readbtc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coin</th>\n",
       "      <th>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:36:20.655141</td>\n",
       "      <td>USD</td>\n",
       "      <td>22923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:36:23.872537</td>\n",
       "      <td>USD</td>\n",
       "      <td>22923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:36:27.075971</td>\n",
       "      <td>USD</td>\n",
       "      <td>22923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:36:30.630470</td>\n",
       "      <td>USD</td>\n",
       "      <td>22923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:36:37.635732</td>\n",
       "      <td>USD</td>\n",
       "      <td>22923.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:50:41.426963</td>\n",
       "      <td>USD</td>\n",
       "      <td>22972.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:50:44.653466</td>\n",
       "      <td>USD</td>\n",
       "      <td>22972.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:50:47.857966</td>\n",
       "      <td>USD</td>\n",
       "      <td>22972.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:50:51.083441</td>\n",
       "      <td>USD</td>\n",
       "      <td>22972.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>BTC</td>\n",
       "      <td>2023-01-27 13:50:54.301058</td>\n",
       "      <td>USD</td>\n",
       "      <td>22972.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Coin                        Date Currency     Price\n",
       "0    BTC  2023-01-27 13:36:20.655141      USD  22923.76\n",
       "1    BTC  2023-01-27 13:36:23.872537      USD  22923.76\n",
       "2    BTC  2023-01-27 13:36:27.075971      USD  22923.76\n",
       "3    BTC  2023-01-27 13:36:30.630470      USD  22923.76\n",
       "4    BTC  2023-01-27 13:36:37.635732      USD  22923.76\n",
       "..   ...                         ...      ...       ...\n",
       "258  BTC  2023-01-27 13:50:41.426963      USD  22972.22\n",
       "259  BTC  2023-01-27 13:50:44.653466      USD  22972.22\n",
       "260  BTC  2023-01-27 13:50:47.857966      USD  22972.22\n",
       "261  BTC  2023-01-27 13:50:51.083441      USD  22972.22\n",
       "262  BTC  2023-01-27 13:50:54.301058      USD  22972.22\n",
       "\n",
       "[263 rows x 4 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import sqlite3\n",
    "\n",
    "conn = sqlite3.connect('mydatabitcoins.db')\n",
    "df = pd.read_sql('select * from PriceBitcoins', conn)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1 (tags/v3.9.1:1e5d33e, Dec  7 2020, 17:08:21) [MSC v.1927 64 bit (AMD64)]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6f7e9eb9836ce13748ee3e9d7e6a780e4cce23fefc931b0091eaabd67db1698b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}