intro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Marketcap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2160.000000</td>\n",
       "      <td>2.160000e+03</td>\n",
       "      <td>2.160000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1080.500000</td>\n",
       "      <td>398.258568</td>\n",
       "      <td>365.592589</td>\n",
       "      <td>382.879899</td>\n",
       "      <td>383.910691</td>\n",
       "      <td>7.057058e+09</td>\n",
       "      <td>4.172084e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>623.682612</td>\n",
       "      <td>628.082281</td>\n",
       "      <td>566.611523</td>\n",
       "      <td>599.719862</td>\n",
       "      <td>601.078766</td>\n",
       "      <td>1.064526e+10</td>\n",
       "      <td>6.909184e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.482988</td>\n",
       "      <td>0.420897</td>\n",
       "      <td>0.431589</td>\n",
       "      <td>0.434829</td>\n",
       "      <td>1.021280e+05</td>\n",
       "      <td>3.221363e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>540.750000</td>\n",
       "      <td>14.265225</td>\n",
       "      <td>13.190950</td>\n",
       "      <td>13.757600</td>\n",
       "      <td>13.819200</td>\n",
       "      <td>3.825102e+07</td>\n",
       "      <td>1.135576e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1080.500000</td>\n",
       "      <td>205.124631</td>\n",
       "      <td>193.302715</td>\n",
       "      <td>198.425096</td>\n",
       "      <td>198.643691</td>\n",
       "      <td>2.148880e+09</td>\n",
       "      <td>2.070063e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1620.250000</td>\n",
       "      <td>396.494561</td>\n",
       "      <td>375.146804</td>\n",
       "      <td>386.264935</td>\n",
       "      <td>386.435272</td>\n",
       "      <td>9.629136e+09</td>\n",
       "      <td>4.231010e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2160.000000</td>\n",
       "      <td>4362.350542</td>\n",
       "      <td>3785.848603</td>\n",
       "      <td>4174.635873</td>\n",
       "      <td>4168.701049</td>\n",
       "      <td>8.448291e+10</td>\n",
       "      <td>4.828819e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SNo         High          Low         Open        Close  \\\n",
       "count  2160.000000  2160.000000  2160.000000  2160.000000  2160.000000   \n",
       "mean   1080.500000   398.258568   365.592589   382.879899   383.910691   \n",
       "std     623.682612   628.082281   566.611523   599.719862   601.078766   \n",
       "min       1.000000     0.482988     0.420897     0.431589     0.434829   \n",
       "25%     540.750000    14.265225    13.190950    13.757600    13.819200   \n",
       "50%    1080.500000   205.124631   193.302715   198.425096   198.643691   \n",
       "75%    1620.250000   396.494561   375.146804   386.264935   386.435272   \n",
       "max    2160.000000  4362.350542  3785.848603  4174.635873  4168.701049   \n",
       "\n",
       "             Volume     Marketcap  \n",
       "count  2.160000e+03  2.160000e+03  \n",
       "mean   7.057058e+09  4.172084e+10  \n",
       "std    1.064526e+10  6.909184e+10  \n",
       "min    1.021280e+05  3.221363e+07  \n",
       "25%    3.825102e+07  1.135576e+09  \n",
       "50%    2.148880e+09  2.070063e+10  \n",
       "75%    9.629136e+09  4.231010e+10  \n",
       "max    8.448291e+10  4.828819e+11  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ether = pd.read_csv('./dataset/coins/coin_Ethereum.csv')\n",
    "ether.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2381.000000</td>\n",
       "      <td>2381.000000</td>\n",
       "      <td>2381.000000</td>\n",
       "      <td>2381.000000</td>\n",
       "      <td>2.381000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>854.959861</td>\n",
       "      <td>884.934784</td>\n",
       "      <td>820.687518</td>\n",
       "      <td>855.601764</td>\n",
       "      <td>1.258034e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1147.176025</td>\n",
       "      <td>1183.295127</td>\n",
       "      <td>1104.921560</td>\n",
       "      <td>1147.126998</td>\n",
       "      <td>1.009168e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.680000</td>\n",
       "      <td>7.320000</td>\n",
       "      <td>5.860000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>140.580000</td>\n",
       "      <td>145.710000</td>\n",
       "      <td>135.760000</td>\n",
       "      <td>140.810000</td>\n",
       "      <td>5.476800e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>284.000000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>272.290000</td>\n",
       "      <td>284.850000</td>\n",
       "      <td>1.415807e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1198.030000</td>\n",
       "      <td>1256.130000</td>\n",
       "      <td>1109.000000</td>\n",
       "      <td>1206.670000</td>\n",
       "      <td>7.627971e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4808.340000</td>\n",
       "      <td>4864.060000</td>\n",
       "      <td>4715.430000</td>\n",
       "      <td>4808.380000</td>\n",
       "      <td>1.792561e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close        Volume\n",
       "count  2381.000000  2381.000000  2381.000000  2381.000000  2.381000e+03\n",
       "mean    854.959861   884.934784   820.687518   855.601764  1.258034e+07\n",
       "std    1147.176025  1183.295127  1104.921560  1147.126998  1.009168e+08\n",
       "min       6.680000     7.320000     5.860000     6.700000  0.000000e+00\n",
       "25%     140.580000   145.710000   135.760000   140.810000  5.476800e+05\n",
       "50%     284.000000   296.000000   272.290000   284.850000  1.415807e+06\n",
       "75%    1198.030000  1256.130000  1109.000000  1206.670000  7.627971e+06\n",
       "max    4808.340000  4864.060000  4715.430000  4808.380000  1.792561e+09"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ether2 = pd.read_csv('./dataset/ether/Ethereum Historical Data.csv')\n",
    "ether2.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Adj Close (BNB)</th>\n",
       "      <th>Volume (BNB)</th>\n",
       "      <th>Adj Close (BTC)</th>\n",
       "      <th>Volume (BTC)</th>\n",
       "      <th>Adj Close (USDT)</th>\n",
       "      <th>Volume (USDT)</th>\n",
       "      <th>Adj Close (ETH)</th>\n",
       "      <th>Volume (ETH)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1753.000000</td>\n",
       "      <td>1.753000e+03</td>\n",
       "      <td>1753.000000</td>\n",
       "      <td>1.753000e+03</td>\n",
       "      <td>1753.000000</td>\n",
       "      <td>1.753000e+03</td>\n",
       "      <td>1753.000000</td>\n",
       "      <td>1.753000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.233499</td>\n",
       "      <td>9.678428e+08</td>\n",
       "      <td>20224.481952</td>\n",
       "      <td>2.574438e+10</td>\n",
       "      <td>1.001696</td>\n",
       "      <td>4.097497e+10</td>\n",
       "      <td>1113.976655</td>\n",
       "      <td>1.295311e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>185.562414</td>\n",
       "      <td>1.477115e+09</td>\n",
       "      <td>17394.840716</td>\n",
       "      <td>1.992131e+10</td>\n",
       "      <td>0.005891</td>\n",
       "      <td>3.889983e+10</td>\n",
       "      <td>1227.649857</td>\n",
       "      <td>1.100046e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.510360</td>\n",
       "      <td>9.284000e+03</td>\n",
       "      <td>3236.761719</td>\n",
       "      <td>2.923670e+09</td>\n",
       "      <td>0.966644</td>\n",
       "      <td>3.581880e+08</td>\n",
       "      <td>84.308296</td>\n",
       "      <td>6.217330e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13.727642</td>\n",
       "      <td>1.215110e+08</td>\n",
       "      <td>7494.169922</td>\n",
       "      <td>9.926540e+09</td>\n",
       "      <td>0.999980</td>\n",
       "      <td>4.785870e+09</td>\n",
       "      <td>204.335999</td>\n",
       "      <td>4.007950e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.393307</td>\n",
       "      <td>3.214369e+08</td>\n",
       "      <td>10370.820313</td>\n",
       "      <td>2.327626e+10</td>\n",
       "      <td>1.000572</td>\n",
       "      <td>3.457206e+10</td>\n",
       "      <td>449.850006</td>\n",
       "      <td>1.064256e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>294.490295</td>\n",
       "      <td>1.463703e+09</td>\n",
       "      <td>35030.250000</td>\n",
       "      <td>3.511636e+10</td>\n",
       "      <td>1.002768</td>\n",
       "      <td>6.077945e+10</td>\n",
       "      <td>1834.727905</td>\n",
       "      <td>1.832204e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>675.684082</td>\n",
       "      <td>1.798295e+10</td>\n",
       "      <td>67566.828125</td>\n",
       "      <td>3.509679e+11</td>\n",
       "      <td>1.077880</td>\n",
       "      <td>2.790675e+11</td>\n",
       "      <td>4812.087402</td>\n",
       "      <td>8.448291e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Adj Close (BNB)  Volume (BNB)  Adj Close (BTC)  Volume (BTC)  \\\n",
       "count      1753.000000  1.753000e+03      1753.000000  1.753000e+03   \n",
       "mean        137.233499  9.678428e+08     20224.481952  2.574438e+10   \n",
       "std         185.562414  1.477115e+09     17394.840716  1.992131e+10   \n",
       "min           1.510360  9.284000e+03      3236.761719  2.923670e+09   \n",
       "25%          13.727642  1.215110e+08      7494.169922  9.926540e+09   \n",
       "50%          22.393307  3.214369e+08     10370.820313  2.327626e+10   \n",
       "75%         294.490295  1.463703e+09     35030.250000  3.511636e+10   \n",
       "max         675.684082  1.798295e+10     67566.828125  3.509679e+11   \n",
       "\n",
       "       Adj Close (USDT)  Volume (USDT)  Adj Close (ETH)  Volume (ETH)  \n",
       "count       1753.000000   1.753000e+03      1753.000000  1.753000e+03  \n",
       "mean           1.001696   4.097497e+10      1113.976655  1.295311e+10  \n",
       "std            0.005891   3.889983e+10      1227.649857  1.100046e+10  \n",
       "min            0.966644   3.581880e+08        84.308296  6.217330e+08  \n",
       "25%            0.999980   4.785870e+09       204.335999  4.007950e+09  \n",
       "50%            1.000572   3.457206e+10       449.850006  1.064256e+10  \n",
       "75%            1.002768   6.077945e+10      1834.727905  1.832204e+10  \n",
       "max            1.077880   2.790675e+11      4812.087402  8.448291e+10  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crypto_prices = pd.read_csv('./dataset/crypto_prices/crypto_data_updated_27_august.csv')\n",
    "crypto_prices.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert date to type datetime\n",
    "ether2['Date'] = pd.to_datetime(ether2['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ether2.plot(x='Date', y=['Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Date'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ether2.plot(x='Date', y='Volume')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.12 ('sds3386')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e6873ab3fe794b72c3aa6a0d7774cfb617077dd0297efcbc2940158c4af11a9a"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}