2/fnfe32z4WkfSRNkzRt5syZtSkaBANgM3KHy/aLQKPDFQSjmtpGNJL+ALyyxaEv2z6/rnZtTwGmZB1mSrq3x49YDni0YvNVZaPNeuRGus3VK7TTC606XJs3nyRpH2CfvPmMpL/02M7c331u70M5uX5kx1+bw6vr6Gyz5T1Qm6GxvV0FsQeAVQvbq+R9dNjfSYfle1VA0jTbk7ufOXyy0WY9coNqc9AUO1tVmCj/p/gd1yfbzGhznV0A7CZpQUmTgHWA64DrgXUkTZK0AClg4IIB6hkEg6BTRywIRi2DCm/eWdL9wJbAbyRdBGD7duBM0iT/74B9bb9kexbwaeAi4E7gzHxuEEwkosMVjEkGFXX2S+CXbY59C/hWi/0XAhfWrBr04XLoQzbarEduUG3Wgu1ZkhodrnmBE2vqcE2U/1P8juuTnQM5En4FQRAENTLa5miCIAiCcUYYmiAIgqBWwtAEQRAEtRKGJgiCIKiVCV34bNDk3FUrUPg/2P5HjW3tZ/uICrKb2J7etG8H278eNgUHiKSNOx23fcNI6TIWkbQyaUV48Xd8RXuJvtu7xPa23fa1kR1RXftpU9Kyth+r2GalZ0td1ycMDSBpK+DrDF1gAba9Zhe5BYH3AWsw5z/msBJtfgY4FPgn8HJDFNiwjjZtvyRpd6BnQwMcJ+kjtm/LOuwOHACUMjSSXt9C31O6yCwPfLyF3Me6yK0LfIG5b5a3dBD7Yf67EDAZuJn0G9gQmEZa7zWukfRe4HDgFaTv3rgHlugidzjwAdLat5fybtOUmb1JZj1SOp1rbT9T2L+97d91kFsIWARYTtLSWUeAJWiT+7BfXbNcP/d5pTYz10i6CZgK/NYlQ4T7eLb0o2tnnSK8GSTdBXwWmM7QBaZbb0LS74AnW8j9sK3QkOwMYPNeeyx9tnkEMD9wBvBsQbZjj13SmsDZwAeBNwAfAXaw/WSJNk8F1gJuKuhr2/t1kbsK+BNzf89zusjdDBzbQm56W6Eh2XOBQ23fmrc3AL5ue5dusmOd/Ht8dyOzeg9yfwE2zBnXy5y/H7AvaeH1RsD+jdyHkm6w3XZ0KWl/UgdnJeDBwqGngONs//dw6lqQ6+eeq9RmlhWwHfAxYFPSYvaTbP+1i1zVZ0tlXbtie8K/SD2rKnK39dHmpcB8A2iz+fXHkrLrMpSxYeEe2ryT3KHpUdebKn7H6X1cn9vL7BuPL+DKinK/BRbr4fxbG+eTRgjTSMYG4MaSn/GZkdC1INfPPVepzRafsw0p3dATwOXAlh3OrfpsGRZdW73CdZa4VNL3gXOB2dbc3X3zV0l6rXMPuEfuBi6T9JumNn9UV5u2t+nlfEm3kobODZYhrUi/VhK2Ow7FM7eRsng/1EvbwK8lvdMpI0Qv/ErSp0iZJ4rX9V8lZG+RdDxwWt7+EHBLj+2PVaZJOgM4jzmv27ld5J4DbpJ0SZNcuxHrPM7uMtt/l/Rm4GxJqzPkCuvGiZK+Aqxmex9J6wCvcvc5w151bdDPfV61TSQtC+wBfJjkBvsMKeXQRsBZwKQ2olWfLZV17Ua4zgBJl7bYbXf26yPpDmBt4B7SP6bh1+76AJZ0aKv9tr9RV5tZ/l2kCqYLFdps6WvON39bbHctwZCv7Uak5KjFH+97usg9DSwKvAj8e0is65zBPa1V7TzflmUXAj4JvDHvugI4xvbz3WTHOpKmtthtd58T27PVftsntzn/j8CBtm8q7JsPOBH4kO15S+h6BsmN9RHbG0haBLjK9kbDqWtBrp/7vFKbWfavwKnAVNv3Nx07yPbhbeSqPlsq69qNMDR90O5BXOYBPIg2JR1LmkzdBjge2AW4zvbeJWRfR5qfAfiT7ZtL6vumNvpeXkZ+UEhaBljF9kQZ0YwIklYBZtl+uMWxrWxfWeIzptmeLOlG2/+R991s+3U1qDyQ+zy3K4+TB/SEdp1J2sP2aZIObHW83VBT0hK2nwKertDmj20fIOlXzOmWarTZsqffT5sFXm97Q0m32P6GpB+S/LLddN6fFAHWcKOcJmmK7aO7yfZjUCS9h6HRxWUlXCPk3u2B9O5WQdJlwHtI98V04BFJV9n+bNXvMNqR9EXb35N0NK1/jy3dJpLOtP3+Fu7VhlzL3n5zzzx/1j62p5QxMpkXJS3caFfSWhRGy8Ola5/3eaU2m1hH0ueZO9qtpaelj2fLcOjakQltaEiuGYDFe5T7ObAD6WFk5vQtG+jkpjk1//3BCLbZ4P/y3+ckrQQ8BqxYQm5vUhTLszA7DPJqoK2hkfRn21tnF1jxx1s2bPa7pEibn+Vd++ce7yFddJ1Kukavz9sPkPzZZUKxl7T9lKT/BE6xfaik8T6iaUSZTetRbv/8d4dh0OG/6C1T8KGkoJRVJf0M2ArYq8P5VXXt554bjutzFimC8ngK0W4dqPpsGc7/ZUvCdTZAlGqKrJs3/2L7353OH4b2vkoyDtsCPyHdLMfb/moXuVuBTRtzFXku43rbr61R11uAjWy/nLfnJUUldVsLUNmtkr/n24CTSSXHr8+jv757dGMFSYsBuLC+pYTMCqROASRX7CM9tjn7f9WDzLLAFqSH/zW2S5Xd7lfXKlRtU9J025tUbLPSs6Wu6zPRRzTAbL/x0aSeEaT1G/u3Gua3kO3ZvZPl3kx6oP2ddLOsKmlPl1sxXKlN29/Mb8+R9GtgIZdYC0MaJVwrqVFDaCfS5G0pmuZ3ruhh3mMpoBEttmRJmZ7cKk0cRqr18udsZNYE/lZSdkyjtGboVFJkoSTNJE22d6x3I+n9wPeBy0i/46MlfcH22T00/+4KKq9MioCcD3ijUhRkxwi5fnTt4z7v5/pUiqCs+mwZpv9l68+OEQ1Iupg0TG4MPfcgRcC8tYtcs3tnd1JP/0sl2pwOfND2X/L2usAvuvVgqrSptOq7Ld1u0PwZGwNb580/2b6xm0yWa57f2RnoOr+jlH3gu6Q1ASLd5AfbPqOL3FuBrwDrA78nu1VsX1ZG34mK0gLZL9u+NG+/Gfi27dd3kbsZeGuj56uU0eEP3UaQkr4NfM/2E3l7aeBztr9SQtcTSavcb6ew8t3dI+Sq6trPfV6pzXxupQjKPp4tlXXtRhgaQNJNbgqNbLWvhVwl905Dtvm8Mm6aKm2qdehqgzI36Km2P9xtXwd9tyzM7ywKXF3yGq3InMP4uSKVWsgsQzJMs90qwOK2W920zbJTaT0Z2vH6jAdauRfLuBwl3Vp0oUqaB7i5m1u1lbtMXTIDFM67w/b63c4bRl37uc8rtdkPfTxbatM1XGeJxyTtAfwib+9Omigvw1L07t6BtECueXFg2QnZntq0/dEe9GrFa4ob+UYr6zsWc05kvgTtF+ZJWs/2XRpKdNlwX64kaSV3X0T7K+Adtn+TP+/VpEnVDUroWnSHLEQafT3Y5tzxxt15Dq84qr+7hNzvJF3E0L3zAUpEMgLzSlrQOd1JdncuWFLXqyWtb/uOkuf3qytUv88rt6nqEZRVny39XJ+OxIgGGnHyR5OSJxq4ipTpuGO206runSy7ICnn02x3FPBTd8kzVKVNtQnfbuD2YdyHAF8CFiatGm4YiBdJ7q9uEWCNtvck+Zkhze+cZPvHbc6fkm+qqoto3wV8EXgnsB5wCskNelM3XVt81jyk+ZqO7qPxQHZdfYP0ezTp9/gN24+XkH0vc7pVf9np/CxzEGlupjHa/ihwge3vlZB9E2mF/MP0voCyiq6V7/OqbWa5qgtTKz1b+tG16+eGoemPKu6dLLco8Lztl/L2vMCCtp8b7jbVZqVwA3dfMfydMkalg3xP8zv5Ab+ly6+raJbfiWRsFgfe5y5JCDt8zquA39heu4r8REDSJOAhD0UkLgysYPvvJWTfQYqABLjY9kUl25xB6unfytAcTdcFlH3qWvU+76fNShGUVZ8t/ejaFdeQQG2svUgRGksVtpcGTiwhtzNp7UVjeylgp5JtXkMhgR2wGKm3UlubFa7Levnvxq1eJT9jC9IcSWN7CdKanG5yN/ao69HAUYXX7aRe71HAUSU/42lSJuDG66/Aewf52xypF3Bxi3vgohJy04AFCtsLkCbK69T16opylXTt8z6vfH1InpWFgRvy9lokI9dNruqzpbb/ZczRJDZ0jn4BsP24pDJx/Ye6MLS0/UQePZxXQnYhF9Yq2H4mD41ra1O9h3EfCOxDqtcy16JLoKMbK3MMyTA1eKbFvlZcIul9wLnOv/ouNPugu5YFaMGSJH/2JNuHSVqNlBB0IrBci3vgFSXk5rP9YkHuRaU1HC1Rnwt5MzdK+jlpPq6XBKA96Vqgn/u8apuQamT1sjC1QdVnSz+6dv7g4fiQccA8kpZ29kfnyKUy16ZVKeyy1/RZSRs7T25L2oShlft1tTmVFMa9a97eI+9rGcZte5/89p3Ap5jTf39MyTbnyNdk+2WlJIrd+ATJ0M2S9DxdHkQuJP5T9YWwPyG5Yt5CWlPzNHAOQy6T8czLklZznpfM85ZlDPxMSe+xfUGW2xFou3jS9tb5b6/ZOIosTDIwbyt+NEMh9MOia4F+7rmqbWL790qhyo0Iyv1dbmFq1WdLZV27MhzDorH+IhXyugv4Zn7dBXy4hNyJwI9IQ9q18vuTSra5KfC/pIf2n4EZwCY1t3lTmX0tzjmTlAZjm/w6DjizZJvnAvuRCq7NT0p3cV6N/8s3A/eSanZcQcq4+8aSsg0XxY2FfTcP+vc5Ei9ge+AfpKiz0/I13L6E3FokV80/8usqYK0ScqeW2TfM37Go631Z17VLyPVzz1W6Pln2EuCdTfumlJCr+mypdH1KfZc6/7Fj6UVa4Pfp/Fq/pMyipGiUacD1wLeBRXtoc35S2O0GwPwV2pwGfKdsm/mHuwdpRfW8+f0lJeTuKLOvjewrgNOBR0g1NX4OvKKE3BtbvUrITSeFgDa216VkMTTg2nxdGgZneXqcKxrLL2A5Ur6rHUiutDIyk/LfxRgqaDaphNwNTdvz9fCbWoUUxfhIfp1DyrRd9nvO1rXk+ZXv86rXJ593N6nDdGi769ZBtudnS9XrU+YVUWeAUhbjE9x7XH7xM1a0Xbq4VyFGfnXbH+8hRr4hvzjJldRLTqrVqRbGfRrw37avydubA/va/kiJNpdxU8oMSZPcZQGlUgbaBgsBm5EMRrfw5kqL1fJ5HyKtHdiYFCCyC/AV22d1kx3rSLrE9rbd9rWQm2uRpTrk6GoRMg/JLdRLyHylTB5ZtnQ9phayizovPC5Lr9enWZb0uz8KWJX0PS9t/rw2sq9n7qzPp5SQq3x9OhFzNIk7gePy3MFUUrqGMjnAivyG7hPcRaaSet9b5u1SWYYlvZa0NmSZvP0osKft27o16BT+2bHgWFNbjbTh85OqDP4jb69Oci+W4VeS3uGUbr30Akrbc+S/krQq8OMS7VVeCGv7Z9knvi3p4beT7Tu7iI1plBKkLgIsl9fSNNZKLUHKJ9ZObj3SA2lJzZniaAkKD6lmbH8H+E6fIfPL255a2D5J0gHdhNSmHlMJudfn8xcDVlPK3fcJ25/qIFPp+jR/jO1ZwKck7UVygy1dQt9TSW6wmxhaLG3Sc6OTXKXrU4YwNIDt44Hj87qJj5JK+l4JHOec+6kEZcvQNljL9gfyYjBsPyepzGf8D6lC4aUASjmppjCUFn9uxdrUGmng9qVahyNt+LdJxuZdwKvICygrfM79wKtLnPdJ0mK1xnf6E2mSvxS276K8ER0PfAI4AFgJKGZdeAr47w5yryL9PpZizqSYT5Ny23XE9iHZsK3DnL3nrkllqZ7Jo1I9JuAI4O2kcHls3yzpjZ1F+rs+mWMbb2yflDt++5aQm0xy//fqrqp6fboShiaTFzWtl1+PAjcDB0r6hO3dSnzEcT02WTXL8KJF42f7srxAqxONHv1WpLmoxormXYG27kIPQwVB27+RND8pweXiwM4usYCyyTjOQyoH3S39DMB/OWU6mJ3tQCmx55E9qj4hsH0kcKSkz7hEIbuC3PnA+ZK2tH11r+0q1fzZnzTfchMpsupqyoXMf4zkAj6CIRfwXiXkqtZjwvZ9Tf3AjvVh+r0+ma1JHcvGZ07P91I3biOF5Zd25WcaJct7vj7dCEMDSDqC1Ou4hJSxtjFcPFzSX7rIbg2sY/unStlOF+s2/5A5lGox8j3npHIO/ZX0SWDrPBxvDJX/VKLNnmkxilqSFAnzaaWU7u1GUQ2K7q5ZJHdmmUwBezK3Udmrxb4AkPQW238EHlCLLN/uvjZlZ0m3kx7ivyNlVf6s7dM6i7E/KTrqGtvbZFfTt0uqfRjJXVxcjvADkgHqxK8lLUVKhX8DuR5Tifbuy+4z5wf9/gwVjOvGjZL2Ze55jzKJWlvlGGzrntdQZc3FgTskXcec64y6uc1/1eL69NqBbkkYmsQtpEnfVhN9m7UTUlq0NZk0TJ5Kmss4jaEFkW2xfXGe7Os1Rv5jpJxU5zK0pqVsduGlST7ixuT8YpTw+VakrwWULqyLKUN2QX4QmCTpgsKhxRn6vsHcvAn4I61rwpRZm/I221+UtDOp/sl7SWHl3QzN87afl4RScs27suu6DBu6kIPN9r9UYoG1q9dj+i9SR2Vl0lzq7ynnwoLUIbyL5Ho7jOQ27mikigETkp5q7CYHTHQQ7bWyZrHNeUgRqE/Q+/XpShiaxEmknlljQeKfnVcCd7nQOwP/QXbp2H4wR4OV5U0MLYKcn6HEky3JPZpzbW/TQxtFvkvqYV3KUHLAr1f8rI70aigaqE3dcronTryK5CpYjpTJoMHTpI5E0AKnctXzAL+1fWaFj2i4ct4FnGX7yXJTjdyfe8/nARdLepy0dqcMPS2wbozaWo3YVKJgWu4AVplXhLQOZVdJO9o+WSmjQUcvQtWACduXw+xcZ//ntDh6XdJ0QMe5lnzuT0jPM5wScJYtGNiVMDSJnwBrMzS5+AlJ29nu1mt50bYlNeZZus2VzEbST3tt0/ZLkl6WtGSVnobtqZJ+C2xOepgf5JLJAasiaSuSMVud9HtrGIx2xZsqBSDk+aR7GYriQ9IOjZsvaE9+yHyRtDC3V34l6S6S6+yT2X38fBcZbO+c3349d3yWJLneyvBDUqmARtj5rsC3OpxfadTWRxBNkUZWiieUqpg+TFpb1hblUhnAWRoql1Fst9tc5RXAG3Kwxe9Ja38+QHdj2Wvap9LEOhog3yivblzc3MO73XbHKCdJnydFzbyVtHDyY8DPy0yq9tHm+aRex8XAbFdfyR89mrMk7eW2f9Xp/H7J3/OzJNfZ7AlU22Xr/fTTdqlCWgEoVZF8lBQoUvxddXU75hHFk7kjtAiwRJkOTH4Qrsqcaz3KBHwgaX2GAgf+6D7WwHVoY89Ox8uM2nPQwzmkuaupJHf112wf20GmWCqj+IBudNK6rSW7wfbGkj4DLGz7eyqX9flp0uLUl0gdh17yz3UkRjSJGcBqDA3dV837OmL7B0qlg58izdN8zfbFdbZJ6n11Lb3cCs1dkna/HBXTtSRtHzxpu3SIpOZMttjwwZhqP/peQ84nMh8gXefmtSEtR56t3FFNLrOOv1FJ3yQFadxNoRwz5aLOyIalJ+OSow+nktypx5Em1g+2/fs2bVRy/zZ9RiPY4HLaXMsWMv3mGJSkLUkjmL3zvlb52prb7Sf/XGeFYkQDki4nPYAb0Wabkiazn4T20Rrqr9ZEsU2Tgg7KtPluUo2Ul1sd79Jm5ZK0VcnGbV7Sg6cYAVOq59pn25sVIgiDDuTfbvND7VjbLZMxSvpGnt+Z2uKwu0VV5WjO17qQLbhuGr16SW8nTfB/hZRfreOotxDNVeRJ0v36P437v43sCqRoupVsvyOPxLa0fUIJfc8kdWIbHcMPksoVvL+L3BuBzwNX2j5c0prAAWW8Hk0ej8tcMlNJ188NQwNKFfva0s7PL2kaaZHTi3l7AdI/t2u23z7aPI00D3EOqWZO6cWF2dC8ueEOyS6Py2o2NK0WvHYd/mfZRuj4VEnLkeradEtd82dS7/FPpP/F01X0nmhUfaj10d45wCdtP1LH57dp8xanBYlHkn73v1ShqFgHuSNJee+KJY6fIhmfJWx/uIPsb0mjqC9nIzcfqXP32hL63mF7/W77hosWHo/dgWm9BCS0xcOYOG0sv0gLnN5DmjB8ZUmZm1rsqz3bLylE+ROkTKtXk2rGLF5CbjeSq+4kUi6ve4APDPrat9H1UFK9kb/m7ZVIhqOb3CRSNu7jSBUYpwFHDPr7jPYXFROnksJ3lyxsr065RK2TSaHCF5FW3F9AKuVc53ecSpoc/xsp1crilEi4SoviX419pHnVrrLMmRH8ppL6ngZsUdjeHDilhNzypLUwF5KCIP5ImsfqJncLME9he17gluG49jFHw+wJu6+R/iECjpZ0mO0Tu4hWrt/QNBexAClM9FmXmIOw/ZSks0mJCQ8ghVl/QdJRbhOIkIMNXiat22mMuGqPOsttV0nUVyl03PY9SvVrXsyvbSiXumaic4OkLTxn4tQyOeL+DFwr6UDSOpMvAJ8rIXcycDhN5ZhrZm9Shon5SYZuOVKnqxuLac5aPauRJvUh/cY68aykZRnKALIF2T3eDvWfY/BnpKCOHUguwj2BmSXkIKXMaQSALFlSpjt19iDGygv4C7BsYXtZUsGsbnLDUr+BnMAR+G6Jc3ckrbe5lXRTvyLvXwT4exfZaQO4tseS8pvdRxql3ErKlN1N7rr8t5Gyf1FK9K5I2QeuJa3e3phCDy1eLa/XraSe7J2kB/7fSSPdlymftn9rUhjvQ5T3BtRa7rlNm/+Zv+/jwKWkyKoyPf135nv8UuAyklfgXfk3eUAX2Y2BK4En8t+/khacdpJZvdOrhL7T899bCvvKlKzenZo8HjFHA0i6ijR3UZxrucx220SVTfKLQSqZ2qceN7q7v/gM4CcuJB+UdLjtgyRta/uSDrKVQ1irUvCLN/4uRloc+IYucpVCx3Nk0dakKL67yAXQbP/vMHydcYdS6Yi2uEu+O0kfBr5K6kRsSFoB/1HbN3eR+xEpOOQCRihIJI8UGmlvNlJOe2N7roWcLWQXJC18hNQJ7bpWKMstRKpx9XZStNvVwNFl5asg6RrbW0i6iFRi4EHgbNtrlZBdkSGPx3UeJo9HGBpA0inAa4HzSUPUHUm9vFsAnJI0tpJbEHgfc9d96Fq/QXOuUp6HNJR/k+0t24g05FrVtyhbb6XVRLrdfvFk30i61vbmkq4hpSd5jOTXXruL3OHAH0jlekXy5W9n+6CS7S5GysT9eVJRrHn7+BpBGySdB+zjPKkvaTNSXZmNushVDhKpiqTrbW8q6SZgc9svSLrd9mtKyG5ASkhbdP+Wqe/SKshiKdu7tpfqD0k7kIJhViUlH10C+LrbrJlTi0WhRYbD+MccTeJ/86vB+flvtzmB80n+1un0nq6huEp5FsllsWO7k5USYn4KWDNHjzVYnDQkL8Orm3tSucdVJ41Eht9jKN9ZmUSGb81GZfa6JKW05R0NTT5na5IP/WrS3FstiUMDsL0TgKRFbD9n+7psbLrJVU2j1A+V0t4o5TR8M8nQXAi8gzQ31dXQABt4ziixSyUN++LSJnYlpdG6DdhGQ0lH2y3ObqRsWojU4b2Z1LnbkDRP17HzW4YY0fSBpNtsdyzgNYxtLUlKgPkd4ODCoafLur7ajIZqXT2f12d8EngDhUVn7VwHRYPKnMZ/cVLU2R5d2tsF+JPtfw6D+kEXlBYGnkDKWt61KJikPWyfloMH5qKd92C4ycsLlgR+5y5rebLL7XWkyLHX5bUxp7lcRc/K1Wmr0soFX9Itfy6pbPSteXsD0khol351ihENoJSf6YvMHRnVbRh/laTXNv4xPba5CmlY28j0/CdSBuf7W53vlNvsSdKEXa9tvZIUEbSwUpbbYhXFRXr9vB45meSbPipvf5DUE2y3PuPnpASAlQyq7bMlvUdDhalqT7MzwfkxvRUFa+QDrG0VehncWw68RoLKWZKWAB4huaXaMgyRY/3QU9LRAq8qPsts36ZUEbdvwtAkqoYDbg3slec+XoCuGYaLTCU9VBu+2j3yvq69pAq8nZTuYxUKBcFIvuM6089Aj66DfgwqgKTvkLIsjGSanQmNeygKZvt/8t9v1K3XMDItu9ymkNy/z5AiTDsxHNVpq9Jr0tEGt2juMujDkvk8XGeApOm2NylOqjcmDrvItYzY6Rapk2Vvap4wbbVvOJH0Ptvn1PX5bdocUdeBBpBmZyKT13P9iFT2eXNSWPlkd6lK2+uIfpDkNWgfJLlzTyHlKHzeozi9kSokHc3ztZ9kKAXNFXRwc/dCjGgSjVTeDyktLnwQWKabkO17NWealOUZWsjVjap1z/vhSkknUCHvUq8M2HWwFHUsOgtaUSwK9iApOrBMUbCRHNH3y09I64reYvswSU+SMgx0TTU1KFwh6ahTIbpjgQttd6ws3CsxoqFtOOA3nFf8d5A7lFxh0/a6SnW2z7LdtcJmHg0dTYroMGko/hnb9/X1ZTq3WTnvUoW2+lqf0Ue7u5MKvF0Ks4u7HWz7jDraC6oxiBF9VTSUdn/2hLpKpN0faygl1Pw+sIDtSZI2Ag5z9xLQXYkRDeChDKVPklKWlKWfCptV6573w3K2z1QqFYvtWZLa+tP7oS5DUqLdX0i6jBFOszNR6cMFNogRfVX+nV2wjTQyyzNyaXNGkkNJ85uXAdi+SSlDfd9MaEMj6ahOx909rXblCptUrHveJz3nXRortFh01njQrSRppeFYdBa0pKoL7GMkA3UEQyP6vepRsW+OIqV9eoWkbwG7kEoMjDf+7blLcQ+Ly2tCGxrSSvUvk9anPN7l3FacKel/gKUkfZx08xxXUrZqCGI/HEgKQ11T0pWkLK99x8iPEmpfdBa0ZHnbUwvbJ0k6oITcIEb0lbD9M0nTgW3JeQlt3zlgtergdkkfBOaVtA6wH92j60ox0Q3NU6SV578lrfztqSKj+6uwWTUEsR/uIPXMniOtbTmPlORvzNNYaZ4XnW3cvOhsgKqNd6q6wAYxoq+MU92nuoNYBoKkU51q6vwvaS3hC6T/50XAN4eljYkcDCBpP1I435qk2hizD1FzDrDcfu11z5vaG/G8SyONWuSuarUvGB6qBrVIupmUyLY4orm8jsCUoDN5Xdt2pA73XHPUZRZKd21jIhuaBpKOsf3JHs4v1pKZC/dW137E0AhX7BsEkn5BykxdXHS2mO1KC0CDzkg6mZQqfw4XmLuXcv4IabHwHCN626fWqW8wNyPR4Q5D0weSvkmqwXEq6Z/yIWBF218bqGJtGOnFk4OgzkVnwdyoYl6tfN6IjuiDzvTa4e7ps8PQVKdVLP1ojq+XdCdpLukfeddqpKJvsyifOicIZhMusKAMEz0YoF+elfQh4HSSK213CgXFRiHbD1qBusnRMt9h7tohtc63TWAGEdQSjDFiRNMHktYgpd/YimRoriT5q/8+QLUmNJL+TFp4dgSp5s9HSeWcR6U7czwQLrCgG2FognFFIUHqrQ33TWPfoHULgonKPINWYCwjaV1Jl0i6LW9vKGk8rhgeS7yQs+3+TdKnJe1M+USnQRDUQBia/jgOOISc/dn2LUDH9OhB7exPKua2H7AJ8GFSfaEgCAZEBAP0xyJONdKL+2YNSpkAbF+f3z5Dmp8Jgq5Iusr261vsPwn4te2zR16r8UMYmv54VNJaDCWp3IW0riYYEJLWBb5Aqnsz+/ft7mW5gwlMKyMTDB9haPpjX1J51/UkPQDcQ1q0GQyOs4BjSW7NWkogBOMPSc/YXkzJPXE0Kfv0fcCLg9VsfBCGpiK5PsWnbG+XywPMY/vpQesVMMv2MYNWIhiz7Exa1Lw+sAIpEe2JA9VoHBCGpiK2X8plnLE9mhdpTgjyinSAX0n6FClL9QuN48ORGDCYELwR+IXtl4AHJf1x0AqNB8LQ9MeNki4guWtmGxvb5w5OpQnLdNJcWSMy4wvMmfg0MgMEwYCI8Ob+WIhUe+MtpFXo7wZ2GKhGExTbk3KamYOA19meRKr0eDPjp7hbUD9XAB+QNK+kFemttHvQhhjR9Mc8pProTwBIWpqhSo/BYPiK7TOzW/MtpKqNxwCbD1atYIzwS9Lv5g5S8tmrB6vO+CAMTX9s2DAyALYfH81VAicIjUizdwHH2f6NpP83SIWC0Y/txfJfA58esDrjjnCd9cc8eRQDzJ6QDuM9WB6Q9D/AB4ALJS1I/M6DYKDEQ7E/IkX66OP9pHIIP7D9RPazf2HAOgXBhCayN/dJpEgPgiDoTBiaIAiCoFbCdx0EQRDUShiaIAiCoFbC0ARASpPeZv9JOSt1EARBJcLQBECkSQ+CoD4ivDkAIk16EAT1ESOaoJlimvSPADHSCYKgL8LQBM3MTpNu+0Eg0qQHQdAXYWiCIAiCWglDEzQTadKDIBhWIhggaCbSpAdBMKxECpogCIKgVsJ1FgRBENRKGJogCIKgVsLQBEEQBLUShiYIgiColTA0QRAEQa2EoQmCIAhqJQxNEARBUCv/H1zGVTQVHGnZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Defining a function with 2 parameters, the series to plot and the title\n",
    "def top10_member_plot(input_dataset,title):\n",
    "# Making the subplot and the figure for two side by side plots\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.bar(input_dataset[0:10].index.values,input_dataset[0:10],color='blue')\n",
    "    plt.xticks(rotation=90)\n",
    "# Setting the figure's main title to the text passed as parameter\n",
    "    plt.suptitle(title)\n",
    "# Setting the ylabel to '% change'\n",
    "    plt.ylabel('% change')\n",
    "    plt.xlabel(\"id\")\n",
    "# Same as above, but for the top 10 winners\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.bar(input_dataset[-10:].index.values,input_dataset[-10:],color='red')\n",
    "    plt.tight_layout()\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.xlabel(\"id\")\n",
    "    plt.show()\n",
    "    \n",
    "DTITLE='24 hours top losers and winners'\n",
    "# Calling the function above with the 24 hours period series and title DTITLE\n",
    "top10_member_plot(volatility.percent_change_24h,DTITLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88127ae9",
   "metadata": {},
   "source": [
    "## 7.Ok, those are... interesting. Let's check the weekly Series too"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "302654ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "volatility_weekly= volatility.sort_values(by='percent_change_7d', ascending=True)\n",
    "WTITLE = \"Weekly top losers and winners\"\n",
    "# Calling the top10_subplot function\n",
    "top10_member_plot(volatility_weekly.percent_change_7d,WTITLE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fc91942",
   "metadata": {},
   "source": [
    "## 8. How small is small?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "277c383e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             id  market_cap_usd\n",
      "0       bitcoin    2.130493e+11\n",
      "1      ethereum    4.352945e+10\n",
      "2  bitcoin-cash    2.529585e+10\n",
      "3          iota    1.475225e+10\n"
     ]
    }
   ],
   "source": [
    "# Selecting everything bigger than 10 billion\n",
    "largecaps=bitcoin[bitcoin['market_cap_usd'] >= 10000000000][['id','market_cap_usd']]\n",
    "# printing out largecaps\n",
    "print(largecaps)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52fe2b47",
   "metadata": {},
   "source": [
    "## 9. Most coins are tiny"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "78612565",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cap=pd.read_csv('cryptocurrency.csv')\n",
    "# Making a nice function for counting different marketcaps from the\n",
    "# \"cap\" DataFrame. Returns an int.\n",
    "def capcount(query_string):\n",
    "    return cap.query(query_string).count().id\n",
    "\n",
    "# Labels for the plot\n",
    "LABELS = [\"biggish\", \"micro\", \"nano\"]\n",
    "\n",
    "# Using capcount count the biggish cryptos\n",
    "big = capcount('market_cap_usd > 3E+8')\n",
    "\n",
    "# Same as above for micro ...\n",
    "micro = capcount('market_cap_usd > 50000000 and market_cap_usd < 300000000')\n",
    "\n",
    "# ... and for nano\n",
    "nano =  capcount('market_cap_usd < 50000000')\n",
    "\n",
    "# Making a list with the 3 counts\n",
    "values = [big, micro, nano]\n",
    "\n",
    "# Plotting them with matplotlib \n",
    "plt.bar(height=values, x=LABELS);\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "pygments_lexer": "ipython3",
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