bitcoin.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df01f4b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "pd.set_option('max_colwidth', 400)\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5cab09e7",
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-17</td>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-18</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-19</td>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-20</td>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-21</td>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2014-09-22</td>\n",
       "      <td>399.100006</td>\n",
       "      <td>406.915985</td>\n",
       "      <td>397.130005</td>\n",
       "      <td>402.152008</td>\n",
       "      <td>402.152008</td>\n",
       "      <td>24127600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014-09-23</td>\n",
       "      <td>402.092010</td>\n",
       "      <td>441.557007</td>\n",
       "      <td>396.196991</td>\n",
       "      <td>435.790985</td>\n",
       "      <td>435.790985</td>\n",
       "      <td>45099500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014-09-24</td>\n",
       "      <td>435.751007</td>\n",
       "      <td>436.112000</td>\n",
       "      <td>421.131989</td>\n",
       "      <td>423.204987</td>\n",
       "      <td>423.204987</td>\n",
       "      <td>30627700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2014-09-25</td>\n",
       "      <td>423.156006</td>\n",
       "      <td>423.519989</td>\n",
       "      <td>409.467987</td>\n",
       "      <td>411.574005</td>\n",
       "      <td>411.574005</td>\n",
       "      <td>26814400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2014-09-26</td>\n",
       "      <td>411.428986</td>\n",
       "      <td>414.937988</td>\n",
       "      <td>400.009003</td>\n",
       "      <td>404.424988</td>\n",
       "      <td>404.424988</td>\n",
       "      <td>21460800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2014-09-27</td>\n",
       "      <td>403.556000</td>\n",
       "      <td>406.622986</td>\n",
       "      <td>397.372009</td>\n",
       "      <td>399.519989</td>\n",
       "      <td>399.519989</td>\n",
       "      <td>15029300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2014-09-28</td>\n",
       "      <td>399.471008</td>\n",
       "      <td>401.016998</td>\n",
       "      <td>374.332001</td>\n",
       "      <td>377.181000</td>\n",
       "      <td>377.181000</td>\n",
       "      <td>23613300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2014-09-29</td>\n",
       "      <td>376.928009</td>\n",
       "      <td>385.210999</td>\n",
       "      <td>372.239990</td>\n",
       "      <td>375.467010</td>\n",
       "      <td>375.467010</td>\n",
       "      <td>32497700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>376.088013</td>\n",
       "      <td>390.976990</td>\n",
       "      <td>373.442993</td>\n",
       "      <td>386.944000</td>\n",
       "      <td>386.944000</td>\n",
       "      <td>34707300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>387.427002</td>\n",
       "      <td>391.378998</td>\n",
       "      <td>380.779999</td>\n",
       "      <td>383.614990</td>\n",
       "      <td>383.614990</td>\n",
       "      <td>26229400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2014-10-02</td>\n",
       "      <td>383.988007</td>\n",
       "      <td>385.497009</td>\n",
       "      <td>372.946014</td>\n",
       "      <td>375.071991</td>\n",
       "      <td>375.071991</td>\n",
       "      <td>21777700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2014-10-03</td>\n",
       "      <td>375.181000</td>\n",
       "      <td>377.695007</td>\n",
       "      <td>357.859009</td>\n",
       "      <td>359.511993</td>\n",
       "      <td>359.511993</td>\n",
       "      <td>30901200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2014-10-04</td>\n",
       "      <td>359.891998</td>\n",
       "      <td>364.487000</td>\n",
       "      <td>325.885986</td>\n",
       "      <td>328.865997</td>\n",
       "      <td>328.865997</td>\n",
       "      <td>47236500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2014-10-05</td>\n",
       "      <td>328.915985</td>\n",
       "      <td>341.800995</td>\n",
       "      <td>289.295990</td>\n",
       "      <td>320.510010</td>\n",
       "      <td>320.510010</td>\n",
       "      <td>83308096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2014-10-06</td>\n",
       "      <td>320.389008</td>\n",
       "      <td>345.134003</td>\n",
       "      <td>302.559998</td>\n",
       "      <td>330.079010</td>\n",
       "      <td>330.079010</td>\n",
       "      <td>79011800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2014-10-07</td>\n",
       "      <td>330.584015</td>\n",
       "      <td>339.247009</td>\n",
       "      <td>320.481995</td>\n",
       "      <td>336.187012</td>\n",
       "      <td>336.187012</td>\n",
       "      <td>49199900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2014-10-08</td>\n",
       "      <td>336.115997</td>\n",
       "      <td>354.364014</td>\n",
       "      <td>327.187988</td>\n",
       "      <td>352.940002</td>\n",
       "      <td>352.940002</td>\n",
       "      <td>54736300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2014-10-09</td>\n",
       "      <td>352.747986</td>\n",
       "      <td>382.726013</td>\n",
       "      <td>347.687012</td>\n",
       "      <td>365.026001</td>\n",
       "      <td>365.026001</td>\n",
       "      <td>83641104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2014-10-10</td>\n",
       "      <td>364.687012</td>\n",
       "      <td>375.066986</td>\n",
       "      <td>352.963013</td>\n",
       "      <td>361.562012</td>\n",
       "      <td>361.562012</td>\n",
       "      <td>43665700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2014-10-11</td>\n",
       "      <td>361.362000</td>\n",
       "      <td>367.191010</td>\n",
       "      <td>355.950989</td>\n",
       "      <td>362.299011</td>\n",
       "      <td>362.299011</td>\n",
       "      <td>13345200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2014-10-12</td>\n",
       "      <td>362.605988</td>\n",
       "      <td>379.433014</td>\n",
       "      <td>356.144012</td>\n",
       "      <td>378.549011</td>\n",
       "      <td>378.549011</td>\n",
       "      <td>17552800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2014-10-13</td>\n",
       "      <td>377.920990</td>\n",
       "      <td>397.226013</td>\n",
       "      <td>368.897003</td>\n",
       "      <td>390.414001</td>\n",
       "      <td>390.414001</td>\n",
       "      <td>35221400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2014-10-14</td>\n",
       "      <td>391.691986</td>\n",
       "      <td>411.697998</td>\n",
       "      <td>391.324005</td>\n",
       "      <td>400.869995</td>\n",
       "      <td>400.869995</td>\n",
       "      <td>38491500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2014-10-15</td>\n",
       "      <td>400.954987</td>\n",
       "      <td>402.226990</td>\n",
       "      <td>388.765991</td>\n",
       "      <td>394.773010</td>\n",
       "      <td>394.773010</td>\n",
       "      <td>25267100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2014-10-16</td>\n",
       "      <td>394.518005</td>\n",
       "      <td>398.807007</td>\n",
       "      <td>373.070007</td>\n",
       "      <td>382.556000</td>\n",
       "      <td>382.556000</td>\n",
       "      <td>26990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2014-09-17  465.864014  468.174011  452.421997  457.334015  457.334015   \n",
       "1  2014-09-18  456.859985  456.859985  413.104004  424.440002  424.440002   \n",
       "2  2014-09-19  424.102997  427.834991  384.532013  394.795990  394.795990   \n",
       "3  2014-09-20  394.673004  423.295990  389.882996  408.903992  408.903992   \n",
       "4  2014-09-21  408.084991  412.425995  393.181000  398.821014  398.821014   \n",
       "5  2014-09-22  399.100006  406.915985  397.130005  402.152008  402.152008   \n",
       "6  2014-09-23  402.092010  441.557007  396.196991  435.790985  435.790985   \n",
       "7  2014-09-24  435.751007  436.112000  421.131989  423.204987  423.204987   \n",
       "8  2014-09-25  423.156006  423.519989  409.467987  411.574005  411.574005   \n",
       "9  2014-09-26  411.428986  414.937988  400.009003  404.424988  404.424988   \n",
       "10 2014-09-27  403.556000  406.622986  397.372009  399.519989  399.519989   \n",
       "11 2014-09-28  399.471008  401.016998  374.332001  377.181000  377.181000   \n",
       "12 2014-09-29  376.928009  385.210999  372.239990  375.467010  375.467010   \n",
       "13 2014-09-30  376.088013  390.976990  373.442993  386.944000  386.944000   \n",
       "14 2014-10-01  387.427002  391.378998  380.779999  383.614990  383.614990   \n",
       "15 2014-10-02  383.988007  385.497009  372.946014  375.071991  375.071991   \n",
       "16 2014-10-03  375.181000  377.695007  357.859009  359.511993  359.511993   \n",
       "17 2014-10-04  359.891998  364.487000  325.885986  328.865997  328.865997   \n",
       "18 2014-10-05  328.915985  341.800995  289.295990  320.510010  320.510010   \n",
       "19 2014-10-06  320.389008  345.134003  302.559998  330.079010  330.079010   \n",
       "20 2014-10-07  330.584015  339.247009  320.481995  336.187012  336.187012   \n",
       "21 2014-10-08  336.115997  354.364014  327.187988  352.940002  352.940002   \n",
       "22 2014-10-09  352.747986  382.726013  347.687012  365.026001  365.026001   \n",
       "23 2014-10-10  364.687012  375.066986  352.963013  361.562012  361.562012   \n",
       "24 2014-10-11  361.362000  367.191010  355.950989  362.299011  362.299011   \n",
       "25 2014-10-12  362.605988  379.433014  356.144012  378.549011  378.549011   \n",
       "26 2014-10-13  377.920990  397.226013  368.897003  390.414001  390.414001   \n",
       "27 2014-10-14  391.691986  411.697998  391.324005  400.869995  400.869995   \n",
       "28 2014-10-15  400.954987  402.226990  388.765991  394.773010  394.773010   \n",
       "29 2014-10-16  394.518005  398.807007  373.070007  382.556000  382.556000   \n",
       "\n",
       "      Volume  \n",
       "0   21056800  \n",
       "1   34483200  \n",
       "2   37919700  \n",
       "3   36863600  \n",
       "4   26580100  \n",
       "5   24127600  \n",
       "6   45099500  \n",
       "7   30627700  \n",
       "8   26814400  \n",
       "9   21460800  \n",
       "10  15029300  \n",
       "11  23613300  \n",
       "12  32497700  \n",
       "13  34707300  \n",
       "14  26229400  \n",
       "15  21777700  \n",
       "16  30901200  \n",
       "17  47236500  \n",
       "18  83308096  \n",
       "19  79011800  \n",
       "20  49199900  \n",
       "21  54736300  \n",
       "22  83641104  \n",
       "23  43665700  \n",
       "24  13345200  \n",
       "25  17552800  \n",
       "26  35221400  \n",
       "27  38491500  \n",
       "28  25267100  \n",
       "29  26990000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the data into a Pandas DataFrame\n",
    "bitcoin_df = pd.read_excel('Resources/BTC-USD.xlsx')\n",
    "bitcoin_df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5ddc501b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3228 entries, 0 to 3227\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       3228 non-null   datetime64[ns]\n",
      " 1   Open       3228 non-null   float64       \n",
      " 2   High       3228 non-null   float64       \n",
      " 3   Low        3228 non-null   float64       \n",
      " 4   Close      3228 non-null   float64       \n",
      " 5   Adj Close  3228 non-null   float64       \n",
      " 6   Volume     3228 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
      "memory usage: 176.7 KB\n"
     ]
    }
   ],
   "source": [
    "# Get a brief summary of the crowdfunding_info DataFrame.\n",
    "bitcoin_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "871bba26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the crowdfunding_info_df columns.\n",
    "bitcoin_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f4c654a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date          Open          High           Low         Close\n",
      "1567 2019-01-01   3746.713379   3850.913818   3707.231201   3843.520020\n",
      "1568 2019-01-02   3849.216309   3947.981201   3817.409424   3943.409424\n",
      "1569 2019-01-03   3931.048584   3935.685059   3826.222900   3836.741211\n",
      "1570 2019-01-04   3832.040039   3865.934570   3783.853760   3857.717529\n",
      "1571 2019-01-05   3851.973877   3904.903076   3836.900146   3845.194580\n",
      "...         ...           ...           ...           ...           ...\n",
      "2893 2022-08-19  23213.312500  23213.312500  20868.847656  20877.552734\n",
      "2894 2022-08-20  20872.841797  21350.806641  20856.730469  21166.060547\n",
      "2895 2022-08-21  21160.392578  21668.845703  21103.197266  21534.121094\n",
      "2896 2022-08-22  21531.462891  21531.462891  20939.183594  21398.908203\n",
      "2897 2022-08-23  21401.044922  21646.203125  20955.138672  21528.087891\n",
      "\n",
      "[1331 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "bitcoin_df['Date'] = pd.to_datetime(bitcoin_df['Date'])\n",
    "\n",
    "# Specify the start and end dates\n",
    "start_date = pd.to_datetime('2019-01-01')\n",
    "end_date = pd.to_datetime('2022-8-23')\n",
    "\n",
    "# Filter the data based on the date range\n",
    "filtered_df = bitcoin_df[(bitcoin_df['Date'] >= start_date) & (bitcoin_df['Date'] <= end_date)]\n",
    "\n",
    "# Drop unwanted columns\n",
    "bitcoin_df=filtered_df.drop([\"Adj Close\", \"Volume\"],axis =1)\n",
    "\n",
    "\n",
    "# Print the filtered data\n",
    "print(bitcoin_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a7aaa6f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date          Open          High           Low         Close  \\\n",
      "1567 2019-01-01   3746.713379   3850.913818   3707.231201   3843.520020   \n",
      "1568 2019-01-02   3849.216309   3947.981201   3817.409424   3943.409424   \n",
      "1569 2019-01-03   3931.048584   3935.685059   3826.222900   3836.741211   \n",
      "1570 2019-01-04   3832.040039   3865.934570   3783.853760   3857.717529   \n",
      "1571 2019-01-05   3851.973877   3904.903076   3836.900146   3845.194580   \n",
      "...         ...           ...           ...           ...           ...   \n",
      "2893 2022-08-19  23213.312500  23213.312500  20868.847656  20877.552734   \n",
      "2894 2022-08-20  20872.841797  21350.806641  20856.730469  21166.060547   \n",
      "2895 2022-08-21  21160.392578  21668.845703  21103.197266  21534.121094   \n",
      "2896 2022-08-22  21531.462891  21531.462891  20939.183594  21398.908203   \n",
      "2897 2022-08-23  21401.044922  21646.203125  20955.138672  21528.087891   \n",
      "\n",
      "      Percent_Change  \n",
      "1567        2.518697  \n",
      "1568        2.388621  \n",
      "1569       -2.458007  \n",
      "1570        0.665614  \n",
      "1571       -0.176306  \n",
      "...              ...  \n",
      "2893      -11.187900  \n",
      "2894        1.385325  \n",
      "2895        1.735518  \n",
      "2896       -0.619446  \n",
      "2897        0.590127  \n",
      "\n",
      "[1331 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the difference between column2 and column1\n",
    "bitcoin_df['difference'] = bitcoin_df['Close'] - bitcoin_df['Open']\n",
    "\n",
    "# Calculate the percentage difference\n",
    "bitcoin_df['Percent_Change'] = (bitcoin_df['difference'] / bitcoin_df['Close']) * 100\n",
    "\n",
    "bitcoin_df=bitcoin_df.drop([\"difference\"],axis =1)\n",
    "\n",
    "# Print the updated DataFrame\n",
    "print(bitcoin_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1b43f89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export the selected dataframe to a new CSV file\n",
    "bitcoin_df.to_csv('bitcoin_df.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cc332a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1331 entries, 1567 to 2897\n",
      "Data columns (total 6 columns):\n",
      " #   Column          Non-Null Count  Dtype         \n",
      "---  ------          --------------  -----         \n",
      " 0   Date            1331 non-null   datetime64[ns]\n",
      " 1   Open            1331 non-null   float64       \n",
      " 2   High            1331 non-null   float64       \n",
      " 3   Low             1331 non-null   float64       \n",
      " 4   Close           1331 non-null   float64       \n",
      " 5   Percent_Change  1331 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(5)\n",
      "memory usage: 72.8 KB\n"
     ]
    }
   ],
   "source": [
    "# Get a brief summary of the crowdfunding_info DataFrame.\n",
    "bitcoin_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed930902",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}