kabuka.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "c6507262",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (0.2.14)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (4.11.1)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: requests>=2.26 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (2.27.1)\n",
      "Requirement already satisfied: pytz>=2022.5 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (2023.3)\n",
      "Requirement already satisfied: html5lib>=1.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.1)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (2.3.6)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.4.2)\n",
      "Requirement already satisfied: appdirs>=1.4.4 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.4.4)\n",
      "Requirement already satisfied: cryptography>=3.3.2 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (3.4.8)\n",
      "Requirement already satisfied: lxml>=4.9.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (4.9.2)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from yfinance) (1.21.5)\n",
      "Requirement already satisfied: soupsieve>1.2 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.3.1)\n",
      "Requirement already satisfied: cffi>=1.12 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from cryptography>=3.3.2->yfinance) (1.15.0)\n",
      "Requirement already satisfied: webencodings in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\n",
      "Requirement already satisfied: six>=1.9 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.3.0->yfinance) (2.8.2)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2021.10.8)\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (2.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (1.26.9)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from requests>=2.26->yfinance) (3.3)\n",
      "Requirement already satisfied: pycparser in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from cffi>=1.12->cryptography>=3.3.2->yfinance) (2.21)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "7530f69f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance as yf\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "b1e6c8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "aapl = yf.Ticker(\"AAPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "2daca7b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "yfinance.Ticker object <AAPL>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aapl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b427af7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AAPL: No data found for this date range, symbol may be delisted\n"
     ]
    }
   ],
   "source": [
    "days = 20\n",
    "hist = aapl.history(period = f'{days}d')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8ceba61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f421edba",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "Empty DataFrame\n",
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     "execution_count": 58,
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   ],
   "source": [
    "hist.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "8a02dba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSFT: No data found for this date range, symbol may be delisted\n"
     ]
    },
    {
     "data": {
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       "Empty DataFrame\n",
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     "execution_count": 59,
     "metadata": {},
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    }
   ],
   "source": [
    "hist_msft = yf.Ticker('MSFT').history(period = f'{days}d')\n",
    "hist_msft.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "2111e7ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([hist,hist_msft],axis = 1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "633fa907",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "Empty DataFrame\n",
       "Columns: [Open, High, Low, Close, Adj Close, Volume]\n",
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     "execution_count": 61,
     "metadata": {},
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    }
   ],
   "source": [
    "hist.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "926f5f3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object', name='Date')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "ac2a9163",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Index' object has no attribute 'strftime'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Input \u001b[0;32mIn [63]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m hist\u001b[38;5;241m.\u001b[39mindex \u001b[38;5;241m=\u001b[39m \u001b[43mhist\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstrftime\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mB \u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mY\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Index' object has no attribute 'strftime'"
     ]
    }
   ],
   "source": [
    "hist.index = hist.index.strftime('%d %B %Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4808b8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10 March 2023</th>\n",
       "      <td>150.210007</td>\n",
       "      <td>150.940002</td>\n",
       "      <td>147.610001</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>68524400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13 March 2023</th>\n",
       "      <td>147.809998</td>\n",
       "      <td>153.139999</td>\n",
       "      <td>147.699997</td>\n",
       "      <td>150.470001</td>\n",
       "      <td>84457100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 March 2023</th>\n",
       "      <td>151.279999</td>\n",
       "      <td>153.399994</td>\n",
       "      <td>150.100006</td>\n",
       "      <td>152.589996</td>\n",
       "      <td>73695900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 March 2023</th>\n",
       "      <td>151.190002</td>\n",
       "      <td>153.250000</td>\n",
       "      <td>149.919998</td>\n",
       "      <td>152.990005</td>\n",
       "      <td>77167900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 March 2023</th>\n",
       "      <td>152.160004</td>\n",
       "      <td>156.460007</td>\n",
       "      <td>151.639999</td>\n",
       "      <td>155.850006</td>\n",
       "      <td>76161100</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Open        High         Low       Close    Volume  \\\n",
       "Date                                                                      \n",
       "10 March 2023  150.210007  150.940002  147.610001  148.500000  68524400   \n",
       "13 March 2023  147.809998  153.139999  147.699997  150.470001  84457100   \n",
       "14 March 2023  151.279999  153.399994  150.100006  152.589996  73695900   \n",
       "15 March 2023  151.190002  153.250000  149.919998  152.990005  77167900   \n",
       "16 March 2023  152.160004  156.460007  151.639999  155.850006  76161100   \n",
       "\n",
       "               Dividends  Stock Splits  \n",
       "Date                                    \n",
       "10 March 2023        0.0           0.0  \n",
       "13 March 2023        0.0           0.0  \n",
       "14 March 2023        0.0           0.0  \n",
       "15 March 2023        0.0           0.0  \n",
       "16 March 2023        0.0           0.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2f598734",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist = hist [['Close']]\n",
    "hist.columns = [\"apple\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "23b0baf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10 March 2023</th>\n",
       "      <td>148.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13 March 2023</th>\n",
       "      <td>150.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14 March 2023</th>\n",
       "      <td>152.589996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15 March 2023</th>\n",
       "      <td>152.990005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16 March 2023</th>\n",
       "      <td>155.850006</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    apple\n",
       "Date                     \n",
       "10 March 2023  148.500000\n",
       "13 March 2023  150.470001\n",
       "14 March 2023  152.589996\n",
       "15 March 2023  152.990005\n",
       "16 March 2023  155.850006"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d5ff496d",
   "metadata": {},
   "outputs": [
    {
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       "      <td>162.360001</td>\n",
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       "      <td>165.630005</td>\n",
       "      <td>163.759995</td>\n",
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       "Date   10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "apple          148.5     150.470001     152.589996     152.990005   \n",
       "\n",
       "Date   16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "apple     155.850006          155.0     157.399994     159.279999   \n",
       "\n",
       "Date   22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "apple     157.830002     158.929993         160.25     158.279999   \n",
       "\n",
       "Date   28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "apple     157.649994     160.770004     162.360001     164.899994   \n",
       "\n",
       "Date   03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "apple     166.169998     165.630005     163.759995     164.660004  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
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   "source": [
    "hist = hist.T\n",
    "hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c0b7b33",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist.index.name = 'Name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "25e5f21a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>160.770004</td>\n",
       "      <td>162.360001</td>\n",
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       "Date   10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "Name                                                                \n",
       "apple          148.5     150.470001     152.589996     152.990005   \n",
       "\n",
       "Date   16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "Name                                                                \n",
       "apple     155.850006          155.0     157.399994     159.279999   \n",
       "\n",
       "Date   22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "Name                                                                \n",
       "apple     157.830002     158.929993         160.25     158.279999   \n",
       "\n",
       "Date   28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "Name                                                                \n",
       "apple     157.649994     160.770004     162.360001     164.899994   \n",
       "\n",
       "Date   03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "Name                                                               \n",
       "apple     166.169998     165.630005     163.759995     164.660004  "
      ]
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     "execution_count": 31,
     "metadata": {},
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    "hist"
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  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "764f06bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "days = 20\n",
    "tickers = {\n",
    "    'apple': 'AAPL',\n",
    "    'amazon':'AMZN',\n",
    "    'google':'GOOGL',\n",
    "    \"microsoft\":'MSFT',\n",
    "    'netflix':'NFLX'\n",
    "}\n",
    "def get_data(days,tickers):\n",
    "    df = pd.DataFrame()\n",
    "    for company in tickers.keys():\n",
    "        tkr = yf.Ticker(tickers[company])\n",
    "        hist = tkr.history(period = f'{days}d')\n",
    "        hist.index = hist.index.strftime('%d %B %Y')\n",
    "        hist = hist [['Close']]\n",
    "        hist.columns = [company]\n",
    "        hist = hist.T\n",
    "        hist.index.name = 'Name'\n",
    "        df = pd.concat([df,hist])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "cb951fe0",
   "metadata": {},
   "outputs": [
    {
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       "      <th>14 March 2023</th>\n",
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       "      <td>160.250000</td>\n",
       "      <td>158.279999</td>\n",
       "      <td>157.649994</td>\n",
       "      <td>160.770004</td>\n",
       "      <td>162.360001</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>166.169998</td>\n",
       "      <td>165.630005</td>\n",
       "      <td>163.759995</td>\n",
       "      <td>164.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amazon</th>\n",
       "      <td>90.730003</td>\n",
       "      <td>92.430000</td>\n",
       "      <td>94.879997</td>\n",
       "      <td>96.199997</td>\n",
       "      <td>100.040001</td>\n",
       "      <td>98.949997</td>\n",
       "      <td>97.709999</td>\n",
       "      <td>100.610001</td>\n",
       "      <td>98.699997</td>\n",
       "      <td>98.709999</td>\n",
       "      <td>98.129997</td>\n",
       "      <td>98.040001</td>\n",
       "      <td>97.239998</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>103.290001</td>\n",
       "      <td>102.410004</td>\n",
       "      <td>103.949997</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>102.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>google</th>\n",
       "      <td>90.629997</td>\n",
       "      <td>91.110001</td>\n",
       "      <td>93.970001</td>\n",
       "      <td>96.110001</td>\n",
       "      <td>100.320000</td>\n",
       "      <td>101.620003</td>\n",
       "      <td>101.220001</td>\n",
       "      <td>104.919998</td>\n",
       "      <td>103.370003</td>\n",
       "      <td>105.599998</td>\n",
       "      <td>105.440002</td>\n",
       "      <td>102.459999</td>\n",
       "      <td>101.029999</td>\n",
       "      <td>101.389999</td>\n",
       "      <td>100.889999</td>\n",
       "      <td>103.730003</td>\n",
       "      <td>104.360001</td>\n",
       "      <td>104.720001</td>\n",
       "      <td>104.470001</td>\n",
       "      <td>108.419998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microsoft</th>\n",
       "      <td>248.589996</td>\n",
       "      <td>253.919998</td>\n",
       "      <td>260.790009</td>\n",
       "      <td>265.440002</td>\n",
       "      <td>276.200012</td>\n",
       "      <td>279.429993</td>\n",
       "      <td>272.230011</td>\n",
       "      <td>273.779999</td>\n",
       "      <td>272.290009</td>\n",
       "      <td>277.660004</td>\n",
       "      <td>280.570007</td>\n",
       "      <td>276.380005</td>\n",
       "      <td>275.230011</td>\n",
       "      <td>280.510010</td>\n",
       "      <td>284.049988</td>\n",
       "      <td>288.299988</td>\n",
       "      <td>287.230011</td>\n",
       "      <td>287.179993</td>\n",
       "      <td>284.339996</td>\n",
       "      <td>291.600006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>netflix</th>\n",
       "      <td>292.760010</td>\n",
       "      <td>293.510010</td>\n",
       "      <td>294.940002</td>\n",
       "      <td>303.790009</td>\n",
       "      <td>310.059998</td>\n",
       "      <td>303.500000</td>\n",
       "      <td>305.130005</td>\n",
       "      <td>305.790009</td>\n",
       "      <td>293.899994</td>\n",
       "      <td>320.369995</td>\n",
       "      <td>328.390015</td>\n",
       "      <td>327.660004</td>\n",
       "      <td>323.519989</td>\n",
       "      <td>332.029999</td>\n",
       "      <td>338.429993</td>\n",
       "      <td>345.480011</td>\n",
       "      <td>348.279999</td>\n",
       "      <td>346.750000</td>\n",
       "      <td>342.350006</td>\n",
       "      <td>339.329987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date       10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         148.500000     150.470001     152.589996     152.990005   \n",
       "amazon         90.730003      92.430000      94.879997      96.199997   \n",
       "google         90.629997      91.110001      93.970001      96.110001   \n",
       "microsoft     248.589996     253.919998     260.790009     265.440002   \n",
       "netflix       292.760010     293.510010     294.940002     303.790009   \n",
       "\n",
       "Date       16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         155.850006     155.000000     157.399994     159.279999   \n",
       "amazon        100.040001      98.949997      97.709999     100.610001   \n",
       "google        100.320000     101.620003     101.220001     104.919998   \n",
       "microsoft     276.200012     279.429993     272.230011     273.779999   \n",
       "netflix       310.059998     303.500000     305.130005     305.790009   \n",
       "\n",
       "Date       22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         157.830002     158.929993     160.250000     158.279999   \n",
       "amazon         98.699997      98.709999      98.129997      98.040001   \n",
       "google        103.370003     105.599998     105.440002     102.459999   \n",
       "microsoft     272.290009     277.660004     280.570007     276.380005   \n",
       "netflix       293.899994     320.369995     328.390015     327.660004   \n",
       "\n",
       "Date       28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         157.649994     160.770004     162.360001     164.899994   \n",
       "amazon         97.239998     100.250000     102.000000     103.290001   \n",
       "google        101.029999     101.389999     100.889999     103.730003   \n",
       "microsoft     275.230011     280.510010     284.049988     288.299988   \n",
       "netflix       323.519989     332.029999     338.429993     345.480011   \n",
       "\n",
       "Date       03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "Name                                                                   \n",
       "apple         166.169998     165.630005     163.759995     164.660004  \n",
       "amazon        102.410004     103.949997     101.099998     102.059998  \n",
       "google        104.360001     104.720001     104.470001     108.419998  \n",
       "microsoft     287.230011     287.179993     284.339996     291.600006  \n",
       "netflix       348.279999     346.750000     342.350006     339.329987  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "days  = 20\n",
    "get_data(days,tickers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "3ccd11db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: altair in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (4.2.0)\n",
      "Requirement already satisfied: jsonschema>=3.0 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (4.4.0)\n",
      "Requirement already satisfied: pandas>=0.18 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (1.4.2)\n",
      "Requirement already satisfied: entrypoints in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (0.4)\n",
      "Requirement already satisfied: jinja2 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (2.11.3)\n",
      "Requirement already satisfied: toolz in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (0.11.2)\n",
      "Requirement already satisfied: numpy in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from altair) (1.21.5)\n",
      "Requirement already satisfied: attrs>=17.4.0 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from jsonschema>=3.0->altair) (21.4.0)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from jsonschema>=3.0->altair) (0.18.0)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.18->altair) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from pandas>=0.18->altair) (2023.3)\n",
      "Requirement already satisfied: MarkupSafe>=0.23 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from jinja2->altair) (2.0.1)\n",
      "Requirement already satisfied: six>=1.5 in /Users/hayata/opt/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas>=0.18->altair) (1.16.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.0.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install altair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "af8781ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "import altair as alt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "e3ac5213",
   "metadata": {},
   "outputs": [
    {
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       "      <td>101.099998</td>\n",
       "      <td>102.059998</td>\n",
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       "    <tr>\n",
       "      <th>google</th>\n",
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       "      <td>91.110001</td>\n",
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       "      <td>103.370003</td>\n",
       "      <td>105.599998</td>\n",
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       "      <td>108.419998</td>\n",
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       "      <th>microsoft</th>\n",
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       "    <tr>\n",
       "      <th>netflix</th>\n",
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       "      <td>346.750000</td>\n",
       "      <td>342.350006</td>\n",
       "      <td>339.329987</td>\n",
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       "Date       10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         148.500000     150.470001     152.589996     152.990005   \n",
       "amazon         90.730003      92.430000      94.879997      96.199997   \n",
       "google         90.629997      91.110001      93.970001      96.110001   \n",
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       "netflix       292.760010     293.510010     294.940002     303.790009   \n",
       "\n",
       "Date       16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         155.850006     155.000000     157.399994     159.279999   \n",
       "amazon        100.040001      98.949997      97.709999     100.610001   \n",
       "google        100.320000     101.620003     101.220001     104.919998   \n",
       "microsoft     276.200012     279.429993     272.230011     273.779999   \n",
       "netflix       310.059998     303.500000     305.130005     305.790009   \n",
       "\n",
       "Date       22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         157.830002     158.929993     160.250000     158.279999   \n",
       "amazon         98.699997      98.709999      98.129997      98.040001   \n",
       "google        103.370003     105.599998     105.440002     102.459999   \n",
       "microsoft     272.290009     277.660004     280.570007     276.380005   \n",
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       "\n",
       "Date       28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "Name                                                                    \n",
       "apple         157.649994     160.770004     162.360001     164.899994   \n",
       "amazon         97.239998     100.250000     102.000000     103.290001   \n",
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       "\n",
       "Date       03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "Name                                                                   \n",
       "apple         166.169998     165.630005     163.759995     164.660004  \n",
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       "netflix       348.279999     346.750000     342.350006     339.329987  "
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f2efde84",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "Date    10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "Name                                                                 \n",
       "apple      148.500000     150.470001     152.589996     152.990005   \n",
       "amazon      90.730003      92.430000      94.879997      96.199997   \n",
       "\n",
       "Date    16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "Name                                                                 \n",
       "apple      155.850006     155.000000     157.399994     159.279999   \n",
       "amazon     100.040001      98.949997      97.709999     100.610001   \n",
       "\n",
       "Date    22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "Name                                                                 \n",
       "apple      157.830002     158.929993     160.250000     158.279999   \n",
       "amazon      98.699997      98.709999      98.129997      98.040001   \n",
       "\n",
       "Date    28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "Name                                                                 \n",
       "apple      157.649994     160.770004     162.360001     164.899994   \n",
       "amazon      97.239998     100.250000     102.000000     103.290001   \n",
       "\n",
       "Date    03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "Name                                                                \n",
       "apple      166.169998     165.630005     163.759995     164.660004  \n",
       "amazon     102.410004     103.949997     101.099998     102.059998  "
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "companies = ['apple',\"amazon\"]\n",
    "data = df.loc[companies]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0b67ab51",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amazon</th>\n",
       "      <td>90.730003</td>\n",
       "      <td>92.430000</td>\n",
       "      <td>94.879997</td>\n",
       "      <td>96.199997</td>\n",
       "      <td>100.040001</td>\n",
       "      <td>98.949997</td>\n",
       "      <td>97.709999</td>\n",
       "      <td>100.610001</td>\n",
       "      <td>98.699997</td>\n",
       "      <td>98.709999</td>\n",
       "      <td>98.129997</td>\n",
       "      <td>98.040001</td>\n",
       "      <td>97.239998</td>\n",
       "      <td>100.250000</td>\n",
       "      <td>102.000000</td>\n",
       "      <td>103.290001</td>\n",
       "      <td>102.410004</td>\n",
       "      <td>103.949997</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>102.059998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>apple</th>\n",
       "      <td>148.500000</td>\n",
       "      <td>150.470001</td>\n",
       "      <td>152.589996</td>\n",
       "      <td>152.990005</td>\n",
       "      <td>155.850006</td>\n",
       "      <td>155.000000</td>\n",
       "      <td>157.399994</td>\n",
       "      <td>159.279999</td>\n",
       "      <td>157.830002</td>\n",
       "      <td>158.929993</td>\n",
       "      <td>160.250000</td>\n",
       "      <td>158.279999</td>\n",
       "      <td>157.649994</td>\n",
       "      <td>160.770004</td>\n",
       "      <td>162.360001</td>\n",
       "      <td>164.899994</td>\n",
       "      <td>166.169998</td>\n",
       "      <td>165.630005</td>\n",
       "      <td>163.759995</td>\n",
       "      <td>164.660004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Date    10 March 2023  13 March 2023  14 March 2023  15 March 2023  \\\n",
       "Name                                                                 \n",
       "amazon      90.730003      92.430000      94.879997      96.199997   \n",
       "apple      148.500000     150.470001     152.589996     152.990005   \n",
       "\n",
       "Date    16 March 2023  17 March 2023  20 March 2023  21 March 2023  \\\n",
       "Name                                                                 \n",
       "amazon     100.040001      98.949997      97.709999     100.610001   \n",
       "apple      155.850006     155.000000     157.399994     159.279999   \n",
       "\n",
       "Date    22 March 2023  23 March 2023  24 March 2023  27 March 2023  \\\n",
       "Name                                                                 \n",
       "amazon      98.699997      98.709999      98.129997      98.040001   \n",
       "apple      157.830002     158.929993     160.250000     158.279999   \n",
       "\n",
       "Date    28 March 2023  29 March 2023  30 March 2023  31 March 2023  \\\n",
       "Name                                                                 \n",
       "amazon      97.239998     100.250000     102.000000     103.290001   \n",
       "apple      157.649994     160.770004     162.360001     164.899994   \n",
       "\n",
       "Date    03 April 2023  04 April 2023  05 April 2023  06 April 2023  \n",
       "Name                                                                \n",
       "amazon     102.410004     103.949997     101.099998     102.059998  \n",
       "apple      166.169998     165.630005     163.759995     164.660004  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "46c884a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.T.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "1706b9c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th>Name</th>\n",
       "      <th>Date</th>\n",
       "      <th>apple</th>\n",
       "      <th>amazon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 March 2023</td>\n",
       "      <td>148.500000</td>\n",
       "      <td>90.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 March 2023</td>\n",
       "      <td>150.470001</td>\n",
       "      <td>92.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14 March 2023</td>\n",
       "      <td>152.589996</td>\n",
       "      <td>94.879997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15 March 2023</td>\n",
       "      <td>152.990005</td>\n",
       "      <td>96.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16 March 2023</td>\n",
       "      <td>155.850006</td>\n",
       "      <td>100.040001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Name           Date       apple      amazon\n",
       "0     10 March 2023  148.500000   90.730003\n",
       "1     13 March 2023  150.470001   92.430000\n",
       "2     14 March 2023  152.589996   94.879997\n",
       "3     15 March 2023  152.990005   96.199997\n",
       "4     16 March 2023  155.850006  100.040001"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0686e6df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Name</th>\n",
       "      <th>Stock Prices(USD)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>148.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>150.470001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>152.589996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>152.990005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>155.850006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>155.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>20 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>157.399994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>21 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>159.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>157.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>158.929993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>160.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>158.279999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>157.649994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>29 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>160.770004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>162.360001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31 March 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>164.899994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>03 April 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>166.169998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>04 April 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>165.630005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>05 April 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>163.759995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>06 April 2023</td>\n",
       "      <td>apple</td>\n",
       "      <td>164.660004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>90.730003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>13 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>92.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>94.879997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>96.199997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>100.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>98.949997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>20 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>97.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>21 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>100.610001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>22 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>98.699997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>23 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>98.709999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>24 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>98.129997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>27 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>98.040001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>28 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>97.239998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>29 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>100.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>30 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>102.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>31 March 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>103.290001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>03 April 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>102.410004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>04 April 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>103.949997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>05 April 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>101.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>06 April 2023</td>\n",
       "      <td>amazon</td>\n",
       "      <td>102.059998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date    Name  Stock Prices(USD)\n",
       "0   10 March 2023   apple         148.500000\n",
       "1   13 March 2023   apple         150.470001\n",
       "2   14 March 2023   apple         152.589996\n",
       "3   15 March 2023   apple         152.990005\n",
       "4   16 March 2023   apple         155.850006\n",
       "5   17 March 2023   apple         155.000000\n",
       "6   20 March 2023   apple         157.399994\n",
       "7   21 March 2023   apple         159.279999\n",
       "8   22 March 2023   apple         157.830002\n",
       "9   23 March 2023   apple         158.929993\n",
       "10  24 March 2023   apple         160.250000\n",
       "11  27 March 2023   apple         158.279999\n",
       "12  28 March 2023   apple         157.649994\n",
       "13  29 March 2023   apple         160.770004\n",
       "14  30 March 2023   apple         162.360001\n",
       "15  31 March 2023   apple         164.899994\n",
       "16  03 April 2023   apple         166.169998\n",
       "17  04 April 2023   apple         165.630005\n",
       "18  05 April 2023   apple         163.759995\n",
       "19  06 April 2023   apple         164.660004\n",
       "20  10 March 2023  amazon          90.730003\n",
       "21  13 March 2023  amazon          92.430000\n",
       "22  14 March 2023  amazon          94.879997\n",
       "23  15 March 2023  amazon          96.199997\n",
       "24  16 March 2023  amazon         100.040001\n",
       "25  17 March 2023  amazon          98.949997\n",
       "26  20 March 2023  amazon          97.709999\n",
       "27  21 March 2023  amazon         100.610001\n",
       "28  22 March 2023  amazon          98.699997\n",
       "29  23 March 2023  amazon          98.709999\n",
       "30  24 March 2023  amazon          98.129997\n",
       "31  27 March 2023  amazon          98.040001\n",
       "32  28 March 2023  amazon          97.239998\n",
       "33  29 March 2023  amazon         100.250000\n",
       "34  30 March 2023  amazon         102.000000\n",
       "35  31 March 2023  amazon         103.290001\n",
       "36  03 April 2023  amazon         102.410004\n",
       "37  04 April 2023  amazon         103.949997\n",
       "38  05 April 2023  amazon         101.099998\n",
       "39  06 April 2023  amazon         102.059998"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.melt(data,id_vars = [\"Date\"]).rename(columns = {'value':'Stock Prices(USD)'})\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "72631400",
   "metadata": {},
   "outputs": [],
   "source": [
    "ymin,ymax = 100,300\n",
    "chart = (\n",
    "    alt.Chart(data)\n",
    "    .mark_line(opacity = 0.8,clip = True)\n",
    "    .encode(\n",
    "    x = \"Date:T\",\n",
    "    y = alt.Y(\"Stock Prices(USD):Q\",stack = None,scale = alt.Scale(domain = [ymin,ymax])),\n",
    "    color = \"Name:N\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "8f797a7d",
   "metadata": {},
   "outputs": [
    {
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       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-f52d65a0cd64e56ae824f8b03eb88b37\"}, \"mark\": {\"type\": \"line\", \"clip\": true, \"opacity\": 0.8}, \"encoding\": {\"color\": {\"field\": \"Name\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Stock Prices(USD)\", \"scale\": {\"domain\": [100, 300]}, \"stack\": null, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-f52d65a0cd64e56ae824f8b03eb88b37\": [{\"Date\": \"10 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 148.5}, {\"Date\": \"13 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 150.47000122070312}, {\"Date\": \"14 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 152.58999633789062}, {\"Date\": \"15 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 152.99000549316406}, {\"Date\": \"16 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 155.85000610351562}, {\"Date\": \"17 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 155.0}, {\"Date\": \"20 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 157.39999389648438}, {\"Date\": \"21 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 159.27999877929688}, {\"Date\": \"22 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 157.8300018310547}, {\"Date\": \"23 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 158.92999267578125}, {\"Date\": \"24 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 160.25}, {\"Date\": \"27 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 158.27999877929688}, {\"Date\": \"28 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 157.64999389648438}, {\"Date\": \"29 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 160.77000427246094}, {\"Date\": \"30 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 162.36000061035156}, {\"Date\": \"31 March 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 164.89999389648438}, {\"Date\": \"03 April 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 166.1699981689453}, {\"Date\": \"04 April 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 165.6300048828125}, {\"Date\": \"05 April 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 163.75999450683594}, {\"Date\": \"06 April 2023\", \"Name\": \"apple\", \"Stock Prices(USD)\": 164.66000366210938}, {\"Date\": \"10 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 90.7300033569336}, {\"Date\": \"13 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 92.43000030517578}, {\"Date\": \"14 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 94.87999725341797}, {\"Date\": \"15 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 96.19999694824219}, {\"Date\": \"16 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 100.04000091552734}, {\"Date\": \"17 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 98.94999694824219}, {\"Date\": \"20 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 97.70999908447266}, {\"Date\": \"21 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 100.61000061035156}, {\"Date\": \"22 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 98.69999694824219}, {\"Date\": \"23 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 98.70999908447266}, {\"Date\": \"24 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 98.12999725341797}, {\"Date\": \"27 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 98.04000091552734}, {\"Date\": \"28 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 97.23999786376953}, {\"Date\": \"29 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 100.25}, {\"Date\": \"30 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 102.0}, {\"Date\": \"31 March 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 103.29000091552734}, {\"Date\": \"03 April 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 102.41000366210938}, {\"Date\": \"04 April 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 103.94999694824219}, {\"Date\": \"05 April 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 101.0999984741211}, {\"Date\": \"06 April 2023\", \"Name\": \"amazon\", \"Stock Prices(USD)\": 102.05999755859375}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8c954c4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
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