HM_cmc_hover.ipynb

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   "cell_type": "code",
   "execution_count": 67,
   "id": "ef551324-b5d3-4f2f-9d1d-2a5f0af93c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "from datetime import datetime\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.chrome.service import Service\n",
    "from selenium.webdriver.common.by import By\n",
    "#from webdriver_manager.chrome import ChromeDriverManager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "4257402e-41d8-4ff3-8d5e-c31fda2d213a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to fetch data and generate treemap\n",
    "def fetch_and_plot():\n",
    "    # Declare browser\n",
    "    chrome_driver_path = '/home/user/Downloads/chromedriver-linux64/chromedriver'\n",
    "    #change \"user\" to your pc user\n",
    "    service = Service(chrome_driver_path)# Create a Service object\n",
    "    driver = webdriver.Chrome(service=service) # Pass the Service object to the webdriver.Chrome\n",
    "\n",
    "    #define url of page to exract data from\n",
    "    url='https://coinmarketcap.com/gainers-losers/'\n",
    "    driver.get(url)\n",
    "    time.sleep(5) #Sleep for few seconds so, by that time, the webpage gets loaded.\n",
    "    ranking =  driver.find_elements(By.XPATH, '//*[@id=\"__next\"]/div[2]/div/div[2]/div/div[2]/div/div[1]/div/table')# get element by XPATH from element selection in inspect mode\n",
    "\n",
    "    \"\"\" rec =  [s.text for s in ranking] + rec #this will return ['#\\nName\\nPrice\\n24h\\nVolume(24h)\n",
    "    print(rec) \"\"\"\n",
    "\n",
    "    data = []  # Extract data\n",
    "    for index, rank in enumerate(ranking):\n",
    "        rowData = rank.text.split('\\n')\n",
    "        #print(rowData) #This prints the extracted data all in a single row\n",
    "        data.append(rowData)\n",
    "    '''print(rowData)'''\n",
    "\n",
    "    #Resahaping the data list to # cols, should be indented or not?\n",
    "    lst1 = rowData[5:] #popping headers\n",
    "    expectedRows = len(lst1) // 4 #divide the list by no of expected cols to get no of expected rows\n",
    "    lst = np.array(lst1)#turning the simngle rowdata list into an array\n",
    "    reshpd = lst.reshape(int(expectedRows), 4) #to get row x col\n",
    "    '''print(reshpd)'''\n",
    "    reshpdtrimmed = reshpd[:, :-1]#'Price' '24h%' 'Vol(24h)' were merged together so I popped them\n",
    "    forth = np.array([row[3].split() for row in reshpd])#now splitting 'Price' '24h%' 'Vol(24h)' on their own\n",
    "    '''print(reshpdtrimmed)'''\n",
    "    merged = np.concatenate((reshpdtrimmed, forth), axis = 1)#coming together making the perfect array\n",
    "    '''print(merged)'''\n",
    "    first10 = np.array(merged[:10])#only need the first 10\n",
    "    '''print(first10)'''\n",
    "\n",
    "     #passing to nympy Dataframe to get tabular form\n",
    "    df = pd.DataFrame(first10, columns=['CmcRank', 'Name', 'Symbol', 'Price', '24h%', 'Vol(24h)'])\n",
    "    #Initially getting errors of ValueError: 5 columns passed, passed data had 125 columns\n",
    "    #so needed to break the  cols down into 5\n",
    "    #print(df)\n",
    "    timestamp = datetime.utcnow().strftime('%b %d, %Y %H:%M:%S UTC')\n",
    "    title = f\"Top 10 CMC 24h% Increase for {timestamp}\"\n",
    "    display_title = f\"**{title}**\"\n",
    "    # Display the title and DataFrame as a Markdown\n",
    "    from IPython.display import display, Markdown\n",
    "    display(Markdown(display_title))\n",
    "    display(df)\n",
    "\n",
    "    df['24h%'] = df['24h%'].str.rstrip('%').astype(float)\n",
    "\n",
    "     # Normalize the colors\n",
    "    fig = px.treemap(\n",
    "        df,\n",
    "        path=['Name'],\n",
    "        values='24h%',\n",
    "        color='24h%',\n",
    "        hover_data={'Symbol': True, 'Price': True, 'Vol(24h)': True, '24h%': True},\n",
    "        color_continuous_scale='Greens',\n",
    "    )\n",
    "\n",
    "    timestamp = datetime.utcnow().strftime('%b %d, %Y %H:%M:%S UTC')#timestamp for the plot\n",
    "\n",
    "        # Customize labels and hover info\n",
    "    fig.update_traces(\n",
    "        textinfo='label+text',\n",
    "        hovertemplate='<b>%{label}</b><br>Symbol: %{customdata[0]}<br>Price: %{customdata[1]}<br>Volume(24h): %{customdata[2]}<br>24h%: %{value}%'\n",
    "    )\n",
    "\n",
    "    title= f'Top10 CMC 24h% Increase for {timestamp}'\n",
    "    display_title = f\"**{title}**\"\n",
    "    from IPython.display import display, Markdown\n",
    "    display(Markdown(display_title))\n",
    "    #fig.update_layout(title=title, margin=dict(t=50, l=25, r=25, b=25))\n",
    "    fig.show()\n",
    "\n",
    "    #driver.quit"
   ]
  },
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   "cell_type": "code",
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   "id": "3eb7690c-f3bb-493d-823f-e2050f3d6318",
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    {
     "data": {
      "text/markdown": [
       "**Top 10 CMC 24h% Increase for Jul 01, 2024 13:45:38 UTC**"
      ],
      "text/plain": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CmcRank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Price</th>\n",
       "      <th>24h%</th>\n",
       "      <th>Vol(24h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>Ethereum Name Service</td>\n",
       "      <td>ENS</td>\n",
       "      <td>$31.46</td>\n",
       "      <td>22.04%</td>\n",
       "      <td>$324,517,915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>dogwifhat</td>\n",
       "      <td>WIF</td>\n",
       "      <td>$2.25</td>\n",
       "      <td>11.77%</td>\n",
       "      <td>$446,712,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>MultiversX</td>\n",
       "      <td>EGLD</td>\n",
       "      <td>$31.21</td>\n",
       "      <td>9.12%</td>\n",
       "      <td>$35,184,470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>Mog Coin</td>\n",
       "      <td>MOG</td>\n",
       "      <td>$0.000001887</td>\n",
       "      <td>7.84%</td>\n",
       "      <td>$44,370,411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>Beam</td>\n",
       "      <td>BEAM</td>\n",
       "      <td>$0.01703</td>\n",
       "      <td>7.81%</td>\n",
       "      <td>$12,608,286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54</td>\n",
       "      <td>Bonk</td>\n",
       "      <td>BONK</td>\n",
       "      <td>$0.00002371</td>\n",
       "      <td>7.63%</td>\n",
       "      <td>$200,854,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48</td>\n",
       "      <td>Bittensor</td>\n",
       "      <td>TAO</td>\n",
       "      <td>$277.86</td>\n",
       "      <td>7.62%</td>\n",
       "      <td>$38,464,776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>Immutable</td>\n",
       "      <td>IMX</td>\n",
       "      <td>$1.58</td>\n",
       "      <td>7.42%</td>\n",
       "      <td>$61,520,993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>56</td>\n",
       "      <td>Brett (Based)</td>\n",
       "      <td>BRETT</td>\n",
       "      <td>$0.1607</td>\n",
       "      <td>6.35%</td>\n",
       "      <td>$48,797,288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>39</td>\n",
       "      <td>Maker</td>\n",
       "      <td>MKR</td>\n",
       "      <td>$2,606.89</td>\n",
       "      <td>6.11%</td>\n",
       "      <td>$74,191,198</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "  CmcRank                   Name Symbol         Price    24h%      Vol(24h)\n",
       "0      70  Ethereum Name Service    ENS        $31.46  22.04%  $324,517,915\n",
       "1      41              dogwifhat    WIF         $2.25  11.77%  $446,712,630\n",
       "2      80             MultiversX   EGLD        $31.21   9.12%   $35,184,470\n",
       "3      98               Mog Coin    MOG  $0.000001887   7.84%   $44,370,411\n",
       "4      82                   Beam   BEAM      $0.01703   7.81%   $12,608,286\n",
       "5      54                   Bonk   BONK   $0.00002371   7.63%  $200,854,315\n",
       "6      48              Bittensor    TAO       $277.86   7.62%   $38,464,776\n",
       "7      40              Immutable    IMX         $1.58   7.42%   $61,520,993\n",
       "8      56          Brett (Based)  BRETT       $0.1607   6.35%   $48,797,288\n",
       "9      39                  Maker    MKR     $2,606.89   6.11%   $74,191,198"
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       "**Top10 CMC 24h% Increase for Jul 01, 2024 13:45:38 UTC**"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CmcRank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Price</th>\n",
       "      <th>24h%</th>\n",
       "      <th>Vol(24h)</th>\n",
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       "      <th>0</th>\n",
       "      <td>70</td>\n",
       "      <td>Ethereum Name Service</td>\n",
       "      <td>ENS</td>\n",
       "      <td>$31.46</td>\n",
       "      <td>22.04%</td>\n",
       "      <td>$324,517,915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>dogwifhat</td>\n",
       "      <td>WIF</td>\n",
       "      <td>$2.25</td>\n",
       "      <td>11.77%</td>\n",
       "      <td>$446,712,630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80</td>\n",
       "      <td>MultiversX</td>\n",
       "      <td>EGLD</td>\n",
       "      <td>$31.21</td>\n",
       "      <td>9.12%</td>\n",
       "      <td>$35,184,470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>98</td>\n",
       "      <td>Mog Coin</td>\n",
       "      <td>MOG</td>\n",
       "      <td>$0.000001887</td>\n",
       "      <td>7.84%</td>\n",
       "      <td>$44,370,411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>82</td>\n",
       "      <td>Beam</td>\n",
       "      <td>BEAM</td>\n",
       "      <td>$0.01703</td>\n",
       "      <td>7.81%</td>\n",
       "      <td>$12,608,286</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>54</td>\n",
       "      <td>Bonk</td>\n",
       "      <td>BONK</td>\n",
       "      <td>$0.00002371</td>\n",
       "      <td>7.63%</td>\n",
       "      <td>$200,854,315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>48</td>\n",
       "      <td>Bittensor</td>\n",
       "      <td>TAO</td>\n",
       "      <td>$277.86</td>\n",
       "      <td>7.62%</td>\n",
       "      <td>$38,464,776</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40</td>\n",
       "      <td>Immutable</td>\n",
       "      <td>IMX</td>\n",
       "      <td>$1.58</td>\n",
       "      <td>7.42%</td>\n",
       "      <td>$61,520,993</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>56</td>\n",
       "      <td>Brett (Based)</td>\n",
       "      <td>BRETT</td>\n",
       "      <td>$0.1607</td>\n",
       "      <td>6.35%</td>\n",
       "      <td>$48,797,288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>39</td>\n",
       "      <td>Maker</td>\n",
       "      <td>MKR</td>\n",
       "      <td>$2,606.89</td>\n",
       "      <td>6.11%</td>\n",
       "      <td>$74,191,198</td>\n",
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       "  CmcRank                   Name Symbol         Price    24h%      Vol(24h)\n",
       "0      70  Ethereum Name Service    ENS        $31.46  22.04%  $324,517,915\n",
       "1      41              dogwifhat    WIF         $2.25  11.77%  $446,712,630\n",
       "2      80             MultiversX   EGLD        $31.21   9.12%   $35,184,470\n",
       "3      98               Mog Coin    MOG  $0.000001887   7.84%   $44,370,411\n",
       "4      82                   Beam   BEAM      $0.01703   7.81%   $12,608,286\n",
       "5      54                   Bonk   BONK   $0.00002371   7.63%  $200,854,315\n",
       "6      48              Bittensor    TAO       $277.86   7.62%   $38,464,776\n",
       "7      40              Immutable    IMX         $1.58   7.42%   $61,520,993\n",
       "8      56          Brett (Based)  BRETT       $0.1607   6.35%   $48,797,288\n",
       "9      39                  Maker    MKR     $2,606.89   6.11%   $74,191,198"
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mbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"coloraxis\":{\"colorbar\":{\"title\":{\"text\":\"24h%\"}},\"colorscale\":[[0.0,\"rgb(247,252,245)\"],[0.125,\"rgb(229,245,224)\"],[0.25,\"rgb(199,233,192)\"],[0.375,\"rgb(161,217,155)\"],[0.5,\"rgb(116,196,118)\"],[0.625,\"rgb(65,171,93)\"],[0.75,\"rgb(35,139,69)\"],[0.875,\"rgb(0,109,44)\"],[1.0,\"rgb(0,68,27)\"]]},\"legend\":{\"tracegroupgap\":0},\"margin\":{\"t\":60}},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('69c1ee2d-99ba-4a42-976c-fa78d3051ba3');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "NoSuchWindowException",
     "evalue": "Message: no such window: target window already closed\nfrom unknown error: web view not found\n  (Session info: chrome=126.0.6478.114)\nStacktrace:\n#0 0x562d7093b69a <unknown>\n#1 0x562d7061e0dc <unknown>\n#2 0x562d705f3384 <unknown>\n#3 0x562d70695d3d <unknown>\n#4 0x562d706abf66 <unknown>\n#5 0x562d7068d613 <unknown>\n#6 0x562d7065d4f7 <unknown>\n#7 0x562d7065de4e <unknown>\n#8 0x562d7090186b <unknown>\n#9 0x562d70905911 <unknown>\n#10 0x562d708ed35e <unknown>\n#11 0x562d70906472 <unknown>\n#12 0x562d708d1cbf <unknown>\n#13 0x562d7092b098 <unknown>\n#14 0x562d7092b270 <unknown>\n#15 0x562d7093a7cc <unknown>\n#16 0x7fac3f8b8ea7 start_thread\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNoSuchWindowException\u001b[0m                     Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[71], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m----> 3\u001b[0m         fetch_and_plot()\n\u001b[1;32m      4\u001b[0m         time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m15\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m:\n",
      "Cell \u001b[0;32mIn[69], line 12\u001b[0m, in \u001b[0;36mfetch_and_plot\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m driver\u001b[38;5;241m.\u001b[39mget(url)\n\u001b[1;32m     11\u001b[0m time\u001b[38;5;241m.\u001b[39msleep(\u001b[38;5;241m5\u001b[39m) \u001b[38;5;66;03m#Sleep for few seconds so, by that time, the webpage gets loaded.\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m ranking \u001b[38;5;241m=\u001b[39m  driver\u001b[38;5;241m.\u001b[39mfind_elements(By\u001b[38;5;241m.\u001b[39mXPATH, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m//*[@id=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__next\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]/div[2]/div/div[2]/div/div[2]/div/div[1]/div/table\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;66;03m# get element by XPATH from element selection in inspect mode\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" rec =  [s.text for s in ranking] + rec #this will return ['#\\nName\\nPrice\\n24h\\nVolume(24h)\u001b[39;00m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;124;03mprint(rec) \"\"\"\u001b[39;00m\n\u001b[1;32m     17\u001b[0m data \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# Extract data\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/selenium/webdriver/remote/webdriver.py:888\u001b[0m, in \u001b[0;36mWebDriver.find_elements\u001b[0;34m(self, by, value)\u001b[0m\n\u001b[1;32m    884\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m[name=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m value\n\u001b[1;32m    886\u001b[0m \u001b[38;5;66;03m# Return empty list if driver returns null\u001b[39;00m\n\u001b[1;32m    887\u001b[0m \u001b[38;5;66;03m# See https://github.com/SeleniumHQ/selenium/issues/4555\u001b[39;00m\n\u001b[0;32m--> 888\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecute(Command\u001b[38;5;241m.\u001b[39mFIND_ELEMENTS, {\n\u001b[1;32m    889\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124musing\u001b[39m\u001b[38;5;124m'\u001b[39m: by,\n\u001b[1;32m    890\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m: value})[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m []\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/selenium/webdriver/remote/webdriver.py:428\u001b[0m, in \u001b[0;36mWebDriver.execute\u001b[0;34m(self, driver_command, params)\u001b[0m\n\u001b[1;32m    426\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcommand_executor\u001b[38;5;241m.\u001b[39mexecute(driver_command, params)\n\u001b[1;32m    427\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response:\n\u001b[0;32m--> 428\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39merror_handler\u001b[38;5;241m.\u001b[39mcheck_response(response)\n\u001b[1;32m    429\u001b[0m     response[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_unwrap_value(\n\u001b[1;32m    430\u001b[0m         response\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvalue\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m))\n\u001b[1;32m    431\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m response\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/selenium/webdriver/remote/errorhandler.py:243\u001b[0m, in \u001b[0;36mErrorHandler.check_response\u001b[0;34m(self, response)\u001b[0m\n\u001b[1;32m    241\u001b[0m         alert_text \u001b[38;5;241m=\u001b[39m value[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124malert\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m exception_class(message, screen, stacktrace, alert_text)  \u001b[38;5;66;03m# type: ignore[call-arg]  # mypy is not smart enough here\u001b[39;00m\n\u001b[0;32m--> 243\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception_class(message, screen, stacktrace)\n",
      "\u001b[0;31mNoSuchWindowException\u001b[0m: Message: no such window: target window already closed\nfrom unknown error: web view not found\n  (Session info: chrome=126.0.6478.114)\nStacktrace:\n#0 0x562d7093b69a <unknown>\n#1 0x562d7061e0dc <unknown>\n#2 0x562d705f3384 <unknown>\n#3 0x562d70695d3d <unknown>\n#4 0x562d706abf66 <unknown>\n#5 0x562d7068d613 <unknown>\n#6 0x562d7065d4f7 <unknown>\n#7 0x562d7065de4e <unknown>\n#8 0x562d7090186b <unknown>\n#9 0x562d70905911 <unknown>\n#10 0x562d708ed35e <unknown>\n#11 0x562d70906472 <unknown>\n#12 0x562d708d1cbf <unknown>\n#13 0x562d7092b098 <unknown>\n#14 0x562d7092b270 <unknown>\n#15 0x562d7093a7cc <unknown>\n#16 0x7fac3f8b8ea7 start_thread\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    while True:\n",
    "        fetch_and_plot()\n",
    "        time.sleep(15)\n",
    "except KeyboardInterrupt:\n",
    "    print(\"Stopped by user\")"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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