ethereum_visualizations_deliverable3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d028c4aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting mplfinance\n",
      "  Downloading mplfinance-0.12.9b5-py3-none-any.whl (71 kB)\n",
      "\u001b[K     |████████████████████████████████| 71 kB 1.8 MB/s eta 0:00:011\n",
      "\u001b[?25hRequirement already satisfied: pandas in /home/kishdubey/.local/lib/python3.9/site-packages (from mplfinance) (1.4.2)\n",
      "Requirement already satisfied: matplotlib in /home/kishdubey/.local/lib/python3.9/site-packages (from mplfinance) (3.5.1)\n",
      "Requirement already satisfied: packaging>=20.0 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (21.3)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (2.8.2)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (4.32.0)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (3.0.7)\n",
      "Requirement already satisfied: cycler>=0.10 in /home/kishdubey/.local/lib/python3.9/site-packages (from matplotlib->mplfinance) (0.11.0)\n",
      "Requirement already satisfied: numpy>=1.17 in /home/kishdubey/anaconda3/lib/python3.9/site-packages (from matplotlib->mplfinance) (1.21.6)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /home/kishdubey/anaconda3/lib/python3.9/site-packages (from matplotlib->mplfinance) (8.4.0)\n",
      "Requirement already satisfied: six>=1.5 in /home/kishdubey/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->mplfinance) (1.16.0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /home/kishdubey/anaconda3/lib/python3.9/site-packages (from pandas->mplfinance) (2021.3)\n",
      "Installing collected packages: mplfinance\n",
      "Successfully installed mplfinance-0.12.9b5\n"
     ]
    }
   ],
   "source": [
    "!pip install mplfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1588c012",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87c39f98",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</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>2404</th>\n",
       "      <td>2019-11-28 23:59:59</td>\n",
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       "      <td>7454.121794</td>\n",
       "      <td>7536.820104</td>\n",
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       "      <td>1.905012e+10</td>\n",
       "      <td>1.348794e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2972</th>\n",
       "      <td>2021-06-18 23:59:59</td>\n",
       "      <td>38187.260160</td>\n",
       "      <td>35255.854918</td>\n",
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       "    <tr>\n",
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       "      <td>2019-04-18 23:59:59</td>\n",
       "      <td>5319.986114</td>\n",
       "      <td>5250.506659</td>\n",
       "      <td>5251.480692</td>\n",
       "      <td>5298.385699</td>\n",
       "      <td>1.325649e+10</td>\n",
       "      <td>9.353240e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1899</th>\n",
       "      <td>2018-07-11 23:59:59</td>\n",
       "      <td>6444.959961</td>\n",
       "      <td>6330.470215</td>\n",
       "      <td>6330.770020</td>\n",
       "      <td>6394.709961</td>\n",
       "      <td>3.644860e+09</td>\n",
       "      <td>1.096319e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>2013-08-20 23:59:59</td>\n",
       "      <td>123.011002</td>\n",
       "      <td>117.508003</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>121.205002</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.403830e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                    Date          High           Low          Open  \\\n",
       "2404 2019-11-28 23:59:59   7730.072666   7454.121794   7536.820104   \n",
       "2972 2021-06-18 23:59:59  38187.260160  35255.854918  38099.477055   \n",
       "2180 2019-04-18 23:59:59   5319.986114   5250.506659   5251.480692   \n",
       "1899 2018-07-11 23:59:59   6444.959961   6330.470215   6330.770020   \n",
       "113  2013-08-20 23:59:59    123.011002    117.508003    119.000000   \n",
       "\n",
       "             Close        Volume     Marketcap  \n",
       "2404   7463.106128  1.905012e+10  1.348794e+11  \n",
       "2972  35787.244782  3.620089e+10  6.705894e+11  \n",
       "2180   5298.385699  1.325649e+10  9.353240e+10  \n",
       "1899   6394.709961  3.644860e+09  1.096319e+11  \n",
       "113     121.205002  0.000000e+00  1.403830e+09  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./dataset/coins/coin_Bitcoin.csv', parse_dates=['Date'])\n",
    "\n",
    "df.drop([\"Name\"],axis=1, inplace=True)\n",
    "df.drop([\"SNo\"],axis=1, inplace=True)\n",
    "df.drop([\"Symbol\"],axis=1, inplace=True)\n",
    "\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b81fd37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mticker\n",
    "from mplfinance.original_flavor import candlestick_ohlc\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "df['Date_mpl'] = df['Date'].apply(lambda x: mdates.date2num(x))\n",
    "\n",
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = plt.subplot2grid((1,1), (0,0))\n",
    "\n",
    "temp_df = df[df['Date']>'2021-03-04']\n",
    "ohlc = []\n",
    "for ind, row in temp_df.iterrows():\n",
    "    ol = [row['Date_mpl'],row['Open'], row['High'], row['Low'], row['Close'], row['Volume']]\n",
    "    ohlc.append(ol)\n",
    "    \n",
    "candlestick_ohlc(ax1, ohlc, width=0.4, colorup='#77d879', colordown='#db3f3f')\n",
    "ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))\n",
    "\n",
    "plt.xlabel(\"Date\", fontsize=12)\n",
    "plt.ylabel(\"Price in USD\", fontsize=12)\n",
    "plt.title(\"Candlestick chart for Bitcoin\", fontsize=15)\n",
    "plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "babafb21",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}